In recent years, with the increasing impact of climate change, achieving the "dual carbon" goal has become increasingly urgent. Fossil fuel consumption is one of the main sources of carbon emissions and is closely related to economic development. Therefore, it is necessary to decouple economic growth from fossil fuel consumption as soon as possible. However, the small sample defect in fossil energy consumption panel data has caused great difficulties for machine learning algorithms. To address this defect, this study uses TimeGAN for data augmentation and compares the performance of six regression analysis methods (XGBoost, CatBoost, LGBM, KNN, linear regression, decision tree) in predicting fossil energy consumption. In addition, feature importance and SHAP are used simultaneously to explore the main driving factors of fossil energy consumption. The research results show that the decoupling index of most provinces fluctuated relatively little from 2011 to 2018. Compared to other machine learning algorithms, XGBoost and CatBoost perform better in predicting fossil energy consumption. Finally, based on two interpretable analysis methods, we found that population has the most significant impact on fossil energy consumption, followed by GDP, and urbanization rate is the least important. The research results provide important information for formulating energy sustainable development strategies and further discuss energy-saving solutions based on data methods.

This paper focuses on examining a new type of n-additive maps called the symmetric reverse n-derivations. As implied by its name, it combines the ideas of n-additive maps and reverse derivations, with a 1-reverse derivation being the ordinary reverse derivation. We explore several findings that expand our knowledge of these maps, particularly their presence in semiprime rings and the way rings respond to specific functional identities involving elements of ideals. Also, we provide examples to help clarify the concept of symmetric reverse n-derivations. This study aims to deepen the understanding of these symmetric maps and their properties within mathematical structures.

Member states of regional intergovernmental organisations exert institutional discursive power by influencing regional narratives and policy frameworks. This influence operates through formal communications that validate specific viewpoints. The effectiveness of such discursive practices is predicated on the organisation's credibility, structure, and regional geopolitics. The interaction between institutional rhetoric and national interests often yields nuanced discursive outcomes, reflecting a synthesis of supranational and state-level priorities. At the centre of interstate dynamics is the pursuit of institutional discursive power, which has become a crucial avenue for states to participate in the creation of international governance guidelines. We consider the institutional discursive power of nations within Regional Fisheries Management Organisations (RFMOs) to see how it impacts the establishment of global fisheries governance systems and order. As intergovernmental organisations, RFMOs have significant influence on global fisheries governance. In order to evaluate and compare the institutional discursive power of different countries within RFMO, this study has developed an evaluation framework and model that includes competitiveness, constructiveness, and influence as its three main dimensions. It does this by drawing on a thorough understanding of the institutional discursive power within RFMO and its formation mechanisms, as well as by integrating theoretical principles from evaluation science.

The tactile Internet (TI) characterises the transformative paradigm that aims to support real-time control and haptic communication between humans and machines, heavily relying on a dense network of sensors and actuators. Non-orthogonal multiple access (NOMA) is a promising enabler of TI that enhances interactions between sensors and actuators, which are collectively considered as users, thus supports multiple users simultaneously in sharing the same resource block (RB), consequently offering remarkable improvements in spectral efficiency and latency. This article proposes a novel downlink power domain NOMA (PD-NOMA) communication scenario for TI by considering multiple users and a base station. The signal-to-interference noise ratio (SINR), sum rate, and fair power allocation (PA) coefficients are mathematically derived in the NOMA system model. The simulations are performed with two-user and three-user scenarios to evaluate the system performance in terms of bit error rate (BER), sum rate and latency between NOMA and traditional orthogonal multiple access (OMA) schemes. Moreover, outage probability is analysed with varying fixed power allocation (PA) coefficients in the NOMA scheme. Finally, we present the outage probability, sum rate and latency analyses for fixed and derived fair PA coefficients, thus promoting dynamic PA and user fairness by efficiently utilising the available spectrum.

Telemedicine has emerged as a transformative tool in modern healthcare, particularly during the COVID-19 pandemic, which catalyzed its rapid expansion. Despite its demonstrated potential in enhancing remote patient monitoring, AI-driven diagnostics, and telepsychiatry, widespread adoption of telemedicine remains constrained by persistent barriers. This review examines the current state of telemedicine, identifying major advancements alongside key challenges that impede its full integration into global healthcare systems. These barriers include the digital divide, regulatory inconsistencies across regions, and patient concerns about data security and the quality of virtual care. Addressing these issues requires a coordinated multi-sectoral approach, emphasizing infrastructure development, regulatory harmonization, patient education, and the incorporation of emerging technologies such as AI, blockchain, and virtual reality. By overcoming these challenges, telemedicine can be established as a cornerstone of equitable, accessible, and patient-centered healthcare worldwide.

Common hops (Humulus lupulus L.) are essential in brewing, contributing to the bitterness, flavor, and aroma of beer, and are widely used in supplements for their antibacterial, anti-inflammatory, and antioxidant properties. However, despite their broad applications, the allergenic potential of common hops remains underexplored, particularly in comparison to the closely related Humulus japonicus. This study aimed to investigate the allergenic potential of common hops. Immunoreactivity of hop stalks, leaves, and cones with antibodies recognizing major allergens of birch (Bet v1a), mugwort (Art v1), and timothy (Phl p5b), as well as three sera from pollen-allergic patients, was assessed using slot blot and Western blotting techniques. Phosphate-buffered saline extracts of hop stalks, leaves, and cones from three cultivars were analyzed via slot blot while Western blotting was used after SDS-PAGE protein separation. Results revealed strong immunoreactivity in native hop proteins, although fewer protein fractions were identified in denatured samples. Nevertheless, the cross reactivity of hop proteins with pollen allergens was confirmed, suggesting that hop proteins may contribute to sensitization in pollen-sensitive individuals. These finding focus the potential allergenic risk of common hops.

Agrocybe cylindracea is an important fungi mushroom, highly valued as a functional food for its nutritional and me-dicinal benefits. Many bioactive extracts from A. cylindracea have been found to exhibit antitumor and antioxidant activities. This research investigated the distinct substrates that affected the physicochemical properties and yield of A. cylindracea. The substrates used were 48% Giant Juncao Grass and 30% Dicraptoris Dicholoma grasses, 48% Wasted Tea Leaves and 30% Sawdust, and 78% Cottonseed Hulls, all supplemented with 20% Wheat Bran and 2% Lime. The findings indicated the cotton seed husk marked a significant in Agrocybe cylindricae yield, followed by the wasted tea leave+sawdust. Ash content peaked in the fruiting bodies grown on Giant Juncao Grass and Dicraptoris Dicholoma, while fat content was highest in those grown on Giant Juncao Grass and Dicraptoris Dicholoma, fol-lowed by Wasted Tea Leaves and Sawdust. Protein content was significantly higher in fruiting bodies cultivated on Wasted Tea Leaves and Sawdust, followed by Cottonseed Hulls. Carbon dioxide emissions varied across substrates, with the highest emissions observed during the maturity stage of the fruiting bodies grown on Giant Juncao Grass and Dicraptoris Dicholoma, and Wasted Tea Leaves and Sawdust. Emissions decreased sharply 110 days after cultiva-tion. Essential metabolites such as dopamine and caffeine were enriched in fruiting bodies grown on Wasted Tea Leaves, Sawdust, and Cottonseed Hulls. In contrast, tyramine and uracil were enriched in those grown on Cotton-seed Hulls.

Red blood cell (RBC) alloimmunization and antibodies formation against non-self-antigen on red cells may occur after blood transfusion, pregnancies or other exposures. The RBC alloimmunization rates varies from 2% to 6% according to recent studies. The antibody screen is performed to identify or confirm the presence of antibodies in patient’s serum or plasma, as a preoperative or pretransfusion tests. The antibody identification process and major crossmatch are provided to improve the safety of red blood transfusion administration. I report three examples associated with clinically significant single and multiple alloantibodies. Ultimately, I summarize key points for a clinical transfusion practice.

Mobile Edge Computing (MEC) is a growing concept that expands on cloud computing technology. It leverages edge infrastructure to efficiently manage computationally intensive and time-sensitive tasks. By tapping into the capabilities of 7G network infrastructure, MEC can effectively reduce latency by transferring computing tasks from edge devices to edge servers. As part of Industrial 4.0 revolution, the use of large number of IOT sensors, edge devices, and edge servers are increasing at rapid phase. To effectively process these sensor and device data with less power and low latency is a key challenge. Here comes the need for an effective task offloading strategy to edge servers. This strategy must meet the industrial automation requirements of reduced energy usage and faster processing. A solution based on an Improved Particle Swarm Optimization with Genetic Algorithm (IPSOGA) is designed. It harnesses the power of effective exploration using Particle Swarm Optimization (PSO) and maintain genetic diversity within the population using Genetic Algorithm (GA). This hybrid approach is powerful in solving complex optimization problems with the aims to effective resource allocation, faster task offloading decisions, and consequently reduce processing delays. The proposed IPSOGA is compared with popular metaheuristic algorithms like Genetic Algorithm (GA) and Simulated Annealing (SA), and it has been proven to be superior in task offloading strategy by effectively reducing processing delays, energy consumption, and optimizing resource allocation efficiently.

Hymenoptera, which includes honeybees, wasps, bumblebees, and hornets, is an order of the class Insecta whose venom can induce anaphylactic reactions in dogs. While several studies have investigated the natural histories and risk factors of Hymenoptera venom allergy (HVA) in humans, only limited information is available on canine patients. Therefore, the aim of this study was to identify risk factors leading to severe systemic reactions (SSR) and to explore the natural history of these patients. This was achieved with an inquiry into the case histories of 178 dogs that were stung by Hymenoptera between 2018 and 2022. Dogs under two years old, dogs that weighed under 10kg, purebred dogs and dogs that were stung in the oral cavity were at a greater risk of developing SSR. Almost two thirds of patients with SSR experienced the same or worse symptoms after subsequent stings and > 40% of patients with local reactions developed SSR when stung again. Next to providing valuable clinical information of HVA in dogs, these findings strongly support the recommendation of venom immunotherapy (VIT) for patients with HVA.

Background and Objectives: Multiple primary malignant tumors represent a small percentage of the total number of oncological cases and can involve either metachronous or synchronous development and represent challenges in diagnosis, staging, and treatment planning. Our purpose is to present a rare case of bladder adenocarcinoma in a female patient with multiple primary malignant tumors and to provide systematic review of the available literature. Materials and Methods:A 67-year-old female patient who was admitted altered general condition, and anuria. The past medical history of the patient included malignant melanoma (2014), cervical cancer (2017), colon cancer (2021), obstructive anuria (2023), liver metastasectomy (2023). Transurethral resection of bladder tumor was performed for bladder tumors. Results: Contrast CT highlighted multiple pulmonary metastases, a poly nodular liver con-glomerate, retroperitoneal lymph node, II/III grade left ureterohydronephrosis, and no digestive tract tumor masses. The pathological result of the bladder resection showed an infiltrative adenocarcinoma. Conclusions: The difference between primary bladder adenocarcinoma tumor and metastatic colorectal adenocarcinoma is the key for the future therapeutic strategy. Identification and assessment of risk factors such as viral infection, radiotherapy, chemotherapy, smoking, genetics are pivotal in understanding and managing multiple primary malignant tumors. Personalized prevention strategies, screening programs may facilitate the early detection of this tumors, synchronous or metachronous one. The use of multicancer early detection (MCED) blood tests for early diagnosis appears promising. However, additional research is needed to standardize these techniques for cancer detection.

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive Fractional Generalized Pareto Motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results demonstrate that the adaptive fGPm model significantly reduces wind power forecasting errors, offering high accuracy and robustness. This method provides practical value in alleviating peak voltage and frequency regulation challenges in power systems and in promoting the integration of wind power into the grid.

Most of our bone are formed thought a process called endochondral ossification, which is a tiny, regulated process leaded by the chondrocytes of the GP and which require continuous cross talk between these cells and chondrocytes and invading cell populations, including osteoblasts, chondroclasts and vascular cells. The better understanding of these signaling pathways is not only important in the normal growth and maturation of the skeleton but might be highly relevant to pathophysiological processes in bones and joints. Since there is poor information about communication between chondrocytes and other cell types in the developing bones, this review examines the current knowledge of how interactions between chondrocytes and bone forming cells modulate bone growth.

Cancer remains one of the most critical global health issues. Conventional treatments, such as radiotherapy, surgery, or chemotherapy, have limitations, especially concerning side effects, resistance, and recurrence. Consequently, new innovative treatments to overcome these problems are needed. Photothermal therapy (PTT) is a promising alternative that uses photothermal agents that convert near-infrared light (NIR) into heat to kill cancer cells. Nanoparticles can be used as photothermal agents and also as drug delivery platforms, improving the drugs’ stability, allowing for targeted delivery, and reducing toxicity. Due to its broad absorption band, high surface area, and versatility for surface functionalization, tungsten disulfide (WS2) has high potential in this context. This paper presents the state-of-the-art on the use of WS2-based materials to achieve effective and biocompatible anti-anticancer new treatment strategies.

Background: To evaluate the incidence of postsurgical cystoid macular edema (PCME) after small-gauge pars plana vitrectomy (PPV) for different retinal pathologies and to assess the role of optical coherence tomography (OCT) biomarkers to guide treatment decision in patients with PCME, refractory to medical therapy. Materials and Methods: Medical records of consecutive pseudophakic patients, who underwent PPV for different retinal pathologies, were retrospectively evaluated in this single center, uncontrolled study. The incidence of post-PPV CME was assessed in this population. Eyes that presented with post-PPV CME within the first 2 months after surgery were considered for further evaluation. A minimum follow-up of 12 months, history of previous cataract surgery performed not earlier than 1 year before PPV, the availability of full clinical and OCT data at 1, 3, 6, 9 and 12 months after PPV, constituted the minimal requirements to be included in the 12-months assessment. Mean best corrected visual acuity (BCVA; LogMAR) together with mean central macula thickness (CMT; m) change, and response to different treatments (medical therapy and dexamethasone intravitreal implant) were evaluated. The impact of specific clinical and structural OCT biomarkers on the exposure to dexamethasone (DEX) intravitreal treatment was assessed. Eventual ocular and non-ocular adverse events, potentially related to the treatment, were investigated as-well. Results: Of 346 pseudophakic patients (352 eyes) enrolled in the study, 54 (54 eyes) developed CME within the first 2 months after PPV (incidence of 15.3%). After the exclusion of 6 subjects, the remaining 48 pa-tients were dimed eligible for the 12-months analysis. Preoperative mean BCVA (1.44 ± 0.99 LogMAR) sig-nificantly improved to 0.32 ± 0.37 LogMAR at 12 months (P< 0.001). Mean baseline CMT of 347 (± 123.5) m, significantly decreased to 290 m (± 80.4; P=0.003) by the end of the FU. Twenty-five eyes (52%) re-quired one or more DEX intravitreal implants for CME, refractory to topical therapy. The correlation analy-sis disclosed the existence of significant correlations between mean CMT values at different time points of the FU. Additionally, patients who required intravitreal therapy at months 3 and 9, were more likely to present with the following features at 1 month OCT: intraretinal fluid (IRF), disorganization of inner retinal layers (DRIL), disorganization of outer retinal layers (DROL) and hyperreflective foci (HRF). No major safe-ty issues arose. Conclusion: Topical therapy alone can be a valuable option for post-PPV CME in approximatively 50% of pa-tients. Significant visual acuity recovery and CMT reduction at 12 months, demonstrated DEX intravitreal implant can be a safe and effective second-line treatment for pseudophakic patients with post-PPV CME, refractory to medical therapy. The presence of specific OCT biomarkers may indicate a more severe CME that might benefit from the intravitreal therapy.

FDG PET has been widely used in oncology studies as well as for cardiovascular diseases. This technique has recently been applied to detect active cardiovascular inflammation, mainly owing to enhanced glucose utilization by the activation of granulocytes and macrophages. To identify active inflammation by FDG PET, physiological uptake in the normal myocardium should be suppressed by appropriate patient preparation. FDG PET has been used to detect active lesions and assess the response to anti-inflammatory therapy in patients with cardiac sarcoidosis. In addition, other active cardiovascular inflammations, such as endocarditis, aortitis, and unstable atherosclerosis may be identified by FDG PET. After cancer treatment, FDG PET is considered an elegant approach not only for the assessment of tumor response to cancer therapy, but also for early detection of possible cardiovascular involvement. This review summarizes the clinical values of FDG PET for the assessment of active cardiovascular inflammation under suitable patient preparation.

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to solve the issues given by manual identification of plant diseases, which is time-consuming and wasteful, as well as low detection accuracy. This paper proposes the development of a robust deep ensemble convolutional neural network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative study was then conducted on the improved dataset. The application of data augmentation, along with the addition of dense layers, batchnormalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 score for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, outperforming the individual performances of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p, q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

Plants are continuously exposed to environmental challenges, including pollutants, pesticides, and heavy metals, collectively termed xenobiotics. These substances induce oxidative stress by generating reactive oxygen species (ROS), which can damage cellular components such as lipids, proteins, and nucleic acids. To counteract this, plants have evolved complex metabolic pathways involving both enzymatic and non-enzymatic mechanisms to detoxify and process these harmful compounds. Oxidative stress in plants primarily arises from the overproduction of ROS, including hydrogen peroxide (H2O2), superoxide anions (O2•−), singlet oxygen (1O2), and hydroxyl radicals (•OH), by-products of metabolic activities such as photosynthesis and respiration. Exposure to xenobiotics significantly elevates ROS levels, causing cellular damage and disrupting metabolism. Plants employ a robust antioxidant defense system featuring enzymatic antioxidants like catalase (CAT), superoxide dismutase (SOD), and various peroxidases (POD). These enzymes collaborate to scavenge ROS, minimizing their detrimental effects. Additionally, plants produce numerous non-enzymatic antioxidants, such as flavonoids, phenolic acids, vitamins C and E, which neutralize ROS and regenerate other antioxidants, providing comprehensive protection against oxidative stress. Xenobiotic metabolism in plants involves three phases: Phase I (modification), Phase II (conjugation), and Phase III (compartmentalization), facilitated by enzymes like cytochrome P450 monooxygenases (CYPs), transferases such as glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters. Plant secondary metabolites, including alkaloids, terpenoids, and flavonoids, significantly contribute to detoxification and antioxidant defense. Understanding these oxidative processes and xenobiotic metabolism pathways in plants is crucial, as it offers insights into developing plant-based strategies to mitigate environmental pollution impacts and improve human health through enhanced detoxification and antioxidant mechanisms, as we discuss in this review.

Background: Statistics from the Ministry of Health and Welfare reported oral cancer as one of the most prevalent malignant cancers, with the third highest incidence and the fifth leading cause of death among men in Taiwan. Lymph node metastasis in oral cancer usually has a low survival rate, with no significant improvement in the past 30 years. Therefore, a more effective survival predictor is warranted. Many cancer studies revealed that monitoring tumor thickness and lymph node density, in addition to tumor, node, and metastasis (TNM) stages, can provide more accurate predictions.Methods: This retrospective study analyzed data from 612 patients with oral cancer who had the habit of chewing betel nuts. The study focused on tumor thickness, lymph node density, and the regional distribution of lymph node metastasis to determine their effectiveness as predictors.Results: The results revealed that a tumor thickness of 6 mm indicated cervical lymph node metastasis and was the optimal cutoff point for overall survival. The optimal cutoff value for lymph node density was 0.04. Patients with a tumor thickness of >6 mm and a lymph node density of >0.04 had significantly lower overall survival rates. Additionally, patients with >1 lymph node metastasis level and lower cervical metastasis exhibited a relatively worse prognosis.Conclusion: Therefore, in addition to TNM staging, tumor thickness, lymph node density and metastasis level are suitable as a parameter for predictors that can be used as references for adjuvant therapies for better therapeutic effects.

Landslides in the hilly regions of Al‐Madinah Al‐Munawarah are a natural hazard that endangers both infrastructure and human lives. The objective of this work is to create maps that show the likelihood of landslides occurring on the Al Figrah Road, which is a vital road connects the cities of Al‐Madinah and Yanbu. This will be done using the analytical hierarchy process (AHP) approach. The landslide susceptibility maps have been created by taking into account various parameters such as elevation, slope, aspect, drainage density, lithology, soil, and precipitation. Assess the significance of these thematic layers in relation to the frequency of landslides in the research area based on historical landslide data and field verification, in order to allocate suitable weights. The sensitivity maps of the study region delineated areas with low risk (93,552,333 m2), moderate risk (271,180,722 m2), and high risk (33,053,839 m2). 28.63% of the Al Figrah total Road is classified as low risk, 48.09% as moderate risk, and 23.28% as high risk. A total of 10 landslides were identified in areas with steep slopes and denudational hills, confirming the findings of the AHP analysis conducted in high‐risk zones. The integrity of these slide zones was greatly impacted by the presence of joints, faults, foliation, and shear zones within igneous and metamorphic rocks. Examining the susceptibility of landslides in this region enhances our understanding of landslides on other steep routes in Saudi Arabia.

Recently, the introduction of Environmental, Social, and Governance (ESG) scores has become crucial for investment decisions and in minimizing portfolio risk. This study aims to understand the relationship between ESG scores and Value-at-Risk (VaR), computed by using a Vine copula-GARCH based approach, chosen for its reliability in detecting interdependencies among multiple stocks. In fact, one of the main challenges in estimating VaR for a stock portfolio is capturing the dependence structure among a large number of assets. The dataset consists of 16 companies listed on the FTSE100 index. The corresponding ESG scores were collected over a comprehensive period of five years, from 2018 to 2022, covering both normal and stressed market conditions. Additionally, a focused analysis was conducted for the period from 2020 to 2022 to isolate the specific effects of the COVID crisis. The results indicate that an increase in assets with the highest ESG scores reduces potential losses in the portfolio. This finding underscores the importance of integrating high-level ESG scores into portfolios to mitigate market risk. Additionally, during periods characterized by stressed market conditions, the impact of ESG scores on VaR is even more pronounced, demonstrating that sustainable assets are more resilient in times of crisis.

In this study, a stepper driver, which is suitable for use in the automotive industry, designed for general use, capable of adaptive learning in the system in which it will be used, with CAN and Universal Asynchronous Receiver-Transmitter (UART) communication options, has been designed. This design is made with PIC18F25K22 Micro Controller Unit (MCU). Rotor speed, armature current, and terminal voltage can be measured on the proposed brushed DC motor drive system. It gives the desired response by evaluating the obstacle and high current situations. The speed measurement of the vehicle and the open and closed status of the automatic door can be monitored. Main contribution of the designed PCB is Adaptive Learning Algorithm (ALA) which uses pulses of the encoder and estimates position of the step and manage operation process to prevent mechanical damage at final point of the motor. On the other hand, unexpected hitting and pinching can be kept under control by watching current value. Beneficial side of this method is that life of the mechanical step increases due to management of the forward and backward operation of the step and prevents potential accidents.

Bacterial keratitis caused by Pseudomonas aeruginosa is indeed a serious concern due to its potential to cause blindness and its resistance to antibiotics, partly attributed to biofilm formation and cytotoxicity to the cornea. The present study uses meta-analysis of transcriptomics dataset to identify important genes and pathways in biofilm formation of P. aeruginosa induced keratitis. By combining data from several studies, meta-analysis can enhance statistical power and robustness, enabling the identification of 83 differentially expressed candidate genes including fis that could serve as therapeutic targets. The approach of combining meta-analysis with virtual screening and in vitro methods provides a comprehensive strategy for identifying potential target genes and pathway crucial for bacterial biofilm formation and development anti-biofilm medications against P. aeruginosa infections. The study identified 83 candidate genes that exhibited differential expression in the biofilm state, with fis proposed as an ideal target for therapy for P. aeruginosa biofilm formation. These techniques, meta-analysis, virtual screening, and in-vitro methods were used in combination to diagnostically identify these genes which play a significant role in biofilms. This finding has highlighted a hallmark target list for P. aeruginosa anti-biofilm potential treatments.

Oxyria (Polygonaceae) is a small genus only comprising two species, Oxyria digyna and O. sinensis. Both species are well-known Chinese herbal medicines with a long history of use. We sequenced and assembled the complete mitogenomes of these two species and conducted a comparative analysis of the mitogenomes within Polygonaceae. Both O. digyna and O. sinensis displayed distinctive multi-branched conformations, consisting of one linear and one circular molecule. These two species shared similar gene compositions and exhibited distinct codon preferences, with mononucleotides as the most abundant type of simple sequence repeats. In the mitogenome of O. sinensis, a pair of long forward repeat sequences can mediate the division of molecule 1 into two sub-genomic circular molecules. Homologous sequence analysis revealed the occurrence of gene transfer between the chloroplast and mitochondrial genomes within Oxyria species. Additionally, a substantial number of homologous collinear blocks with varied arrangements were observed across different Polygonaceae species. Phylogenetic analysis suggested that mitogenome genes can serve as reliable markers for constructing phylogenetic relationships within Polygonaceae. Comparative analysis of eight species revealed Polygonaceae mitogenomes exhibited variability in gene presence, and most protein-coding genes (PCGs) have undergone negative selection. Overall, our study provided a comprehensive overview of the structural, functional, and evolutionary characteristics of the Polygonaceae mitogenomes.

In the field of machine learning, the rapid development of data volume and variety requires ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) has emerged as a key solution that aims to ensure fairness and privacy protection in a distributed learning environment. FFL enhances privacy protection and solves the inherent limitations of existing federated learning (FL) by promoting fair model training in diverse participant groups, preventing the exclusion of individual users or minorities, and improving overall model fairness. In this study, FFL discusses the causes of bias and fairness of existing FL, and separates solutions based on data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies to overcome heterogeneity. In order to improve the causes of bias, fairness, and privacy protection of FL, fairness evaluation indicators and applications and challenges of FFL are discussed. Since it addresses bias, fairness, and privacy issues in FL of all mechanisms, it can be an important resource for practitioners who want to implement efficient FL solutions.

Reinforcement learning (RL) is one of the core frameworks in Artificial Intelligence (AI) used for decision-making tasks. In particular, positive reinforcement rewards desirable actions, which helps an AI agent optimize its policy by maximizing the cumulative reward over time. This paper critically reviews how positive reinforcement affects decision-making in AI models. We explore the mechanisms, limitations, and potential biases introduced by positive reinforcement in AI systems and provide insights into real-world applications and ethical considerations.

The freeze-drying of camel and mare milk presents challenges due to their unique compositions, such as high lactose and protein content. This research aims to optimize vacuum freeze-drying parameters, including temperature, drying time, and layer thickness, for enhanced product quality and reduced energy consumption. Ultrasound and homogenization techniques were applied to enhance moisture transfer during drying. Experimental data suggest optimal conditions of -50°C to +50°C over 22–24 hours, significantly improving reconstitution quality. Mathematical modeling in Python and Maple helped refine process parameters to achieve desirable product consistency.

The extract of the fruit chasteberry has phytotherapeutic effects, making it an important ally in women's well-being during premenstrual syndrome (PMS); however, it contains alcohol in the composition, has a bitter taste and spicy flavor, and can also cause a burning sensation. Encapsulation techniques can minimize these problems. In this context, the present study aimed to encapsulate the concentrated extract of chasteberry and develop dark chocolate with the potential for minimizing PMS symptoms by adding 2.5% free or microencapsulated chasteberry extract in the chocolate bar formulation. Microparticles loaded with chasteberry extract were produced by spray drying and covered by spray-chilling using Arabic gum and vegetable fat as the carriers, respectively. The particles were characterized by morphology, size, X-ray diffraction, TGA, and stability of total phenolics and casticin – the molecule responsible for phytotherapeutic effects-. The best particle was added to a dark chocolate mixture. The chocolate samples produced with free and encapsulated extracts were submitted to sensory trials. The two paired comparison tests proved that particles efficiently for masking the chasteberry extract burning sensation and bitterness in the chocolates. Besides, particles were also efficient in protecting casticin. In this context, the encapsulation using the combination of both techniques was efficient in promoting casticin and total phenolics stability and producing chocolate with good sensory aspects. Therefore, it was possible to develop a product that can be used in clinical trials to evaluate its potential to minimize PMS symptoms.

Background: The dynamic nature of today’s organizations, inclusive of educational institutions require employees to have self-leadership attributes. Several factors in an academic environment can affect the self-leadership necessary for the optimal performance of nurse educators. This study aims to explore nurse educators’ understanding of self-leadership and their perspectives on how this phenomenon can be promoted to create a healthy work environment in an educational setting. Methods: An exploratory, descriptive qualitative design was adopted. Data were collected through focus group interviews with nurse educators at four educational institutions in South Africa, then subjected to Tesch’s qualitative content analysis. Results: Two themes emerged, namely, Self-leadership means nurse educators being supported to take own initiatives, with its four sub-themes, and A supportive healthy academic work environment, with its four sub-themes. Conclusions: Aca-demic institutions who work collaboratively with nurse educators and support them in self-leadership practices contribute to the creation of healthy work environments.

The paper provides analysis that the generation of the gyroscopic torque described by the vector multiplication is one-way inertial phenomenon. Respectively, vector multiplication is one-way equation from the type: If B times C is equal A, A is not equal B times C. Presumably, it affects the Conservation Laws.

Expanded Polystyrene (EPS) beads lightweight soil is a new type of artificial geotechnical material with low density and high strength, to promote the wide application of EPS beads lightweight soil in the project, replacing some cement with fly ash as a curing agent, which can consume the waste fly ash and reduce the cost of the project. EPS beads were used as a lightweight material, and cement and fly ash were used as curing agents into the raw soil to make fly ash EPS lightweight soil. The EPS beads contents are 0.5%, 1%, 1.5%, and 2%, the total curing agent contents are 10%, 15%, 20%, and 25%, and the proportions of cement replaced by fly ash are 0%, 15%, 30%, 45%, and 60%, respectively. Unconfined compressive strength (UCS) test and scanning electron microscopy (SEM) test were conducted. The results show that the EPS content, the total curing agent content, and the proportion of cement replaced by fly ash have a significant impact on the UCS of lightweight soil. It decreases with an increase in the EPS content, increases with an increase in the total curing agent content, and decreases with an increase in the proportion of cement replaced by fly ash. When the proportion of cement replaced by fly ash is not too high, the strength of lightweight soil decreases less, and its performance can still meet the engineering needs. At the same time, it can also consume fly ash and reduce environmental pollution. EPS lightweight soil mixed with fly ash still has advantages, and it is recommended that the proportion of cement replaced by fly ash is controlled to less than 30%. The failure patterns of lightweight soil mainly include splitting failure, oblique shear failure and bulging failure, which are related to the material mix ratio.

In order to better understand the temporal and spatial variation of canopy rainfall interception with watershed, In this study, the remote sensing observation data at the regional scale were coupled with the vegetation canopy rainfall interception model constructed by A.P.J. DEROO for validation, and the spatial variation trend of rainfall interception was analyzed through linear and nonlinear trends, and the rainfall interception was quantified and the influencing factors were analyzed at different altitudes and regions at time scales. The results show that: (1) Compared with the PML_V2 and measured values, the root mean square errors of the simulated rainfall inter-ception values are 5.69 and 3.37, and the effective factor are 0.66 and 0.64, respectively, which confirms the reliability of the A.P.J.DE ROO model in estimating rainfall interception in Beijing. (2) From 2005 to 2020, the annual interception volume and interception rate showed an upward trend, with the largest increase trend in 2005 at 3.8%/a, and the interception volume reversed in 2015, changing from an increase (6.5 mm/a) to a decrease (-5.8 mm/a). (3) The Theil-Sen Median trend was used to test the spatial variation trend of rainfall interception in the canopy in Beijing, with an increasing trend of 89.5%, a decreasing trend of 8.7%, and a basically unchanged area of 1.8%. Through the BFAST01 mutation test, 4.4% of the canopy rainfall interception in the whole region had mutations, and the southern mutation detection type accounted for the largest proportion. (4) Leaf area index, rainfall, canopy height and slope had the strongest explanatory power for canopy interception in the monthly scale analysis, while wind speed and temperature were weaker. The results of this study provide new insights into the analysis of spatial and temporal changes in canopy interception in Beijing, which can help to accurately assess the impact of forest ecosystems on regional water cycles and provide scientific and practical insights for water resource man-agement.

Skin cancer is one of the most prevalent cancers worldwide, with an increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Melanoma, despite being much less common than basal or squamous cell carcinomas, is the most deadly, with nearly 8,300 Americans expected to die from melanoma each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive and expensive and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermatoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We will specifically focus on AI’s application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in-vivo skin imaging devices.

Although evolutionary transitions of individuality have been extensively theorized, little attention has been paid to the origin of levels of organization within organisms. How and why do specialized cells become organized into specialized tissues or organs? What spurs a transition in organizational level in cases where the function is already present in constituent cell types? We propose a hypothesis for this kind of evolutionary transition based on two features of cellular metabolism: metabolic constraints on functional performance and the capacity for metabolic complementation between parenchymal and supporting cells. These features suggest a scenario whereby pre-existing specialized cell types are integrated into tissues when changes to the internal or external environment favor offloading metabolic burdens from a primary specialized cell type onto supporting cells. We illustrate this process of “supra-functionalization” using the nervous system and the pancreas.

This study explored the antimicrobial potential of Piper betle L. (PBL) and Piper nigrum L. (PNL) extracts against MRSA. Plant parts including stem, leaf, and fruit were extracted using aquadest, methanol, and hexane, resulting in 18 distinct extracts. FT-IR combined with cluster analysis (CA) categorized the extracts, and anti-MRSA activity was assessed through the paper disk dif-fusion method. The most potent extracts were further analyzed using GC-MS to identify bioac-tive compounds. Additionally, molecular docking studies were conducted for MRSA protein targets (4DKI, 6H5O, and 4CJN). The hexane extract of PNL and the aqueous extract of PBL fruit showed the strongest inhibitory effects. GC-MS identified Piperine (14.22%) and Diisooctyl phthalate (14.67%) as major compounds, with Piperolein B, Piperanine, Beta-caryophyllene ox-ide, and Alpha-caryophylladienol as minor compounds in the hexane extract of PNL, while hy-droxychavicol (81.89%) and Chavibetol (12.01%) were predominant in the aquadest extract of PBL. Molecular docking revealed that Piperolein B and Piperine had strong binding affinities to MRSA proteins 4DKI, 6H5O, and 4CJN, comparable to ciprofloxacin. In conclusion, this study confirms the potential of PBL and PNL as sources of novel anti-MRSA agents, supporting fur-ther research to develop new therapies.

Presently, the recurrent flood disasters resulting from global extreme weather have inflicted damage upon the Chinese ancient site. Considering the complexity of the materials consist of the Chinese ancient sites, a flood risk assessment method for the ancient sites in Henan province considering multiple types of materials is proposed in this paper. Within the framework, the hazard, sensitivity and vulnerability of the ancient sites are selected and calculated by the selected indicators respectively. To overcome the complexity of the materials, we regard the types of ancient sites as the material score to calculate vulnerability of ancient site. Subsequently, the weight of each indicator is captured based on the analytic hierarchy process (AHP) method. Finally, 157 ancient sites in Henan province are selected for the flood disasters risk calculation and analysis in this paper. The results demonstrate our proposed method can obtain more accurate rainstorm and flood risk of the ancient sites.

In this paper we present the complete technical analysis of the diffusion function D(t) in the hypothesis of quantum-relativistic effects inside a nanostructure. The calculation is carried out through the use of a novel analytical model, known as DS model, that have a wide scale range of applicability and is usable for every oscillating process in Nature. The nanoscale will be considered in the paper, but the presence of a gauge term inside the model allows its application from sub-pico-level to macro-level. In addition to the theoretical framework, the graphical behavior of the function D(t) in some cases is considered and examples of application are performed.

The integration of responsible artificial intelligence (RAI) principles with emerging neurosymbolic AI (NSAI) systems is crucial for the development of fair, explainable, and trustworthy AI technologies. This paper presents a systematic review exploring the convergence of RAI and NSAI, analyzing current research to assess how RAI principles such as explainability, bias, robustness, transparency, and privacy have been applied to NSAI. This work employed a systematic literature review to synthesize findings from a sample of papers demonstrating RAI principle implementations. Our analysis reveals two main trends: significant research demonstrates the application of NSAI to enhance RAI principles in other AI systems, while limited work directly applies RAI principles to NSAI architectures. Key challenges include the lack of established frameworks for implementing RAI within NSAI systems and the com- plexities inherent in merging neural and symbolic reasoning methods. This review highlights open research gaps and suggests pathways for future work, emphasizing the need for robust RAI frameworks tailored to NSAI systems.

Chitosan-based hydrogels are promising in bone tissue regeneration due to their biocompatibility, tunable properties, and ability to mimic the extracellular matrix. With high water content and porous structure, they can encapsulate bioactive agents for controlled release. Chitosan, derived from chitin in crustacean exoskeletons and fungi, possesses mechanical qualities ideal for biomedical applications. This study will evaluate recent advances in chitosan hydrogels for bone regeneration, focusing on their efficacy, mechanisms, and challenges in clinical settings.

The paper presents a comprehensive study of the relationship between investor sentiment and stock market returns in Saudi Arabia from 2009 to 2022. The study uses linear autoregressive distributed lag models (ARDL) to analyze the complex dynamics between variables. ARDL models are particularly valuable for capturing both long- and short-term relationships between variables and allow for dynamic adjustments over time. The study shows that a positive change in investor sentiment leads to higher stock market returns after a lag of two months, but this positive effect reverses in the long-run, suggesting the possibility of overreaction and subsequent adjustments. Money supply has a consistent positive impact on stock returns over time, while real estate market returns have limited short-term impacts on investor sentiment and macroeconomic factors. The ARDL modeling approach is essential to capture the dynamic interaction between investor sentiment and stock returns and reveals the temporary impact of sentiment on market performance. The study extends its analysis to construct a composite investor sentiment index using principal component analysis, tests the stationarity of stock returns and sentiment index through extended Dickey-Fuller and Phillips-Perron unit root tests, and determines the optimal lag length for time series modeling. The ARDL modeling results for stock market returns and real estate returns provide insights into the short- and long-term relationships between investor sentiment, macroeconomic variables, and market returns; in addition, the study provides important insights into the interaction between investor sentiment and stock market returns in Saudi Arabia, with implications for regulatory policy and investment management. The results demonstrate the impact of investor psychology on market performance, which has practical implications for understanding and managing behavioral risk. The study adopts an integrated approach, combining rigorous ARDL models with economic intuition and practical relevance, which is expected to better understand the dynamic interaction between sentiment and returns.

L-(+)-Furanomycin 1 is a miniature antibacterial natural product that contains an -amino acid core. This non-proteinogenic α-amino acid was first isolated in 1967 by Katagiri and co-workers from the fermentation broth of Streptomyces threomyceticus L-803 (ATCC 15795). It is a substrate of isoleucyl aminoacyl tRNA synthetase that replaces isoleucine in the protein translation process and exhibits antibacterial properties in vitro. It effectively acts as an antibacterial agent against M. tuberculosis, E. coli, B. subtilis, and some Shigella and Salmonella bacterial species at concentrations as low as the micromolar range. Consequently, synthetic chemists have garnered considerable interest from their specific structure-activity profile, distinctive chemical compositions, and distinct biological profile. This review comprehensively describes cutting-edge synthetic methodologies for synthesizing furanomycin and its analogs reported to date. Therefore, this review will offer an initial perspective on synthesizing furanomycin and its customized compounds.

This study investigates the reintroduction and ecological adaptation of the endangered dung beetle, Gymnopleurus mopsus, in South Korea, a region from which it has been absent since the 1970s. To facilitate this, we imported genetically identical populations of G. mopsus from Mongolia and embarked on a comprehensive restoration research project. A key focus of this endeavor was to evaluate the adaptability of these beetles to the local environment, an essential aspect of successful reintegration of species from foreign ecosystems. Under meticulously controlled field cage conditions, we conducted an in-depth monitoring of the life history traits of G. mopsus. This monitoring revealed that the adult beetles, which entered hibernation in September 2019, began emerging in stages from late April to May 2020. Following hibernation, we observed that the adults engaged in reproductive activities from late May until early August, with the emergence of the first-generation (F1) adults occurring from late July to mid-September. This led to a notable tripling in population size, increasing from 34 to 109 individuals. The successful survival and reproductive behaviors of these Mongolian dung beetles in the climatic conditions of Korea suggest a promising potential for their adaptation when reintroduced into native habitats. We are now directing our efforts towards long-term monitoring, focusing on the survival and reproductive efficacy of these new generations, to further validate the success of this ecological restoration project.

Building or solar orientation, a key architectural design parameter, significantly influences energy consumption in buildings. Optimizing building orientation to harness passive solar benefits is a fundamental and cost-effective measure in designing energy-efficient buildings. The optimal orientation, however, varies with the geographical location, climatic conditions, and building type. Notably, Afghanistan’s building sector lack tailored energy efficiency regulations. Therefore, this study investigates the impact of building orientation on the energy performance of residential buildings across nine cities in Afghanistan, each characterized by distinct climatic conditions and geographic locations, employing BEoptTM energy simulation software. The findings reveal diverse optimal orientations, dividing the country into three distinct climatic zones: subarctic, continental, and hot arid, offering potential energy savings ranging from 25.6% to 48.9% compared to the least efficient orientations. These insights are crucial for formulating standardized building regulations in Afghanistan, emphasizing location-specific design approaches. This research also has implications for urban planning decisions that promote sustainable built environment.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p,q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

During the past 25 years, vesicular stomatitis virus (VSV) has produced multiple epidemic outbreaks in the US, resulting in the emergence of different viral lineages. Currently, very little is known about the pathogenesis of many of these lineages, thus limiting our understanding of the potential biological factors favoring each lineage in these outbreaks. To determine the potential phenotypical differences between two VSV Indiana (VSIV) serotype epidemic strains using a pig model. These strains are representative of the epidemic lineages that affected the US between 1997-1998 (IN98COE) and 2019-2020 (IN0919WYB2), the latter responsible for one of the most extensive outbreaks in the US. Our initial genome analysis revealed the existence of 121 distinct mutations between both strains, including the presence of a 14-nucleotide insertion in the intergenic region between G and L genes observed in IN0919WYB2. The levels of viral RNA in clinical samples between pigs infected with IN98COE or IN0919WYB2 were compared. Overall, higher and prolonged expression of viral RNA in pigs infected with IN98COE was observed. However, clinically, IN0919WYB2 was slightly more virulent than IN98COE, as well as more efficient to produce infection through contact transmission. Additionally, infectious virus was recovered from more samples when pigs were infected with IN0919WYB2, as revealed by virus isolation in cell culture, indicating the increased ability of this virus to replicate in pigs. Sequence analyses conducted from isolates recovered from both experimental groups showed that IN0919WYB2 produced more variability during the infection, denoting the potential of this strain to evolve rapidly after a single infection-contact transmission event in pigs. Collectively, the results showed that epidemic strains of VSIV may represent disparate phenotypes in terms of virulence/transmissibility for livestock, a situation that may impact the intensity of an epidemic outbreak. This study also highlights the relevance of pathogenesis studies in pigs to characterize phenotypical differences of VSV strains affecting livestock in the field.

Hydroponics, a soilless cultivation method, is increasingly being adopted for its ability to produce high-quality vegetables while reducing resource usage and minimizing the risks of soil-borne pathogens. However, the reuse of nutrient solutions in hydroponics can accelerate the spread of pathogenic microorganisms. This study aimed to evaluate the bacterial decontamination effects of UV-C light (254 nm) on a hydroponic nutrient solution. The solution was contaminated with Escherichia coli and irradiated with UV-C at energy doses of 20.37 and 489.02 mJ/cm². Experiments were conducted under both static and dynamic fluid conditions to assess bacterial inactivation and changes in electrical conductivity over time. Results showed no significant change in electrical conductivity after 144 hours of UV-C exposure. Under static conditions, bacterial inactivation was observed, with a reduction of 1.7 log CFU/mL at 20.37 mJ/cm², though no significant additional reduction was seen at the higher dose. However, dynamic conditions resulted in greater inactivation, with reductions of 4.2 and 5.2 log CFU/mL at 20.37 and 489.02 mJ/cm², respectively. These findings suggest that fluid agitation plays a crucial role in enhancing UV-C efficiency by preventing bacterial sedimentation, which occurs in static conditions. The use of UV-C as an eco-friendly disinfection method appears promising for hydroponic systems, as it does not alter the composition of the nutrient solution.

Limited evidence on hepatitis A seroprevalence among HIV-exposed uninfected (HEU) children compared to their HIV infected (HIV) and unexposed uninfected (HUU) counterparts compromises rational vaccine decision-making. This study comprised of a retrospective health facility-based population of children aged 1 month – 12 years. Archival sera were tested for markers of acute (anti-HAV IgM) or past (total anti-HAV) HAV infection. Subgroup analysis was conducted based on HIV exposure or infection status. Of 513 participants, 54% (277) were male and the median age was 10 (IQR: 4 – 25) months. The median maternal age was 29 (IQR: 25 – 34) years. An anti-HAV seropositivity of 95.1% (117/122 [95% CI 90.2-98.4]) was found among those ≤6 months of age, indicative of the rate of transplacental antibody transfer. Among 1–12-year-olds, hepatitis A seroprevalence was 19.3% (37/192 [95% CI 14.1-25.7]), while 1.1% (2/188 [95% CI 0.12-2.76]) had evidence of acute infection. Compared to HIV-exposed subgroups (HIV=60%, 6/10 [95% CI 27.4-86.3] and HEU=45%, 9/20 [95% CI 23.8-68]), hepatitis A seroprevalence among HUU children was low (29.2%, 47/161 [95% CI 22.4-37.0]). Natural immunity among HIV -exposed and -unexposed children in South Africa is insufficient to protect against severe liver complications associated with HAV infection later in adulthood.

Background: Bloodstream infections (BSIs) pose a major health risk to hemodialysis patients, significantly contributing to morbidity and mortality. This study aimed to identify the bacterial pathogens responsible for BSIs in this population, analyze clinical and laboratory profiles, and identify risk factors associated with poor outcomes. Methods: This retrospective study was conducted at Sohar Hospital, Oman, analyzing cases from January 2018 to December 2022. Data were obtained from e-health records, focusing on patient demographics, bacterial etiology, antibiotic susceptibility, and clinical outcomes. Statistical analysis included t-tests, Mann-Whitney tests, chi-square, and Fisher’s exact tests, with univariate odds ratios calculated for mortality associations using the R package GTSUMMARY. Results: Among 1332 hemodialysis patients, 148 (11.1%) developed a BSI, with an average age of 60.43±14.46 years. Infections were more common among females (54%) and those over 60 years. The predominant comorbidities were hypertension (86%) and diabetes mellitus (74%). The main causative agents were coagulase-negative staphylococci (29.7%), Staphylococcus aureus (20.3%), Pseudomonas aeruginosa (12.2%), and Klebsiella pneumoniae (8.8%). Gram-negative bacteria showed high susceptibility (80-100%) to carbapenems and piperacillin-tazobactam, while Gram-positive bacteria exhibited 80-100% susceptibility to amoxicillin-clavulanic acid and universal sensitivity to linezolid and vancomycin. Conclusions: BSIs in hemodialysis patients at Sohar Hospital are associated with high morbidity and mortality rates, underscoring the need for prompt empirical antibiotic therapy and targeted treatment adjustments based on culture results. This study highlights critical mortality-associated factors, offering valuable insights for improving patient management and survival outcomes in this vulnerable population.

Sleep deprivation is an increasing problem in the modern age. While molecular theories on the mechanism of sleep are still developing, sleep’s importance for cognitive function and physical health has been well reported. In the past 2 decades, major advances in the genetics of sleep have been made. Among families of natural short sleepers averaging around 4-6 hours nightly, mutations on 4 genes (DEC2, ADRB1, NPSR1, and GRM1) have been associated with significantly shorter sleep without observed cognitive or health detriments. Studies in mice have further established causality of these mutations as well as finding increased resistance to neurodegeneration. Yet, despite these promising results, there is a current lack of studies investigating potential chronic risks of sleep deprivation, particularly physical and physiological effects that may not be apparent. For circadian repressor element DEC2, circadian dysregulation risks should be evaluated further. For adrenergic receptor ADRB1, cardiovascular and heart failure risks would be of interest. For NPSR1, asthma and other immune responses, as well as mood disorders are relevant. For the currently least studied G-protein receptor GRM1, its altered regulation of the extracellular signal-regulated kinase (ERK) pathway could result in developmental and proliferation effects. While these familial natural short sleep genes hold potential for our understanding of sleep and therapeutic development, it is critical not to overdraw conclusions. By investigating the possible downstream effects, one can be aware of the potential risks of natural short-sleep genetics, better understand the mechanisms of sleep, and advance research in reducing the harms of sleep deprivation.

Hybrid nanocomposites have emerged as a groundbreaking class of materials in the aerospace industry, offering exceptional mechanical, thermal, and functional properties. These materials, composed of a combination of metallic matrices reinforced with a mixture of nanoscale particles, such as carbon nanotubes (CNTs), graphene, and ceramic nanoparticles (SiC, Al2O3), provide a unique balance of high strength, low weight, and enhanced durability. Recent advances in developing these nanocomposites have focused on optimizing the dispersion and integration of nanoparticles within the matrix to achieve superior material performance. Innovative fabrication techniques have ensured uniform distribution and strong bonding between the matrix and the reinforcements, including advanced powder metallurgy, stir casting, in-situ chemical vapor deposition (CVD), and additive manufacturing. These methods have enabled the production of hybrid nanocomposites with improved mechanical properties, such as increased tensile strength, fracture toughness, wear resistance, and enhanced thermal stability and electrical conductivity. Despite these advancements, challenges remain in preventing nanoparticle agglomeration and ensuring consistent quality and repeatability in large-scale production. Addressing these issues is critical for fully leveraging the potential of hybrid nanocomposites in aerospace applications, where materials are subjected to extreme conditions and rigorous performance standards. Ongoing research is focused on developing novel processing techniques and understanding the underlying mechanisms that govern the behavior of these materials under various operational conditions. This review highlights the recent progress in the design, fabrication, and application of hybrid nanocomposites for aerospace applications. It underscores their potential to revolutionize the industry by providing materials that meet the demanding requirements for lightweight, high-strength, and multifunctional components.

New conjugated 2,6-diphenylaniline-azulene co-oligomers of linear and branched structure were synthesized by the interaction of borylazulenes with diphenylaniline bromides under Suzu-ki-Miyaura cross-coupling conditions. The obtained diphenylaniline-azulene co-oligomers inten-sively absorb and emit visible light (410-700 nm region), in particular, exhibits strong emission in the green as well as orange range with maxima of 510 / 590 nm. It is shown that such properties appear as a result of the positive resonance exposure to ani-line fragments significantly rearranging the electronic structure of azulene, in particular, the lev-els and energy gaps of frontal HOMO - LUMO orbitals.

(1) Background: This article examines the funding landscape of climate change adaptation research in South Africa to understand the origin, distribution, and thematic prioritization of funding for climate-change research in South Africa. (2) Methods: The article used specific search terms to query peer reviewed publications in the Web of Science and Scopus databases (1985 to 2023). (3) Results: Environmental Science emerged as the most dominant research area during this period, while agriculture appeared in the top four position among research areas in climate change adaptation research in South Africa in both databases. The largest number of publications on climate change adaptation research in South Africa is generated through domestic funding, followed by the United States of America, the United Kingdom, and Germany. The National Research Foundation (NRF) had the highest number of publications. The analysis revealed a strong paucity of funding relationship between South Africa and other African countries in the field of climate change adaptation research. (4) Conclusions: Knowledge of these trends can inform policy decisions on funding of climate change adaptation in South Africa and strategic partnerships between South Africa and the rest of the world.

This study deals with the turning of AISI O2 steel, which has high machinability and dimensional stability in heat treatment and is used in the manufacture of many machine elements, with cryogenically treated and untreated and coated and uncoated cutting tools. In the first phase of the study, along with the determined cutting parameters, Fc and Ra output parameters were also optimized using the RSM method. In addition, numerical significance was obtained with ANOVA analysis, and estimated values were calculated by preparing regression equations. In the second phase, the wear performance of cutting tool tips with and without deep cryogenic treatment was explained with the support of SEM images, EDX, XRD, and microhardness analyses. Feed (F) was found to be the most influential parameter on Fc at 78.89%. The lowest optimized Fc value found with the RSM method was 339.99 N. In order to obtain this value, Vc 252.525 m/min and F 0.16 mm/rev should be selected. In addition, CVD-coated and cryogenic heat-treated cutting tools should be preferred for ideal Fc. It was shown that the most effective parameter on Ra was F, with 69.38%. For both heat treatment and coating conditions, Ra increased significantly as the feed rate increased. In the case of selecting Vc as 243.434 m/min and F as 0.16 mm/rev, the smallest Ra value optimized with the RSM method is 1.04 μm. In addition, a cutting tool without coating and cryogenic heat treatment is also needed for ideal Ra. According to the results of the analysis confirming the effect of deep cryogenic heat treatment on the wear performance of cutting tools in the second stage, coated and uncoated cutting tools had at least 16% longer life compared to the others.

Age-related diseases place an intense and growing stress on healthcare systems. Numerous theories of ageing exist. Yet prediction, prevention and treatment limitations of age-related disease persist because we lack a complete aetiological understanding of their origins. Here we propose a new theory that aims to provide a blueprint-type understanding of ageing and disease aetiology, working across multiple scientific disciplines. Supported by specific examples, we demonstrate that the primary driver of much of age-related diseases is the futile triggering of specific biological pathways in a pathological manner, which we term pathophysiological pathways or patho-pathways. Patho-pathways induce pathological cell, tissue and system changes in the form of dysactivity (out of context activity that is at the wrong time and/or place), which is characteristic of age related disease. As part of patho-pathways, all wild-type (normal) genes have the potential to contribute to age-related diseases (though specific risk varies and can be formularised). In a domino-style effect, one patho-pathway typically triggers others, resulting in complex, but traceable, cascades. Utility, in terms of changing clinical and scientific practice, is derived from mapping these cascades to create Blueprint Maps that allow identification of biomarkers and therapeutic targets far upstream in the causal chain of events. Finally, our theory provides testable hypotheses concerning predictions of links between otherwise seemingly disparate diseases as well as new interventions to reduce ageing.

Biologists often set out to find relevant data in an ever changing landscape of interesting databases. While leading journals publish descriptions of databases, they are usually not current and do not frequently update the list to discard defunct or poor quality database. These indices usually include databases that are proactively requested to be included by their authors. The challenge for individual biologists then is to discover, explore and select databases of interest from a large unorganized collection and effectively use them in analysis without too large an investment. The FAIR data principle being advocated to improve searching, finding, accessing, and inter-operating among these diverse information sources to increase usability is proving to be a difficult proposition and consequently, a large number of data sources are not FAIR compliant. Since linked open data does not guarantee FAIRness, biologists are now left on a solo hunt for information on the open network. In this paper, we propose SoDa, for intelligent data foraging on the internet by biologists. SoDa helps biologists discover resources based on analysis requirements, generate resource access plans, and stores cleaned data and knowledge for community use. A secondary search index is also supported for community members to find archived information in a convenient way.

This study aimed to investigate the potential of the antagonistic bacterium KPB25 (Bacillus altitudinis) as a biological control agent against fire blight by analyzing the changes in the epiphytic and endophytic bacterial communities of apple tree leaves following treatment. We used metagenomic analysis to examine the diversity and similarity of the bacterial communities before and after KPB25 treatment. The KPB25 treatment resulted in increased community richness and diversity in endophytic bacteria. Conversely, in epiphytic bacteria, community diversity decreased after treatment. Beta-diversity analysis revealed that the endophytic community formed distinct clusters following KPB25 treatment, indicating a significant shift in the community structure. However, the epiphytic community did not exhibit any clustering, and the KPB25 treatment did not result in substantial changes in the community structure. Relative abundance analysis of the endophytic and epiphytic communities highlighted that some bacterial families, which increased in abundance following KPB25 treatment, oxidized sugars into organic acids or produced antibiotics, potentially creating an environment that makes it difficult for Erwinia amylovora to survive when attempting to infect its host. These findings suggest that KPB25 interacts with certain microbial taxa within apple trees, contributing to the regulation and alteration of the microbial community in a manner that promotes an environment unfavorable for E. amylovora. Overall, KPB25 enhanced microbial groups within the residual bacterial community of apple leaves that contributed to fire blight suppression but did not significantly disrupt or alter the pre-existing community. Therefore, KPB25 has the potential to be a biological control agent for the management of fire blight in apple orchards. Its ability to interact with and modify the microbial community without causing significant disturbance further highlights its suitability for biological control.

Critical and progressive cachexia may be observed in numerous medical disciplines, but in patients with various diseases several pathways overlap (endocrine, inflammatory and kidney diseases, heart failure, cancer). A case-control analysis is presented of patients with initial immune-mediated thyroiditis complicated with cachexia, pulmonary pa-thology coexisting with opportunistic infection, finally diagnosed cancer (thyroid cancer, misdiagnosed as NSCLC). Contrary to other patients with lung cancer the presented pa-tients were not active smokers, exclusively women who developed cachexia with existing autoimmune process in the first phase. Furthermore, coexistence of short overall survival without cancer progression in the most seriously ill patients, correlation with sex (con-trary to history of smoking) and predisposition to mycobacterial disease are very sug-gestive. The unique sequence shed light on immune-mediated thyroid disease as a sub-clinical paraneoplastic process modified by various therapeutic regimens, however, with systemic consequences and a sequel, requiring holistic approach. Differential diagnosis of severe cachexia, adenocarcinoma with pulmonary localization and tuberculosis reac-tivation require analysis of immunological and genetic background. Contrary to highly specialized teams (e.g. lung cancer units) the immunotherapy and general medicine in aging populations require multidisciplinary observation, holistic and inquiring approach. Lack of differentiation, confusing bias as well as discrepancy in literature are the main obstacles in statistical research, that indicate correlation of common factors only. Such time-lapse case studies may be first to build evidence of a pathway and association be-tween immune parameters, comorbidity and cancer.

Liquid crystal torons, localized topological structures, are known for their stability and dynamic behaviour in response to external stimuli, making them attractive for advanced material applications. In this study, we investigate the flow of torons in chiral nematic liquid crystals around obstacles. We simulate the fluid flow and director field interactions using a hybrid numerical method combining lattice Boltzmann and finite difference techniques. Our results reveal that the toron dynamical behaviour depends strongly on the impact parameter from the obstacle. At impact parameters smaller than half cholesteric pitch, the flowing toron is destabilized by the interaction with the obstacle; otherwise, the flowing toron follows a trajectory with a deflection which decays exponentially with the impact parameter. Additionally, we explore the scattering of torons by multiple obstacles, providing insights into how the dynamics of these structures respond to complex environments.

Building Information Modeling (BIM) is a revolutionary method in the construction industry that enhances project coordination, minimizes mistakes, and enables efficient project completion. An essential aspect of achieving successful BIM implementation is the creation of a BIM Execution Plan (BEP). The BEP facilitates effective communication, defines roles and responsibilities, and establishes standardized processes for all stakeholders involved. Although BIM is being used more widely, the absence of standardized BEPs frequently leads to inefficiencies in projects, especially those of significant scale [3]. This paper introduces the development of a new dynamic platform designed specifically for automating and standardizing the creation of BEPs. The platform aligns with ISO 19650 standards and offers customizable workflows to suit various project needs, improving collaboration, data management, and project outcomes. In addition, the paper emphasizes the advantages of utilizing this platform, including the efficient creation of BEPs, improved collaboration through centralized data management, and the ability to tailor BEPs to specific project requirements. The platform's user-friendly interface and extensive data management capabilities not only increase efficiency but also minimize risks and enhance project results. The survey results indicate that the BEP platform is effective in resolving common issues encountered in the creation of BEPs, such as miscommunication and inconsistencies. Additionally, it helps to improve project outcomes and reduce risks.

In today’s fast-paced, modern world, parents often face significant challenges in balancing their professional responsibilities with the demands of child monitoring and nurturing. Our project addresses these challenges by developing an IoT-based Smart Cradle Monitoring System, designed to assist parents in ensuring their child's well-being while they are working. The smart cradle features an automatic swinging mechanism that activates upon detecting the sound of a baby crying, soothing the baby promptly without requiring immediate parental intervention. Additionally, a web camera provides continuous video surveillance, allowing parents to monitor their child remotely in real-time through dedicated alerts. The system leverages IoT technology to connect various components, including an Arduino microcontroller, Raspberry Pi high-sensitivity sound sensors, and a motorized swinging mechanism. This integration ensures seamless operation and remote access, offering peace of mind to working parents. By upgrading traditional cradles with modern electronics, our project provides a reliable and efficient solution that enhances child care, ensuring both the child’s well-being and the parent’s peace of mind. This smart cradle is designed to alleviate the challenges faced by working parents, offering superior results compared to conventional cradles, and helping parents focus on their work while being reassured of their child's safety and comfort.

Self-assembling peptides have emerged as promising materials for pharmaceutical and biomedical applications due to their unique biocompatibility, biodegradability, and multifunctionality. These properties make them ideal candidates for addressing the challenges posed by biofilm-associated infections linked to implanted medical devices. Additionally, these peptides can form a variety of nanostructures, including nanotubes, hydrogels, and nanosheets, which facilitate targeted drug delivery and enhance therapeutic efficacy. The successful translation of peptide-based materials into clinical applications is exemplified by products like RADA16 and lanreotide, which improve therapeutic outcomes and enhance patient compliance. Furthermore, the development of peptidomimetics extends the versatility of these materials across various biomedical applications. This review highlights the potential of peptide-based biomaterials as effective tools in combating infections and as innovative platforms for drug delivery, emphasising their significance in modern medical therapies.

Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents. This study examined whether heart rate data can predict strategy effectiveness. Teachers and coders from the research team recorded behavioral and heart rate data over three months for each participating student on the autism spectrum using the KeepCalm app, a platform that provides in-the-moment strategy suggestions based on heart rate and past behavioral data, across 226 instances of strategy interventions. A binary logistic regression was performed to assess whether heart rate reduction, time to return to heart rate baseline, and documented skills and challenging behaviors predicted strategy effectiveness. Results suggested that heart rate reduction may be a significant predictor, and supported the existing practice of using behavioral patterns as proxies for strategy effectiveness. Additional analyses indicate proactive strategies are more effective and are associated with greater reduction in heart rate, relative to reactive strategies. Further exploration of how internal physiological data can complement observable behaviors in assessing intervention strategy effectiveness is warranted given the novelty of our findings.

This article centers on the experiences and voices of eight Kenyan youth climate change activists by highlighting how they navigate local and global challenges while engaging in climate change action. Specifically, the article examines how the activists are combating food insecurity and promoting biodiversity restoration by discussing findings from two projects. The first project is the Ondiri Wetland Botanical Gardens, where youth activists have planted over 270 indigenous trees, of which about 58 are different species of trees native to the Kenyan Highland of Kikuyu, to counter the biodiversity loss and alleviate the impact of climate change on flora and fauna. The second project is the One Million Trees for Kilifi project, where activists are planting orchards in various schools in Kilifi County in Kenya to mitigate the impact of climate change and end malnutrition among K-12 learners. Guided by the Youth Participatory Action Research (YPAR) methodology, this study counters the deficit and exclusionary narrative that has often left out young people from climate change education and governance policy decision-making and advocates for a more equitable and inclusive approach that centers their voices and perspectives in solving existential problems like climate change.

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OS). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OS, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OS; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model's accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

Background: Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) is an emerging technique in trauma care for managing severe hemorrhage when conventional resuscitation is inadequate. Methods: We report a case involving a 45 yo female patient who sustained multiple traumatic injuries—including thoracic, pelvic, and aortic damage—after a fall from approximately 5 meters. The timely insertion of a REBOA device provided critical hemodynamic support, allowing for temporary stabilization in the emergency department according to the principles of Damage Control Resuscitation. Results: This intervention facilitated her subsequent transfer to a Level 1 Trauma Center for definitive surgical intervention.Although the patient's prognosis remained poor due to the severity of her injuries, the use of REBOA illustrated its potential utility in improving hemodynamic stability in critically injured patients. Conclusions: Our findings suggest that REBOA can be a valuable adjunct in trauma management, offering a life-saving option in critical hemorrhagic cases where traditional methods are insufficient. Further research is warranted to refine REBOA application, particularly concerning optimal balloon positioning and integration with imaging techniques.

This study introduces an integrated methodology that incorporates vessel motion dynamics into the evaluation of operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs). By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, the methodology accounts for vessel motion effects during offshore operations. The approach was demonstrated through a numerical case study at two wind farm sites, Marram Wind and Celtic Sea C, with a fictive wind farm layout of 100 × 15 MW NREL turbine on a UMaine VolturnUS-S platform. Three Major Component Replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy resulted in the highest O&M costs—94 k€/MW/year at Marram Wind and 97 k€/MW/year at Celtic Sea C—due to extended MCR durations (90-180 days), leading to lower availability (90%-94%). In contrast, the FTF and SHC strategies, which are still under development, demonstrate significantly lower costs and reduced downtime. The SHC strategy, in particular, has proven to be the most cost-effective, achieving up to a 64% reduction in costs while increasing availability to 97%-98%. The integrated approach incorporates vessel dynamics, accounting for factors such as wave direction, wave period, and vessel response to varying sea states. This allows for greater flexibility in setting operational limits, potentially permitting higher limits in favorable conditions where vessel motion impact is reduced. However, in scenarios where dynamic vessel responses lead to increased motions—such as when waves approach from the side or when the wave period is close to the vessel's natural roll period—more restrictive limits may be necessary, even if significant wave heights are lower. This flexibility or restriction highlights the importance of incorporating motion-based dynamics for emerging technologies in the evolving FOWT O&M market.

Cutaneous metastases from clear cell renal carcinoma (ccRC) are uncommon and often indicate a poor prognosis. These metastases typically occur on the scalp, face, and trunk, and they can be difficult to diagnose due to their resemblance to benign dermatological tumors. We report the case of a 56-year-old patient with a history of ccRC (TNM stage 4) who was referred to our Dermatology department with two rapidly enlarging, painful lesions on the left jawline and scalp, which had developed one month and one week earlier, respectively. On examination, the lesions appeared as well-defined, round to oval plaques with a central ulceration and a peripheral red rim. Dermoscopic examination revealed a structureless pink to orange pattern, atypical central vessels, and irregular linear vessels in a corona-like arrangement. Despite the patient's stable oncological treatment for six months, pain management had recently included paracetamol, tramadol, and NSAIDs. Given the recent medication changes and the clinical presentation, differential diagnoses initially included drug-induced eruptions such as erythema multiforme or fixed drug eruption. However, a biopsy of the facial lesion revealed immunohistochemical positivity for CD10, CAIX, and PAX8, confirming the diagnosis of metastatic ccRC with sarcomatoid differentiation. Unfortunately, despite continued targeted therapies and palliative care, the patient’s condition deteriorated rapidly, leading to death two months later. This case highlights the aggressive progression and poor prognosis associated with cutaneous metastases from renal cell carcinoma, particularly once the skin is involved.

The concept of clean energy was introduced by the Mexican authorities as part of the wholesale electricity market with the objectives of both measuring the progress in decarbonization and fostering emission-free and low-emission technologies. In the present work, the evolution of clean energy production for the period 2017-2023, corresponding to seven full years of operation of the electricity market, was analyzed and compared to official targets. Emission of greenhouse gases (GHG) was calculated from fuel consumption statistics. The consistency between electricity generation and fuel consumption data has been assessed. The projected short-term evolution of electricity generation and GHG emissions through 2026, locked in by decisions in the recent past, is modeled and discussed. The extent to which official clean energy figures can be considered a predictor for emissions from the power sector is discussed as well. The results are believed to be a relevant contribution for the assessment of Mexico’s progress with its national and international climate change mitigation goals.

Polo-Like Kinase-1 (PLK1) is a key regulator of the G2/M cell cycle and has been linked to treatment resistance in prostate cancer (PCa). This study aimed to explore the correlation between PLK1 expression, determined through RNA-seq analysis, and clinical outcomes in PCa patients. We analyzed tumor samples from 452 patients diagnosed with prostate adenocarcinoma between 2014 and 2020, sourced from nine institutions affiliated with the Oncology Research Information Exchange Network (ORIEN). PLK1 expression levels were determined using RNA-seq data and stratified into high (≥75th percentile) vs. low (<75th percentile) expression. Univariate and multivariate Cox proportional hazard models compared overall survival between patients with low vs. high PLK1 expression, adjusting for age, race, PSA, and stage. Of the 452 tumors, 241 (53.3%) had high PLK1 and 211 (46.7%) had low PLK1 expression. Median follow-up was 2.74 years for low PLK1 and 2.71 years for high PLK1. No significant difference in overall survival was observed between low and high PLK1 groups in univariate (p=0.86; HR 1.09) or multivariate (p=1.0; HR 0.99) analyses. This study found no association between RNA-seq-based PLK1 expression and prognosis in PCa. PLK1 expression is not a prognostic marker for prostate cancer.

Introduction: Multiple sclerosis (MS) is a long-term inflammatory disease that affects the central nervous system due to an abnormal immune response. Hormonal and enzyme changes in these patients can influence the progression and prognosis of the disease. In this research, the levels of cortisol, adrenocorticotropic hormone (ACTH), melatonin, and lactate dehydrogenase (LDH) in the blood of MS patients were studied.Methods: This study involved 50 MS patients and 50 healthy individuals. The levels of ACTH, melatonin, and cortisol in the blood were measured using specific ELISA kits. The data obtained were then analyzed statistically using SPSS software.Results: The findings revealed a significant increase in the levels of cortisol and LDH in MS patients (P<0.05), while the levels of melatonin and ACTH showed a significant decrease (P<0.05). The elevated cortisol levels may be attributed to chronic stress and inflammation associated with MS. An increase in LDH levels can indicate tissue damage caused by myelin destruction. On the other hand, decreased melatonin levels may lead to sleep disturbances and increased stress in these patients, while decreased ACTH levels may be linked to disruptions in the hypothalamic-pituitary-adrenal (HPA) axis. Conclusion: The hormonal and enzyme changes observed in MS patients illustrate the wide-ranging impact of the disease on various body systems. The increase in cortisol and LDH as well as the decrease in melatonin and ACTH could serve as useful biomarkers for predicting disease progression and managing the condition. These findings align with previous studies and contribute to a deeper understanding of the pathological mechanisms of MS.

Background: Cancer remains a leading cause of death worldwide. The progress in effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with co-morbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have become instrumental in discovery of genetic and molecular biomarkers to cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in co-morbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusion: AI holds significant potential to improve the precision and effectiveness of cancer management and therapy, particularly for cancer patients with co-morbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for outlines approach.

Background: Intestinal helminthiases and schistosomiasis are among the neglected tropical diseases targeted by the World Health Organization (WHO) for elimination as a public health issue by 2030. For this, it is important to determine their distribution and predicting factors, after a decade of mass deworming campaigns. In particular, such information is scarce among pre-school aged children who have not been the target for epidemiological studies and schistosomiasis deworming activities. This cross-sectional study and secondary data analysis focused on preschool-aged children and was aimed to determine the prevalence of intestinal helminthiases (Ascaris, ankylostoma, trichirus trichura, taenia) and Schistosomiasis, and identify the factors associated with them in Rwanda. Methods: Data from 4,675 preschool-aged children randomly selected from 17 districts endemic to Schistosoma mansoni were analyzed. Parasitological assessment was performed using the Kato Katz technique. Having at least one intestinal helminthiasis or schistosomiasis case was considered a positive outcome. A structured questionnaire for childcare guardians, literature, and GIS were used to collect information regarding environmental factors. Data were aggregated at village level to calculate village prevalence used in depicting the distribution of intestinal helminthiases and schistosomiasis. Descriptive analysis was performed, and prevalence was assessed as a measure of frequency. The chi-square test was used for bivariate analysis whereas logistic regression was used for multivariable analysis. The confidence level was set at 95%, and the decision was made based on the P-value. Odds ratios (ORs) were assessed as a measure of association. STATA 15 and ArcGIS software were used in this study. Results: The prevalence of intestinal helminthiases and schistosomiasis among preschool-aged children was 20.3%. Ascaris was the most prevalent parasite in this study (15%). Being older than 24 months, having self-employed parents, residence, fetching water into the lake or water body, land cover, clay soil, and high LST are predictors of intestinal helminthiases and schistosomiasis among preschool-aged children in Rwanda. Conclusion: The prevalence of 20.3% is moderate and in the march towards the elimination of these parasitic infections, deworming activities should be coupled with water supply, reinforcement of the use of Early Child Development (ECD), relocating people living in proximity of wetlands and water bodies, and health education are key interventions in preventing preschool-aged children to get intestinal helminthiases and Schistosomiasis.

This research is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (grant AP14871855).

The application of wool as mulch in strawberry cultivation was analysed to find a so-lution for the rational use of wool from mountain sheep. In the plantation, the experimental plots mulched with wool, straw and bark were appointed. The plots were monitored during the ex-periment, while the soil temperature and moisture content were measured. The data collected in two-hour intervals were analysed, taking into account air temperature and falls registered in the local meteorological station. Additionally, the progress of mulch biodegradation was tracked. The changes in the wool morphology that occurred by biodegradation were observed during micro-scopic examinations using the Scanning Electron Microscope (SEM). It was stated that wool mulch plays an essential role in thermoregulation of the soil surface, prevents the overheating of the soil during the summer heat and protects it against excessive cooling during cold nights. The wool mulch minimises the fluctuations between the soil’s day and night temperature. The fluctuations do not exceed 2 - 3 degrees on hot summer days, which are five times smaller than for the control plot. The wool retains large amounts of rainwater several times its weight. The water is then slowly released, providing the growing plants a moist environment during a longer rainless pe-riod. Moreover, wool is difficult to biodegrade and maintain its properties for a long time, lasting longer than one vegetation season. Compared to straw and bark, the temperature fluctuations recorded for wool are two times smaller, and its effectiveness in water management is consid-erably better. The beneficial impact of the wool mulch ensuring favourable conditions for strawberry growth was explained by the specific wool structure and its unique properties.

This study examines Brazil's complex economic journey from 1930 to 2000, exploring the interplay between historical legacies, policy choices, and global economic forces in shaping the nation's development trajectory. It argues that Brazil's economic history during this period is characterized by a series of paradoxes: rapid industrialization alongside persistent inequality, economic miracles followed by devastating crises, and modernization efforts constrained by colonial-era structures. The analysis begins with the Estado Novo of Getúlio Vargas, tracing the evolution of Brazil's economy through state-led industrialization, the "Economic Miracle" of the military regime, the debt crisis and hyperinflation of the 1980s, and the neoliberal reforms of the 1990s. Special attention is given to the enduring impact of slavery and colonial land distribution on Brazil's social and economic structures. The study highlights how these historical factors have contributed to ongoing challenges such as regional disparities, income inequality, and the "Brazil Cost." By examining these issues, the paper provides insights into the complexities of economic development in postcolonial societies and the challenges of overcoming historical constraints in the pursuit of equitable and sustainable growth.

Molecular cages are preorganized molecules with a central cavity, typically formed through the self-assembly of their building blocks. This requires in most cases forming and breaking reversible bonds during the self-assembly reaction pathway for error correction to drive the reaction to the cage product. In this work, we focus on both Pd–ligand and hydrazone bonds implemented in the structure of a Pd2L4 hydrazone molecular cage. As the cage contains two different types of reversible bonds, we envisaged a cage formation comparative study by performing the self-assembly of the cage through 3 different reaction pathways involving the formation of Pd–ligand bonds, hydrazone bonds, or a combination of both. The 3 reaction pathways produce the self-assembly of the cage with yields ranging from 73% to 79%. Despite the complexity of the reaction the cage is formed in a very high yield, even for the reaction pathway that involves the formation of 16 bonds. This research paves the way for more sophisticated cage self-assembly designs.

Alzheimer's disease is a progressive neurodegenerative disorder that disrupts the brain regions involved in memory and cognitive functions. This impairment ultimately results in severe deficits in memory, learning, reasoning, communication, and the ability to perform daily activities. Our research reveals a statistically significant decrease in the density of neural progenitor cells within the dentate gyrus of the hippocampus and the subventricular zone of the lateral ventricles in patients diagnosed with Alzheimer's disease. This reduction correlates with the disease's progression. As Alzheimer’s disease advances, cognitive functions decline progressively, leading to the identification of five distinct stages of β-amyloid distribution across various brain structures. Early symptoms include short-term memory deficits and learning difficulties, along with cognitive, psychiatric, and neurological disturbances. Given the hippocampus's essential role in these functions, one would expect to see substantial changes in this brain region. Thus, understanding adult neurogenesis is vital in Alzheimer’s disease, particularly since the hippocampus, a primary neurogenic area, is notably impacted in affected individuals.

Polluting discharges are produced at a rate that is inexorably rising in tandem with human activity development. Eutrophication and other matters, such as offensive aquatic life, require wastewater to be treated before being discharged into the environment. Prior releasing into water bodies, the treated waters must to undertake laboratory analysis. For this purpose, an analysis using standard methods was carried out at the Laboratory Waterworks in Debrecen on various samples taken from various wastewater treatment plants (sewage treatment plants) in Hungary. The overall objective of this study was to assess the quality of the effluents from the fourteen (14) wastewater treatment plants and investigate their efficiency by measuring various physicochemical parameters and microbiological parameters as excellent indicators to evaluate the performance of the studied wastewater treatment plants. The microbiological indicator monitored was the heterotrophic count (HPC) by estimating its total number, which provides useful information for the assessment and monitoring of water samples. The determination of physicochemical parameters included biological oxygen demand (BOD5), chemical oxygen demand (COD) and nitrite -N (NO2), ammonia-N (NH3-N), nitrate-N (NO3) total phosphorus (TP), Kjeldahl- Nitrogen (organic N+ammonia-N) (TKN), total inorganic N (TI), total suspended solids (TSS) and total nitrogen (TN). The performance of the wastewater treatment plants examined was checked and the results were conclusive. In the future, solutions should be applied to these wastewater treatment plants and some recommendations are suggested.

This article describes the leadership and organizational elements that have made the Seven Mountains Mandate (7MM), a very adaptable, simple to explain, and feasible to implement message and portable practice, which can start at a local level to be expanded progressively to regional and national dimensions, one of the main doctrines of the Apostolic Restoration Movement (ARM). A new classification of apostolic networks is proposed in this article and a partial map of some of the networks that participate in the expansion of the 7MM is presented. Additionally, by using definitions from network leadership theory, it is shown how different types of leaders that function as horizontal connectors in apostolic networks, such as conveners, catalysts, mobilizers, facilitators, weavers, provocateurs, illuminators, hosts, and curators contribute to the diffusion of the 7MM by promoting its global widening, deepening, and lengthening (WDL). The article first describes the evolution of the church from denominations to networks, and how the ARM adopted the network organizational structure that serves as a channel for the diffusion of the 7MM. Cases from the USA, Guatemala, Zambia, and Venezuela are used as examples to demonstrate how the 7MM expansion or widening (W), its contextualization and adaption or deepening (D), and its sustainability, reproduction, and evolution, or lengthening (L), occur globally based on the network nature of the ARM and the network leadership enacted in the process.

The results of shaking table tests from the literature on a one-story, two-bay reinforced concrete frame experiencing both shear and axial failures are compared with nonlinear dynamic analyses using simplified models designed for assessing the collapse of older reinforced concrete structures. To simulate the nonlinear behavior of columns—both those prone to shear failure and those more flexure-dominant—the one-component beam model was utilized. This model consists of a linear elastic element linked in series to a rigid-plastic linear hardening spring at each end, representing a concentrated plasticity element. To capture strength degradation through path-dependent plasticity, a negative kinematic hardening model was used, connecting points at shear and axial failure, respectively. Shear failure points were determined via pushover analysis of the shear-critical columns using Phaethon software. While the simplified model yielded reasonable predictions of the overall frame response and lateral strength degradation, the reduced computational cost of the modeling approach led to some deviations between the calculated and measured shear forces and drifts during portions of the time-history response.

In pursuit of overcoming Fenton’s oxidation limitations in wastewater treatment, an introduction of heterogeneous photocatalyst is developed. In this regard, the current work is introducing ZnO nanocrystals that were successfully prepared by thermal decomposition technique and then capped with oleic acid (OA). The synthesized ZnO-OA and the pristine ZnO were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FE-SEM). Then, the study introduces the application of such materials in advanced oxidation processes, i.e. Fenton's reaction to treat dye-containing wastewater. Synthetic wastewater that prepared using Reactive Blue 4 (RB4) is used as a simulated textile wastewater effluent. Fenton’s oxidation is applied, and the system parameters were assessed using the modified Fenton’s system. The synthesized samples of ZnO are characterized by a recognized wurtzite hexagonal structure. Modifying the surface of ZnO with oleic acid (OA) led to an increase in crystallite size, lattice parameters, and cell volume. These modifications are linked to the efficient capping of ZnO nanoparticles by OA, which further improved the dispersion of the nanoparticles, as demonstrated by SEM imaging. The optimum conditions of ZnO and ZnO-OA synthesized modified Fenton composites showed 400, 40 mg/L for H2O2 and catalyst respectively, at pH 3.0 within 90 min under UV irradiation for maximal dye oxidation reached 93%. The catalytic performance at its optimal circumstances was in accordance with a pseudo-second-order kinetics model for both ZnO-OA and the pristine ZnO based Fenton’s systems. Also, the thermodynamic parameters were also checked and the values settled that both Fenton systems are spontaneous in nature, proceeds at a low energy barrier condition (10.38 and 31.38 kJ/mol for ZnO-OA and the pristine ZnO based Fenton reaction, respectively).

We lack a unified theory of aging that spans multiple scientific disciplines, including life history theory, evolution, cellular and molecular biology, and medicine. This gap has hindered both our ability to effectively target and treat age-related diseases in humans and our understanding of the vast variation in aging rates across species. This paper builds on the Blueprint Theory of Aging, which suggests that biological pathways can be ‘dis’-activated outside their intended context, leading to pathological pathways (patho-pathways) that drive cellular and tissue ‘dys’-function and age-related ‘dis’-eases, ultimately limiting both healthspan and lifespan. We propose that evolution operates through complex trade-offs within interconnected networks (network constraints), optimizing beneficial functions while minimizing the costs of misactivated patho-pathways. These optimizations are shaped by a species' environment and are driven by a central evolutionary dilemma: prioritize immediate reproduction or reduce current fecundity in favour of future reproductive success. Evolution adjusts network constraints along a continuum, balancing early-life reproduction with lifetime reproductive output. When environmental pressures result in lower juvenile survival than adult survival, the optimal strategy shifts toward maximizing total progeny over a lifetime. This strategy, in turn, determines the final trade-offs selected, the structure of network constraints, and ultimately, the rate of aging for that species. Understanding these evolutionary calculations not only explains species-specific aging patterns but also sheds light on the wide variability in aging rates across nature. Finally, we reveal hidden evolutionary connections between mechanisms such as Antagonistic Pleiotropy and Mutational Accumulation, deepening our insight into the evolutionary dynamics underlying aging.

Obesity is among the most prevalent risk factors in the severe forms of Coronavirus disease 2019 (COVID-19) infection. COVID-19 patients with obesity often face severe complications that might be associated with overexpression of adiponectin, inflammatory cytokines, and angi-otensin-converting enzyme 2 (ACE2) receptors in visceral fat. The pre-existing subclinical in-flammation associated with obesity can also lead to severe inflammatory responses. Elevation of pro-inflammatory cytokines considerably activates coagulation cascades, including the tissue factor (TF) pathway. The hypercoagulable state in COVID-19 is presented by severe pulmonary complications such as venous thromboembolism (VTE), disseminated intravascular coagulation (DIC), and disruption of vascular endothelial cells which can lead to severe complications and death. The interaction between inflammatory response and coagulation mechanism in COVID-19 patients with obesity warrants a further understanding of prognosis and potential therapeutic approaches. This review discussed the crosstalk between inflammation and coagulopathy in obesity-related severe COVID-19 infection.

Allogeneic hematopoietic stem cell (HSC) transplants are usually used in cases where patients have active cancerous illness in their bone marrow, but allogeneic transplants can cause graft vs. host disease. If autologous HSCs could be purged of cancer cells, they could then be used for transplantation purposes without concern. It is still controversial whether purging is necessary, but it certainly would alleviate concerns. A novel method of purging is discussed herein that does not require chemotherapy and is targeted for each patient. It could be applied either in vitro or in vivo.

Immune-mediated inflammatory diseases (IMIDs) are characterized by dysregulated immune responses and chronic tissue inflammation. In the setting of inflammatory bowel disease (IBD), dipeptidyl peptidase 4 (DPP4) and gut microorganisms have been proved to interplay, potentially influenced by dietary factors. This rapid review aimed to study the DPP4-gut microbiome link in IBD. A literature search across five databases and two other sources identified seven relevant studies reporting data on DPP4 and gut microbiome, in patients with IBD-related IMIDs or in vitro or in vivo models: one cross-sectional, one in vitro, and five in vivo studies. The findings revealed a significant impact of DPP4 and its substrates, i.e. glucagon-like pep-tide-1/2 (GLP-1/2), on the composition of gut microbiome and on the development of dysbiosis. Increased DPP4 activity is associated with decreased GLP-1/2, increased pathogenic bacteria from Actinobacteria, Bacteroidetes, Deferribacteres, Firmicutes, Fusobacteriota, Proteobacteria, and Verrucomicrobia phyla, and decreased -diversity of beneficial gut microbes, including Clostridiaceae, Lachnospiraceae, and Ruminococcaceae families and short-chain fatty ac-id-producing bacteria like Odoribacter and Butryvibrio spp., with exacerbation of intestinal in-flammation. This overview revealed that understanding the DPP4-gut microbiome association is critical for the development of DPP4-targeted therapeutic strategies to guarantee gut microbiome balance and modulation of immune response in IBD.

This research is part of the NeuroArtIfAct project []aimed at exploring the cognitive and emotional processes involved in perceiving Etruscan artifacts. This case study is focused on the Sarcophagus of the Spouses at the National Etruscan Museum in Rome. The study utilized AI and eye-tracking technology to analyze how viewers engage with the Etruscan Sarcophagus of the Spouses, revealing key patterns of visual attention and engagement. Open AI ChatGBT 4o was used in conjunction with Python in order to elaborate all the spreadsheets and data coming from the eye0tracking recording. Results showed that viewers primarily focused on the central figures, especially the faces and hands, indicating a high level of interest in the human elements of the artifact. The longer fixation durations on these features suggest that viewers find them particularly engaging, likely due to their detailed craftsmanship and symbolic significance. The eye-tracking data also highlighted specific gaze patterns, such as diagonal scanning across the sarcophagus, which reflects the composition's ability to guide viewer attention strategically. These findings suggest that the artifact's design effectively captures and directs viewer attention, emphasizing its cultural and emotional significance.

Healthcare is undergoing a technological transformation, particularly in robotic surgery and arti-ficial intelligence (AI) applied to cytology. These innovations are improving the precision and effi-ciency of both surgical procedures and laboratory diagnostics. Robotic systems, such as the Da Vinci, enable surgeons to perform complex procedures with enhanced control and precision, leading to minimally invasive surgeries with reduced recovery times. This technology has signifi-cantly impacted urological, cardiac, and gastrointestinal surgeries, and its use is expected to grow. In cytology, AI automates the analysis of cell samples, leading to faster and more accurate diag-noses, especially in detecting diseases like cervical cancer. AI reduces errors and enhances diag-nostic accuracy by identifying complex patterns in cellular images. Predictive analytics, driven by big data, also allows for personalized treatments based on patient-specific data. However, these technologies face challenges, including high costs, ethical concerns, data privacy issues, and resistance from healthcare professionals. Continuous training is essential for medical personnel to effectively adopt these tools. Furthermore, the rise of telemedicine and telesurgery, supported by advancements in 5G, offers new opportunities for remote care but also introduces risks, such as depersonalization of medical care and technological barriers. Ultimately, while robotic surgery and AI hold great promise for improving clinical outcomes, their successful integration will depend on addressing these challenges and ensuring that healthcare professionals are adequately prepared for the digital future.

Six up-to-date reconstructions of hemispheric and global temperature and two indices of volcanic activity were analyzed using both Fourier and wavelet approaches over time intervals up to 1500 years. A cyclicity with a period of 188-250 years was found present in both Northern Hemisphere temperature and volcanic activity. These cycles were found to be negatively correlated, with the maximum correlation coefficient being reached when volcanic variations lead temperature vari-ations by 20 years. The combined probability of the null hypothesis – the proposition that in the Northern Hemisphere there is no real association between the bicentennial change in volcanic activity and temperature and that the revealed correlations arose purely by chance – was found to be no more than 1.2×10-2 over the entire time interval and less than 10-2 over the time interval AD 1270-1980. The effect was weaker in global temperature and was not detected in the Southern Hemisphere. Possible origins of the identified bicentennial correlations are discussed and guidelines for further research are proposed.

The establishment of Ministry of Natural Resources and Ministry of Agriculture and Rural Affairs of China, marks integrated installation of centralized and unified supervision of agricultural natural resources assets. The value goal of comprehensive supervision of agricultural natural resource assets should include process-oriented, multi-participation, assets accretion and technological innovation. However, different supervision departments have different objectives and interest gambling. From the perspective of game equilibrium, effective balance between development and use and comprehensive supervision of agricultural natural resource assets, promote the reconstruction of structure of rights(powers), form a practical systematic selection. Take comprehensive supervision legislation as solution path, take central and local comprehensive cohesion as realization mechanism, highly integrated and coordinate in legislation, to implement institution allocation which efficient use of agricultural natural resources assets and rural green ecological environment protection are considered.

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

We have collected theoretical arguments supporting the functional role of nano-metallic coatings of solar cells, which enhance their efficiency via by plasmon strengthening of absorption of sun-light photons and reduction of binding energy of photo-excitons. The quantum character of plasmonic effect related with the absorption of photons (called as optical plasmonic effect) is described in terms of Fermi golden rule for quantum transitions of semiconductor band electrons induced by plasmons from a nano-metallic coating. The plasmonic effect related with the lowering of exciton binding energy (called as electrical plasmonic effect) is also quantumly characterized with particular significance for metalized perovskite solar cells. Quantum coupling between plasmons in nanoparticles from a coating with band electrons in a semiconductor substrate significantly modifies material properties (dielectric functions) both of particles and of a semiconductor, beyond the ability of a classical electrodynamics description.

Objective. This study aimed to determine the correlation of colour M-mode velocity propagation in the assessment of left ventricular diastolic function among acute decompensated heart failure patients with preserved ejection fraction (HFpEF). Materials and Methods. This research is an observational analytical study using a cross–sectional study design conducted from April to June 2022. The subjects of this study were patients diagnosed with acute HFpEF and treated at Dr. Soetomo General Academic Hospital, Surabaya. Patients with heart rhythm disturbances, moderate to severe valvular heart disease, and congenital heart defects were excluded from this study. Patients were measured for diastolic function including mitral valve early mitral inflow velocity (MV E Velocity), mitral peak velocity of early filling (E’) (E' Septal and E' Lateral), left atrial volume index (LAVI), The maximal tricuspid regurgitation velocity (TR V Max), Vp, the ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/E'), the ratio between early and late ventricular filling velocity (E/A) and E/Vp Ratio. N-terminal-pro hormone B-type natriuretic peptide (NT–ProBNP) examination was also performed on study subjects as an additional diagnosis of acute heart failure. Results. There were 33 patients who met the inclusion and exclusion criteria and were included in our study. There were 26 patients (78.8%) with grade I diastolic dysfunction, 4 patients (12.1%) with grade II diastolic dysfunction, and 3 patients (9.1%) with grade III diastolic dysfunction. There is a strong positive correlation between Vp with parameters E' Septal (p = &lt; 0.001; r = 0.636) and E' Lateral (p = &lt; 0.001; r = 0.650). Vp is negatively correlated with the ratio E/E' (p = 0.029; r = –0.37). The PCWP estimate with E/Vp ratio had a strong correlation with the NT–Pro BNP value &gt; 300 pg/ml in acute HFpEF patients (p = &lt; 0.001; r = 0.726) compared to the PCWP estimate with E/E' (p = &lt; 0.001; r = 0.549). Conclusion. There is no correlation between Vp value and degree of diastolic dysfunction in patients with acute HFpEF.

Artificial intelligence (AI) shows great potential in medical diagnosis and drug analysis. Through deep learning technology, AI can automatically extract features from large amounts of complex medical data, significantly improving diagnostic accuracy and drug development efficiency. Especially in drug target discovery and antiviral peptide classification, AI technology can accelerate data processing and prediction, helping researchers identify potential therapeutic molecules more quickly and optimize the drug development process. This study proposes and validates a Deep learn-based model, deep-Avpiden, to improve the classification and discovery efficiency of antiviral peptides (AVPs). By using sequential convolutional networks (TCNs), the model outperforms traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in capturing long-term dependencies and parallel computing capabilities. In terms of dataset, we used AVPs and non-AVPS samples from multiple databases, totaling 5,414 cleaned and de-weighted peptide sequences for model training after data preprocessing and embedding. The Deep-AVPiden model has been shown to outperform existing advanced classifiers in experiments, and its effectiveness has been verified by accuracy, precision, recall rate, and area under the ROC curve (OC-ROC). In addition, to accommodate computing resource constraints, we propose an optimized version of Deep-AvPIDen (DS), which utilizes Deep separation convolution technology to significantly reduce computing resource consumption. Through the online application platform, researchers can efficiently classify antiviral proteins and discover new AVPs. Future research could further optimize the model's computational efficiency, handle larger data sets, and expand its potential for biomedical problems such as drug combination prediction and new drug discovery.

The Internet of Things (IoT), as a technology, transforms every day, consumer devices into devices capable of collecting and transmitting data. This momentous progress highlights the need for reliable risk management frameworks that address the potential risks associated with IoT devices across all aspects of life. Traditionally, the feasibility of IoT was limited by the high costs of sensors and their connectivity. Although, recent advancements have greatly reduced costs, enabling widespread connectivity of devices to the Internet. Consequently, numerous IoT devices and networks are left vulnerable without a comprehensive framework for managing these risks effectively. This paper introduces a more systematic framework designed to counter security risks and safeguard IoT devices. This framework takes a step-by-step approach for focusing on mitigating risks arising from inadequate security designs for IoT devices. It involves procedures for risk identification, evaluation, and prioritization which is followed by comprehensive risk analysis and control measures, and because risk evaluation is tedious, we suggest using machine learning (ML) to accelerate the risk evaluation process, boost the precision and consistency of risk assessments, and provide valuable insight, which ultimately enables risk analysts to make better informed and timely decisions. Through multiple case studies, we demonstrate the practicality and efficacy of the framework for evaluating IoT device risks and guiding the implementation of appropriate controls to safeguard devices and networks. We also develop security dashboards that provide visual summaries comparing device values, threat assessments, and risk mitigation costs, aiding in clear communication and prioritization of security measures.

Agroclimatic indicators help convey information about climate variability and change in terms that are meaningful to the agricultural sector. This study evaluated climate projections for Angola, particularly for provinces with higher agrarian potential. To this end, a set of 15 predefined agroclimatic indicators in 2041–2070 and 2071–2099, under the anthropogenic forcing scenarios RCP4 and RCP8, were compared with the historical period 1981–2010 as a baseline. Data were extracted within the geographic window of longitudes 10–24°E and latitudes 4–18°S and from five climate model chain experiments, namely MIROC-ESM-CHEM, HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M and NorESM1-M. The averages of the set of agroclimatic indicators, as well as their differences between historical and future periods, are discussed concerning the likely implications for agriculture in Angola. The results show significant increases in average daily maximum (2–3°C) and minimum (2–3°C) temperatures in Angola. For the future, a generally significant reduction in precipitation (and its associated indicators) is expected in all areas of Angola, with the southwest region (Namibe and Huíla) recording the most pronounced decrease, up to 300 mm, while the maximum number of consecutive dry days will increase across the country and especially in the northeast of the country. A widespread increase in temperatures is expected, which could lead to hot and dry conditions in Angola that could lead to more frequent, intense and prolonged extreme events, such as tropical nights, maximum number of consecutive summer days, warm and wet days and Warm spell duration index periods. These changes can seriously affect agriculture, water resources and ecosystems in Angola, thus requiring adaptation strategies to reduce risks and adverse effects, while ensuring the sustainability of the country's natural resources and guaranteeing its food security.

This study presents the development of a Recurrent Neural Network (RNN) model for classifying smart home device data. Utilizing a dataset sourced from Kaggle, the study explored the processes of data gathering, loading, normalization, and model construction. The RNN, equipped with Long Short-Term Memory (LSTM), was trained and evaluated, demonstrating significant improvements in training and validation accuracy over 10 epochs, culminating in a test accuracy of 83.25%, and only 35.4% loss. The evaluation of the model on the test set provides a test accuracy result, accompanied by an in-depth analysis of ROC curves and Area Under the Curve (AUC) scores for multi-class classification, along with a confusion matrix. The AUC score of 0.9896 indicates outstanding performance in accurately classifying IoT device categories. These findings indicate that the RNN with LSTM exhibits superior learning efficiency and generalization capabilities, making it more suitable for IoT device classification tasks. This article emphasizes the concept of IoT and reviews recent studies on the application of deep learning models across various IoT domains, including smart homes, industrial systems, and healthcare. Future research could focus on improving real-time processing capabilities, and scalability, and incorporating diverse IoT data types to enhance and broaden the model's practical applications.

A longstanding debate is raised about how far does the credit sector promote local investment and whether banking sector is efficacious in the meaning it helps respond to investors financing requirements and whether it serves the conduct of monetary policy in its conventional formulation. There are much concerns also about the alternatives of monetary policy conduct when the interest rate channel transmission mechanism does no longer work in terms of effectiveness of monetary policy and how to step aside credit supply insufficiencies and disruptions in order to respond to excess credit demand. This article provides an assessment of the relevance of unconventional monetary policy to deal with the issue of the vicious role played by the credit sector in its failure to fulfill banking sector efficacy due to an excessive search for yield motivation and extreme awareness from systemic risk. We run an ARDL and Bound test to the data collected on the Tunisian banking sector and show that Banking sector efficacy is affected in the short run by Fisherian sensitivity another expression of the impact of inflation on real activity and banking sector specificities and in the long run it is affected by the impact of inflation on real activity and business cycle effects expressed in terms of sensitivities of Credit supply and demand to output gap. Banking specificities do no longer matter in the long run and banking sector efficacy is not at all affected by Monetary policy in the short run because of nominal rigidities and in the long run because of monopolistic competition and backward oriented expectations that diverts the pass through effect of monetary policy as the pricing of loans is exclusively tributary on the search for yield motivation and the agency costs and result in a non competitive price of risk premiums that do not translate the transmission of monetary policy. We conclude that the behavior of the banking sector is vicious because it conveys importance exclusively to the search for yield motivation and profitability of the banking sector and to the mitigation of systemic risk and does not play any role in the promotion of local investment which is a pillar of economic performance and growth. We propose the Credit easing as an unconventional monetary policy that can step aside the hindrance of an inelastic credit supply through modifying asset prices and thereby improving attractiveness of assets to the banking sector and forcing financing through credit allocation but warn from the difficulties that might result from unconventional monetary policy in terms of inability to withdraw from the perturbation and gauge discretionary policy accurately.

The article addresses the issues of scalability and security in blockchain networks, with a focus on sharding algorithms and decentralized data storage. Key challenges include the low throughput and high transaction latency in public networks such as Bitcoin and Ethereum. Sharding is examined as a method to enhance performance through data distribution, but it raises concerns regarding node management and reliability. Sharding schemes, such as Elastico, OmniLedger, Pyramid, RepChain, and SSchain, are analyzed, each presenting its own advantages and drawbacks. Alternative architectures like Directed Acyclic Graphs (DAG) demonstrate potential for improved scalability but require further refinement to ensure decentralization and security. Protocols such as Brokerchain, Meepo, AHL, Benzene, and CycLedger offer unique approaches to addressing performance and transaction consistency issues. The article emphasizes the need for a comprehensive approach, including dynamic sharding, multi-level consensus, and inter-shard coordination. Additionally, a conceptual model is proposed that incorporates sharding of transactions, states, and networks, which enables greater scalability and efficiency.

Background/Objectives: Obesity and nonalcoholic fatty liver disease (NAFLD) reduce life expectancy; nonoperative interventions show poor results. Individually, chitosan, acetic acid (AA), and sepiolite clay attenuate high-fat diet-induced obesity (DIO) via reduced energy digestibility and increased energy expenditure. We hypothesized that a chitosan–sepiolite biocomposite suspended in AA would attenuate DIO and NAFLD to a greater extent than AA alone via its more substantial adsorption of nonpolar molecules; Methods: We tested this dietary supplement in C57BL/6J mice fed a high-fat diet, compared to supplementation of a bile-acid sequestrant (cholestyramine) and standalone AA; Results: Biocomposite supplementation attenuated DIO rate and NAFLD progression, whereas standalone AA showed mild attenuation of DIO gain and did not prevent HFD-induced hepatic fat accumulation. Biocomposite intake was accompanied by a decreasing digestibility trend counterbalanced by increased intake; hence, it did not affect energy absorption; Conclusions: Therefore, DIO attenuation was suggested to be related to higher energy expenditure, a phenomenon not found with AA alone, as supported by calculated energy expenditure using the energy-balance method. These results support further investigation of the biocomposite’s efficacy in attenuating obesity and NAFLD, specifically when applied with a restricted diet. Future studies are needed to determine this biocomposite's safety, mechanism of action, and efficacy compared to its components given separately or combined with other ingredients.