The safety of vegetable food is compromised by various factors, including the inefficient or excessive use of sanitizers. Instances of individuals falling ill after consuming raw vegetables have been reported, with outbreaks of diseases caused by pathogens on fresh vegetables becoming increasingly prevalent globally, attracting significant media coverage and impacting the economic viability of vegetable cultivation. Measures to enhance food safety in postharvest horticultural produce involve controlling microbial proliferation and minimizing cross-contamination. Sanitizers were utilized in the food safety arsenal for a variety of purposes, including pathogen elimination and microbe reduction, hand, tools, and vegetable contact surface cleaning, and produce shelf life extension. Choosing an appropriate sanitizer for all vegetables is difficult due to a lack of knowledge on which sanitizers are ideal for the many types of vegetables grown on farms under different environmental circumstances. Although chlorine-based sanitizers, such as sodium or calcium hypochlorite, have been widely used for the past 50 years, recent research has revealed that chlorine reacts with an organic compound in fresh vegetables to produce trihalomethane, a carcinogen precursor, and as a result, many countries have prohibited the use of chlorine in all foods. As a result, horticulture research groups worldwide are exploring for non-chlorine, ecologically friendly sanitizers for the vegetable industry. They also want to understand more about present procedures in the vegetable business for employing alternative sanitizers, as well as the efficacy and potential dangers to the food safety of fresh salad vegetables. This review-cum-research paper presented detailed information on non-chlorine sanitizers, such as their efficacy, benefits, drawbacks, regulatory requirements, and the need for additional research to lower the risk of marketed salad vegetable food safety.

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges, and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody-couplers or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment further standardization efforts are required. The study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs, and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

Fowlpox virus (FPV) has been used as a vector for many years. To date, surprisingly, very little data exist on the persistence of fowlpox vaccines in chicken tissues, or what kind of immune cells respond to the vaccination at the site of inoculation. Although both humoral and cellular mediated immunity (CMI) play a part in overall immunity against FPV, little is known regarding the cell-mediated immune responses to FPV infection. The main aim of this paper was to measure persistence of the recombinant fowlpox vaccine in the skin tissues following vaccination. The results showed that the recombinant FPVs do not persist for long, and are cleared by 6 days post-vaccination (dpv). The response of immune cells (macrophages, B cells, CD4+ and CD8+ T cells) that infiltrating the site of vaccination was estimated.

Background/Objectives: PEMF therapy has gained significant attention due to its potential benefits in promoting tissue repair and improving overall well-being. Recent publications in the field of exercise physiology, showed that pulsed electromagnetic fields (PEMFs) enhanced the velocity and the quantity of muscle O2 available and the rate of muscle oxygen extraction and utilization, in semi-professional cyclists, during a heavy constant-load exercise. In this paper, we investigate the effect of stimulation on muscular activation, in sedentary young people, during a constant-load exercise, performed at moderate intensity. Methods: Nine male sedentary young people, partic-ipated in this study. The response of the muscle activity, was recorded by surface EMG and assessed measuring the root mean square (RMS) normalized to the peak of the maximum voluntary con-traction. Data were collected in the right vastus Medialis (RVM) and right Biceps Femoris (RBF) at the baseline (standstill sitting), on warm-up (unloaded cycling) and during 30 minutes of con-stant-load exercise, in two experimental conditions (PEMF ON vs PEMF OFF). Results: The mul-tivariate analysis of variance performed on RVM showed a significant effect of PEMF in both warmup and exercise, while on RBF showed a significant effect of PEMF and warmup. Conclusions: PEMF strongly enhanced the amplitude of muscle activity in young sedentary people. The increase in the amplitude of the muscular response may lead to a higher oxygen consumption and release.

Comprehensive understanding of tree characteristics and conditions holds paramount importance for the precise management of hazelnut orchards. It facilitates the determination of tree vigor, pruning requirements, phytosanitary interventions, and plant water consumption. The primary objective of this study was to explore, for the first time on a fruit tree with a bushy structure and across trees of four Italian distinct hazelnut cultivars, the efficacy of multispectral and thermal UAV (Unmanned Aerial Vehicle) technologies in assessing canopy attributes, vegetative growth, and predicting abiotic stresses. These technologies serve as tools for precision agriculture, enabling the computation of various indices such as the Normalized Difference Vegetation Index (NDVI) and crop water stress index (CWSI). The study of water content is of particular importance at this time, especially considering the increasing water stress levels in Europe as well as globally. While Red Green Blue (RGB) and thermal imagery collectively demonstrated superior performance in model reconstruction, the multispectral UAV remained more adept at characterizing size traits of hazelnut plants. Thermal images alone proved inadequate for accurately reconstructing hazelnut biometric characteristics. Furthermore, all indices were found to be cultivar-specific, underscoring the importance of conducting studies across different cultivars. The utilization of two UAVs, namely multispectral and thermal, facilitated the examination of the relationship between NDVI and CWSI across tree species.

Cryptococcus neoformans is a facultative intracellular fungal pathogen. Ten-generation-old (10GEN) C. neoformans cells are more resistant to phagocytosis and killing by macrophages than younger daughter cells. However, mechanisms that mediate this resistance and intracellular parasitism are poorly understood. Here, we identified factors important for the intracellular survival of 10GEN C. neoformans, such as urease activity, capsule synthesis, and DNA content using flow cytometry and fluorescent microscopy techniques. Real-time visualization of time-lapse imaging was applied to determine the phagosomal acidity, membrane permeability, and vomocytosis (non-lytic exocytosis) rate in J774 macrophages that phagocytosed C. neoformans of different generational ages. Our results showed that old C. neoformans exhibited higher urease activity, and enhanced capsule formation, which was supported by an increased Golgi complex. In addition, old C. neoformans were more likely to be arrested in the G2 phase, resulting in the occasional formation of aberrant trimera-like cells. To finish, the advanced generational age of the yeast cells slightly reduced vomocytosis events within host cells, which might be associated with increased phagolysosomal pH and membrane permeability. Altogether our results contribute to a better understanding of the strategies used by old C. neoformans to resist phagosomal killing and suggest that old generation C. neoformans intracellular behavior may be driving cryptococcosis pathogenesis.

To explore the relevant factors that affect the efficacy of consciousness-regaining therapy (CRT)for prolonged disorder of consciousness(pDOC),a retrospective analysis was conducted on the case data of 114 patients with pDOC admitted to department of functional neurosurgery of Tianjin Huanhu Hospital from January 2019 to January 2022. Basic information on the cases,data on pDOC disease assessment, consciousness-regaining therapy methods, and efficacy evaluation were collected. A total of 114 patients were grouped and compared based on the efficacy at the end of treatment. Among them, there were 61 cases in the ineffective group and 53 cases in the effective group. There was no statistically significant difference (P>0.05) between the two groups in terms of gender, age, etiology, acute cerebral herniation, emergency craniotomy surgery, emergency decompressive craniectomy, time from onset to start of consciousness-regaining therapy , and duration of consciousness-regaining therapy(P>0.05). However, there were statistically significant differences (P<0.05) in terms of secondary hydrocephalus, , consciousness-regaining therapy methods, CRS-R grading before treatment, and GOSE score at 6 months after treatment. Binary logistic regression analysis showed that the type of therapy (OR=0.169, 95% CI: 0.057-0.508) was a related factor affecting the efficacy of consciousness-regaining therapy (P<0.05).Compared with non-invasive therapy, personalized awakening therapy using various invasive consciousness-regaining therapy methods can help improve the efficacy of therapy for pDOC.

Metabarcoding of prokaryotic and fungal ribosomal DNA was used to describe the microbial communities in soils of remnant equatorial rainforest, maize-bean intercrop, and sugarcane in western Kenya. Cropping systems influenced the microbial community composition and functional traits (energy source and nutrient cycling) of bulk soil in each crop. Microbial richness and diversity tended to increase with cultivation intensity. Soil of maize-bean intercrop had lower percentages and sugarcane had higher percentages of unique amplicon sequence variants of both bacteria and fungi compared to the remnant forest. Functional traits were altered by cultivation intensity. Compared to remnant forest soils, maize-bean intercrop soil had lower percentages of aerobic chemoheterotrophic bacteria and higher percentages of N-cycling bacteria, while sugarcane had higher percentages of aerobic chemoheterotrophic bacteria and lower percentages of N-cycling bacteria. In the face of increasing forest loss and pressures for agricultural productivity, this landscape provides a rich site for studying the impacts of cropping systems on soil health.

Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in cattle raised in North America. At the feedlot, cattle are subject to metaphylactic treatment with macrolides to prevent BRD, a practice that may promote antimicrobial resistance and has resulted in an urgent need for novel strategies. Mannheimia haemolytica is one of the major bacterial agents of BRD. The inhibitory effects of two amphipathic, α-helical (PRW4, WRL3) and one β-sheet (WK2) antimicrobial peptides were evaluated against multidrug-resistant (MDR) M. haemolytica isolated from Alberta feedlots. WK2 was not cytotoxic against bovine turbinate (BT) cells by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. All three peptides inhibited M. haemolytica, with WK2, being the most efficacious against multiple isolates. At 8-16 µg/ml, WK2 was bactericidal against Mh 330 in broth and at 32 µg/ml in the presence of BT cells, reduced the population by 3 logs CFU/ml without causing cytotoxic effects. The membrane integrity of Mh 330 was examined using NPN (1-N-phenylnaphthylamine), and ONPG (o-Nitrophenyl β-D-galactopyranoside) with both the inner and outer membranes being compromised. Thus, WK2 may be a viable alternative to the use of macrolides as part of BRD prevention and treatment strategies.

This study aimed to investigate the appearance and frequencies of the booroola polymorphism of the bone morphogenetic protein receptor 1b (BMPR1B) gene (FecB) and the embrapa polymorphism of the growth differentiation factor 9 (GDF9) gene (FecGE) in sheep in Thailand. A total of 454 cross-bred sheep blood samples were collected from four provinces in Thailand during August 2022 to July 2023. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to identify the FecB and FecGE genotype. The history of ewe birth types was collected from the owners to analyze the association between Fecundity (Fec) genotypes and the history of birth types. The genotypic frequencies of FecB for homozygous genotype (B/B), heterozygous genotype (+/B) and wild-type (+/+) were 0.22%, 1.54%, and 98.24%, respectively. Meanwhile, the genotypic frequencies of FecGE for homozygous genotype (E/E), heterozygous genotype (+/E), and wildtype (+/+) were 0.00%, 2.42%, and 97.58%, respectively. Furthermore, three ewes exhibited both FecB and FecGE genotypes. Fisher’s exact test revealed that the possessor of FecB genotype was associated with multiple births (p<0.01). Both FecB and FecGE mutations were identified in crossbred sheep in Thailand. Sheep containing FecB allele is a better candidate to be selected to improve the prolificacy of crossbred sheep in Thailand.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

Curcumin, a hydrophobic polyphenol compound derived from the rhizome of the Curcuma genus, has a wide spectrum of biological and pharmacological applications. Previously, curcumin nanoparticles with different stabilizers had been produced successfully in order to enhance solubility and per oral absorption. In the present study, we tested the anti-inflammatory effect of D-α-Tocopheryl polyethylene glycol 1000 succinate (TPGS)-stabilized curcumin nanoparticles in vivo. Lambda-carrageenan (λ-carrageenan) was used to induce inflammation in rats; it was given by an intraplantar route and intrapelurally through surgery in the pleurisy test. In the λ-carrageenan-induced edema model, TPGS-stabilized curcumin nanoparticles were given orally one hour before induction and at 0.5, 4.5, and 8.5 h after induction with two different doses (1.8 and 0.9 mg/kg body weight (BW)). Sodium diclofenac with a dose of 4.5 mg/kg BW was used as a standard drug. A physical mixture of curcumin-TPGS was also used as a comparison with a higher dose of 60 mg/kg BW. The anti-inflammatory effect was assessed on the edema in the carrageenan-induced paw edema model and by the volume of exudate as well as the number of leukocytes reduced in the pleurisy test. TPGS-stabilized curcumin nanoparticles with lower doses showed better anti-inflammatory effects, indicating the greater absorption capability through the gastrointestinal tract.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

Blazars show rapid and high amplitude variability. In this work , Fermi daily light curve of 130 sources are analyzed, distribution of daily variability are compared with each other by using Kolmogorov-Smirnov (K-S) test. results show: 1)Some pairs of the distributions are similar; 2) In most cases, the distributions are not Gaussian.

In this work, we'll present the power spectra density, auto-correlation function, dentrended fluctuation analysis and multifractal spectrum are performed to the daily light curves of Blazars monitored by Fermi LAT. Some signs of power law are found.

In this paper, we propose the definition of block tensors and the real representation of tensors. Equipped with the simplification method, i.e., the real representation along with the M-P inverse, we demonstrate the conditions that are necessary and sufficient for the system of dual split quaternion tensor equations (A*NX,X*SC)=(B,D) when its solution exists. Furthermore, the general expression of the solution is also provided when the solution of the system exists and we use a numerical example to validate it in the last section. To the best of our knowledge, it is the first time that the aforementioned tensor system has been examined on dual split quaternion algebra. Additionally, we provide its equivalent conditions when its Hermitian solution X=X* and η-Hermitian solutions X=Xη* exist. Subsequently, we discuss two special dual split quaternion tensor equations. Last but not least, we propose an application for encrypting and decrypting two color videos and we validate this algorithm through a specific example.

With the development of the times, the objective requirement of CIM is to establish a 3D digital city. This article proposes a software front-end and back-end open source solution for the production and release of 3D modeling in different industries. This article is based on the construction practice of CIM basic platform database, and analyzes the spatial resolution and corresponding entity expression effect of 3D models in CIM basic platform from the perspective of CIM industry application, as well as the advantages and disadvantages of application. It also elaborates on the classification of 3D models and the geographic elements and buildings they contain, and introduces the relationship between 3D models and vector data, raster data, attribute data, as well as the difficulties of modeling technology. On this basis, large-scale 3D modeling and mapping experiments were conducted in the power industry, water industry, construction industry, and indoormap scene. The article has improved and optimized the algorithms for 3D excavation volume and wire azimuth, and introduced various parametric modeling techniques. From the perspective of project application and user feedback, the modeling quality, performance, and user experience have been recognized by the market. This article has certain reference value for the construction of the 3D model basic framework, theme oriented model system, modeling technology selection, and the release of massive 3D data services in the CIM basic platform construction.

Background: Orthognathic surgery is a surgical procedure that consists of osteotomies and maxillary movements aimed at facial harmony and occlusal repositioning. Methods: The present study aimed to perform a retrospective study using cone beam computed tomography (CBCT) to evaluate the air volume of the maxillary sinus and its attached structures, detection and diagnosis of anatomical defects, induced or not by maxillary advancement surgery - mandibular and/or maxillary impaction in two stages: preoperative (T0) and postoperative of at least 6 months (T1). Results: Of the 31 patients selected, it was found that the volume of the maxillary sinus had no statistically significant change, descriptively there were 6.45% for retention cyst and 32.25% for mucosal thickening, reduction in 6 cases of septal deviation in T1 that were present in T0 in patients undergoing maxillary advancement and impaction. Furthermore, it was possible to observe a decrease in postoperative cases of obstructed ostium and an increase in cases of patent ostium. Conclusion: That orthognathic surgery for maxillary advancement and/or impaction does not pose a risk to the health of the maxillary sinuses during the period analyzed.

The traditional method for determining the Marshall stability (MS) and Marshall flow (MF) of asphalt pavements is laborious, time consuming, and costly. This study aims to predict these parameters using explainable machine-learning techniques. A comprehensive database comprising 721 hot mix asphalt (HMA) data points was established, including variables such as aggregate percentage, asphalt content, and specific gravity. Models were constructed using the PyCaret Python library, and their performance was assessed using metrics such as the mean absolute error (MAE) and coefficient of determination (R²). The CatBoost regression model outperformed the other models, achieving R² values of 0.835 and 0.845 for MS and MF, respectively. Additionally, Shapley values were used to quantify the variable effects on the predictions. This approach enables the efficient preselection of design variables, reducing the need for extensive laboratory testing and promoting sustainable construction practices.

Lung cancer treatment has long been a major field of study in research due to the com-plexity and variability of cancer. Because drug resistance is a major hurdle for lung cancer treat-ment, in this article, we will review the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) used for the treatment of Non Small Cell Lung Cancer (NSCLC) and the EGFR mutations that cause resistance to them. Then, we will discuss the applications of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) genome ed-iting in medical treatment. The proposed treatment is specifically for NSCLC patients who have acquired resistance to EGFR TKIs due to mutations to EGFR such as T790M and C797S and would make these TKIs become effective once again. Currently, there is no treatment targeting C797S on the market, but fourth-generation EGFR-TKIs are currently in trial [1,2]. (Figure 1) This proposal will review the potential steps and options available as well as some challenges that may complicate the process.

This paper discusses an improved XGBoost model based on four-vector optimization algorithm to improve the accuracy of loan approval prediction. Through the analysis of correlation heat maps, we found that there were significant positive and negative correlations among some variables, which laid the foundation for subsequent machine learning analysis. Based on this, we compare the traditional machine learning algorithm with the improved model to evaluate its performance in loan approval forecasting. In the confusion matrix analysis of the training set, the improved XGBoost model demonstrated excellent performance, with all loan approval predictions being correct with 100% accuracy. However, the performance in the test set was slightly different, with 1,182 projects receiving correct loan approval predictions and 99 projects forecasting errors. Of these, 26 projects that should have been predicted to be "unapproved" were incorrectly labeled as "approved," while 73 projects that should have been predicted to be "approved" were incorrectly labeled as "unapproved." These results suggest that we still need to pay attention to the misjudgment of the model in practical application. By synthesizing all model evaluation indicators, we found that the improved XGBoost model based on the four-vector optimization algorithm has a higher accuracy in loan approval prediction than the traditional XGBoost model, with an increase of 1.5%. In addition, the other evaluation indicators also show a trend of significantly better than the traditional model. This study shows that the four-vector optimization algorithm can effectively improve the application effect of XGBoost model in the field of loan approval, and provide more accurate data support and decision-making basis for the financial industry. In the future, we will continue to explore the potential and application prospects of this algorithm in other fields.

The utilization of sustainable biomaterials in pharmaceutical and medical applications has garnered significant attention in recent years due to their potential to address environmental concerns while meeting the demands of modern healthcare. This review provides a comprehensive overview of the latest advancements and applications of sustainable biomaterials in the pharmaceutical and medical fields. Beginning with an exploration of sustainable sourcing methods and production techniques, the review highlights the importance of eco-friendly practices in biomaterial development. Subsequently, it delves into the diverse range of sustainable biomaterials, including biopolymers, natural extracts, and bio-based nanoparticles, elucidating their properties and biomedical applications. Moreover, the chapter discusses the role of sustainable biomaterials in drug delivery systems, tissue engineering, wound healing, and medical device fabrication, emphasizing their biocompatibility, biodegradability, and therapeutic efficacy. Along with discussions on regulatory issues and commercialization prospects, the potential and challenges related to the integration of sustainable biomaterials into pharmaceutical and medical practices are also critically reviewed. By elucidating the advancements and potential of sustainable biomaterials, this article aims to inspire further research and innovation in the pursuit of environmentally friendly and effective solutions for healthcare applications.

As sequencing technologies advance, short DNA sequence fragments increasingly serve as DNA barcodes for species identification. Rapid acquisition of DNA sequences from diverse organisms is now possible, highlighting the increasing significance of DNA sequence analysis tools in species identification. This study introduces a new approach for species classification with DNA barcodes based on an ensemble of deep neural networks (DNN). Several techniques are proposed and empirically evaluated for converting raw DNA sequence data into images fed into the DNNs. The best-performing approach is obtained by representing each pair of DNA bases with the value of a related physicochemical property. By utilizing different physicochemical properties, we can create an ensemble of networks. Our proposed ensemble obtains state-of-the-art performance on both simulated and real data sets. The code of the proposed approach is available at https://github.com/LorisNanni/AI-powered-Biodiversity-Assessment-Species-Classification-via-DNA-Barcoding-and-Deep-Learning .

The comorbidity of osteoarthritis and type 2 diabetes mellitus represents a complex clinical challenge and complicates patient management due to the interplay of overlapping pathophysiological mechanisms. The research aimed to analyze the exacerbation of clinical symptoms and biochemical markers in patients with osteoarthritis and type 2 diabetes mellitus compared to those with osteoarthritis. We utilized various assessment methods to measure the patient cohorts' inflammation, oxidative stress, and glycemic control. The study also involved the administration of alpha-lipoic acid to patients with comorbid osteoarthritis and type 2 diabetes mellitus and monitored its effects on joint function, inflammatory markers, oxidative stress levels, and glycemic control. Our findings revealed that the presence of type 2 diabetes mellitus significantly intensified osteoarthritis symptoms and altered biochemical markers, including increased inflammation, elevated oxidative stress, and disrupted immunological balance. The treatment with alpha-lipoic acid resulted in improved joint function, reduced inflammation and oxidative stress levels, and better glycemic control. Additionally, it was identified several potential predictors for developing osteoarthritis and type 2 diabetes mellitus comorbidity. The results underscore the detrimental impact of type 2 diabetes mellitus on osteoarthritis and highlight the beneficial effects of alpha-lipoic acid in alleviating some of the adverse consequences associated with this comorbidity. Nonetheless, further research is necessary to comprehensively evaluate the therapeutic efficacy of alpha-lipoic acid and its long-term effects on managing comorbidity of osteoarthritis and type 2 diabetes mellitus.

The dynamic response of the shed-tunnel structure under rockfall impact attracts much attention in recent years. However, the research on the dynamic response of the shed-tunnel structure under rockfall impact mostly focuses on the rockfall single impact, while in practical engineering, the shed-tunnel structure often encounters the rockfall repeated impacts during its service life. Therefore, this research focuses on exploring the dynamic response characteristics of the shed-tunnel structure under the rockfall repeated impacts. First of all, based on the model test of the shed-tunnel under rockfall impact as a reference, the FEM (Finite Element Method)-SPH (Smoothed Particle Hydrodynamics) coupling numerical calculation model is established based on ANSYS/LS-DYNA finite element code. The numerical simulation of the dynamic response of the shed-tunnel structure under rockfall impact is realized, and the rationality of the model is verified. Then, with this model and the full restart technology of LS-DYNA code, the effects of four factors, e.g. rockfall mass, rockfall impact velocity, rockfall impact angle and rockfall shape, on the impact force and impact depth of the buffer layer, the maximum plastic strain and axial force of the rebar, the shed roof vertical displacement and the shed-tunnel plastic strain are studied. These results show that the cumulative deformation and damage of the shed-tunnel structure caused by the rockfall repeated impacts play an important role in the destruction of the shed-tunnel structure, and the special attention should be paid to this issue.

Ming-style round-backed armchairs as a classic Chinese chair design are renowned for the blend of ergonomics, practicality, and elegance. Although the Chinese armchair furniture industry has made strides in sustainability, there is still a need for further improvements in material selection, manufacturing processes, and supply chain management to fully support the development of sustainable furniture. Laminated bamboo lumber as new sustainable materials are becoming a popular option for furniture designers in the era of "sustainable design". This research aims to investigate the feasibility and application of laminated bamboo lumber in the design of Chinese armchair furniture and proposes innovative ideas for optimizing the armchair's design. First, an empirical study was conducted to comprehensively understand the classification of Ming-style round-backed armchairs’ structure to develop a 3D model. Secondly, an experimental research method was then employed to explore the parameters of laminated bamboo lumber for future studies. Additionally, a validation experiment was conducted to compare real-world scenarios with simulations using finite element analysis in Ansys. Finally, finite element analysis was used to test the feasibility and application of laminated bamboo lumber in furniture design, focusing on the material’s mechanical properties. Results indicated that laminated bamboo lumber has excellent mechanical properties as furniture design materials, subsequently, using the optimized results, the use of 0.6319 kg of steel was successfully reduced by 23.68% to ensure the armchair’s stability. This provides a reference for the future compter-assist and standardized innovative design of Chinese armchair. By incorporating compter-assist design and lightweight optimization, it is possible to save materials and make more efficient use of resources, thereby contributing to sustainable development goals in the field of furniture design.

Background: Alzheimer's disease (AD) is a leading cause of dementia, significantly influenced by the Apolipoprotein E4 (APOE4) gene and gender. This study aims to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 AD patients. We applied three ML regression models—XGBoost, Random Forest (RF), and Linear Regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, Brain Age Difference (BAD) and Integrated Difference (ID), to evaluate model performance and analyze the influence of APOE4 genotype and gender on brain aging. Results: AD patients displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing APOE4 carriers with non-carriers, the models showed enhanced ID values and consistent brain age predictions, improving overall performance. Gender-specific analyses indicated slight enhancements, with models performing equally well on both genders. It indicates that APOE4 may be a more robust predictor of brain age than gender. Conclusion: Robust ML models for brain age prediction can be pivotal in the early detection of AD risk via MRI brain structural imaging, especially for APOE4 carriers. Such early identification may facilitate timely preventive interventions for AD.

In recent years, Ethiopia's number of returnee entrepreneurs has increased dramatically, significantly contributing to personal financial achievement and the nation's economic development and transformation. However, their businesses are typically smaller and less profitable than non-migrant entrepreneurs. This study explores factors affecting the success of these returnee migrant entrepreneurs, including individual characteristics, entrepreneurial skills, and experience, financial resources, as well as the mediating role of government policies and institutional support. Data was collected from 390 returnee migrants who started their businesses and analyzed using a robust theoretical foundation and PLS-SEM to understand the impact of success factors. The results show that entrepreneurial skills, experience, and financial resources positively and significantly influence the success of returnee migrant entrepreneurs. Furthermore, social networks, while not directly significantly influencing the entrepreneurial success of returnee migrants are mediated by government policies and institutional support. Professional guidance and counseling provided by experts and mentors of the accelerator program were found to be essential for entrepreneurial success but did not improve the effectiveness of the policy. Financial resources, although crucial, did not significantly impact policy efficacy. These findings underscore the need for tailored policy interventions addressing the challenges faced by returnee migrants, including economic self-sufficiency, socio-psychological support, and effective social networks to boost the success of returnee migrants. Experienced returnee entrepreneurs play a key role in developing a vigorous and supportive entrepreneurial ecosystem through community engagement and policy advocacy. Additionally, personal traits are crucial for entrepreneurial success, while practical experience influences government policies for substantiated policy development in returnee migrant entrepreneurship and its benefits.

Introduction. Anesthesia is widely used in various fields of dentistry to reduce patient discomfort when the treatment would be painful. The dentist may need to infiltrate the anesthetic into multiple locations and this is usually done with the same needle which passes through the soft tissues several times to distribute the anesthetic solution over a larger area. The present study aims to evaluate, through a morphological analysis under a scanning electron microscope (SEM), how the beveled tip of a 30G needle, used to inject anesthetic several times into different areas of the vestibular mucosa, changes. Materials and methods. 30G needles with two different brands were tested, in vivo used for a variable number of sequential injections from 1 to 6 on 192 patients. The specimens were analyzed under SEM observation to evaluate any deformations induced by contact with the vestibular soft tissues. Results. The amount of deformation, measured along the axis parallel to the needle, was proportional to the number of injections. The protrusion of deformations, measured on an axis perpendicular to the needle, were maximum on the needles used 3 times. For the needles used 4,5 and 6 injections, no further increase in deformations was noted. Conclusion. From the data it seems that the tested needles are not produced to withstand multiple injections consequently after the first injection on soft tissues, they undergo a deformation which could increase the risk of injury to the mucosa itself or to nerves and vessels with which they come into contact. It would be advisable to use the single needle for a reduced number of injections.

Pregnancy-related morbidity and mortality remains high across the United States and the majority of deaths are preventable. Misdiagnosis and delay in diagnosis are thought to be contributors to preventable harm. These diagnostic errors in obstetrics are understudied. We present selected research methods to ascertain rates of and harm associated with diagnostic errors, the challenges in investigating them, and present future steps toward achieving diagnostic excellence in obstetrics.

Current trends in systemic risk literature provide new insights into multi-hazard risks that interact and compound with built environments, creating more significant impacts on socio-economic and human systems. Recurrent natural hazards and extreme weather events are more likely to compound and cascade into more impactful events, especially in vulnerable societies. In small-island communities (SICS), including large archipelagos and Small Island Developing States (SIDS), the long-term impact of multi-, compounding-, and cascading-hazards (MCC hazards) can result in persistent vulnerability and residual risks. This is due to delayed responses, limited resources, poverty, fewer evacuation options, and inadequate markets and infrastructure. Using Ternate, a densely populated small volcanic island in North Maluku, Indonesia, as a case study, this paper assesses the impacts of seven types of natural hazards: flash floods, landslides, extreme weather, extreme waves and abrasion, earthquakes, tsunamis, and volcanic eruptions. The research focuses on the multi-hazard impact on population, land use, and infrastructure exposures in 60 villages in Ternate. The findings highlight population density, land use, and infrastructure exposure to multi-hazard risks, providing valuable information on potential losses in future hazard events.

Lung cancer remains a pervasive global health challenge, necessitating precise and efficient classification methods to inform optimal treatment strategies. In this study, we investigate the potential of machine learning algorithms in developing a computer-aided diagnostic tool for early and accurate lung cancer classification, leveraging essential protein biomarkers as predictive features. Our investigation reveals the superiority of Deep Neural Networks (DNNs) in lung cancer classification, achieving an impressive accuracy of 96.91%. This highlights the transformative potential of DNNs in facilitating real-world clinical applications, offering clinicians a powerful tool for accurate diagnosis and prognosis. Additionally, we analyze the performance of Ensemble Methods, including Voting (91.75%) and Bagging (93.81%), as viable alternatives to DNNs. These ensemble techniques demonstrate robust performance, further underlining their utility in lung cancer classification tasks. Furthermore, our study underscores the critical role of hyperparameter tuning, particularly in adjusting parameters such as min child weights and learning rates, in mitigating overfitting and enhancing the generalizability of diverse machine learning models. For instance, Support Vector Machines (SVM) exhibit varying accuracies ranging from 74.23% to 95.88% across different hyperparameter configurations, emphasizing the importance of hyperparameter optimization in maximizing model performance. In summary, our comprehensive comparative analysis sheds light on the efficacy of different machine learning approaches in lung cancer classification. By leveraging advanced algorithms and optimizing model parameters, researchers and clinicians can harness the power of machine learning to advance early detection and personalized treatment strategies for lung cancer patients.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

Driven by the importance of early detection for improving patient outcomes, this study investigates the effectiveness of various machine learning algorithms in predicting lung cancer based on clinical data. Lung cancer remains a significant global health concern, and this research seeks to contribute to advancements in early diagnosis through the application of machine learning techniques. The dataset consists of 385 training and 97 test samples, with a k-fold cross-validation approach (k=5) utilized to mitigate overfitting. Additionally, the ADASYN oversampling technique is employed to address class imbalance. Our analysis includes traditional algorithms such as Logistic Regression and Decision Trees, as well as ensemble methods like XGBoost, LGBM, and AdaBoost. Furthermore, we explore the effectiveness of deep learning models, particularly Deep Neural Networks (DNN). Results demonstrate promising accuracy rates across all models, with some outperforming others in terms of precision, recall, and F1-score. Our findings reveal that LGBM achieves the highest accuracy among all models, with an accuracy rate of 96.91%. It also exhibits superior performance in terms of precision, recall, and F1-score, particularly in classifying both positive and negative instances of lung cancer.

While there are well-known synthetic methods in the literature to find the image of a point under circular inversion in l2−normed geometry (Euclidean geometry), there is no similar synthetic method in Minkowski geometry, also known as the geometry of finite-dimensional Banach spaces. In this study, we have succeeded in giving a synthetic construction for the circular inversion in l1−normed spaces, which is one of the most fundamental examples of Minkowski geometry. Moreover, this synthetic construction has been given using the Euclidean circle, independently of the l1−norm.

Scientific studies on the impact of wood species on solid fuel production, performance, and sustainability are grossly inadequate. The knowledge of this is imperative as users of solid fuels are increasing rapidly, especially in Africa. On this note, it becomes necessary to explore measures that will improve its efficiency and sustainability as an energy source. This study investigates some properties of selected wood species used as an energy source in Nigeria and their pelleting potential. Nine (9) samples were characterized and assessed for suitability of pelleting following four wood pellet quality standards. The findings revealed Anogeissus leiocarpus as the species with the best rating score in terms of fuel properties while Vitellaria paradoxa as the lowest. However, in terms of conformity with the four wood pellets standards, Khaya senegalensis, Parkia biglobosa and Anogeissus leiocarpus having presented density, calorific value, sulfur, arsenic, cadmium, and lead within the limits of the wood pellet quality standards were considered the best wood species in terms of fuel suitability and pelleting potential. The findings therefore suggest that not all wood species are suitable as fuel. Thus, for species that do not meet the standard wood pellet requirements, alternatives such as the use of biomass blends, additives, or process adjustments can be employed to adapt the quality to the standards or by using the fuels in improved cookstoves.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

The carbohydrate antigen Sda is expressed on cells and secretions of the vast majority of Caucasians. The epitope is formed by a terminal GalNAc residue β4-linked to a α3-sialylated galactose. Different carbohydrate chains N- or O-linked to glycoproteins can be terminated by this epitope. The final step of Sda biosynthesis is catalyzed by the GalNAc transferase B4GALNT2. In this review, we will discuss the multifaceted aspects of B4GALNT2/Sda biology. In particular, we will see its implications in fertility and pregnancy, in susceptibility to infectious diseases, in chronic kidney diseases and in Duchene muscular dystrophy. In particular, we will show its regulation and prognostic implications in cancer.

Urban lakes, especially those of natural origin, provide ecosystem services, recreation being one of the most important and highly valued by the city dwellers. Fulfilling the needs of city residents to relax and have contact with nature has become a priority in urbanized areas and has been proved to positively affect people’s health and well-being. The recreational potential of water bodies was identified to be most important aspect of ecosystem services to the residents of the neighboring areas. An assessment of recreational ecosystem services (RES) provisioning to society based on the real time spent by the citizens and housing values in the rural-urban gradient revealed that the economic benefits of lakes differ in urbanized, suburban and rural landscapes. The growth of cities has led to an increased population density in the surroundings of ecologically valuable areas, resulting in higher pressure from visitors seeking recreational areas. Along with urbanization, the impoverishment of ecosystem functions takes place, limiting their capability to provide ecosystem services. In this work provisioning of recreational ecosystem services of 28 floodplain lakes located along the urban-rural gradient of the Warsaw agglomeration was assessed. The relationship between the ecological value of the water bodies, measured using naturalness indices, and the recreational ecosystem services they can provide was assessed. The results showed that the floodplain lakes located along the urban-rural gradient are of a great importance to the citizens due to their recreational potential. The provisioning of recreational ecosystem services is poorly connected with the ecological characteristics of the floodplain lakes. Only hemeroby, was significantly correlated with provisioning, and there was no relationship with factors such as naturalness of vegetation or water quality, demonstrating that public preference was not generally influenced by high ecological quality. These data should be available to potential buyers and be integrated in spatial planning management plans in order to shape future housing policy.

Blockchain technology presents significant advancements in demand-side management (DSM) by facilitating decentralized, transparent, and secure transactions within energy systems. This study investigates a novel blockchain-based approach aimed at enhancing electricity efficiency through optimized DSM strategies. We propose an innovative framework that integrates blockchain technology with real-time data analytics to optimize energy consumption, encourage consumer participation, and promote efficient energy utilization. Our experimental analysis reveals that the integration of blockchain enhances DSM by achieving a 25% reduction in electricity costs for consumers, a 30% increase in demand response participation rate, and a 15% improvement in overall grid stability compared to traditional DSM approaches. These findings underscore the effectiveness of our proposed method, showcasing its originality and potential for future energy management solutions.

The increasing demands on global resources due to technological development driven by consumer expectations and demands have resulted in significant problems with ecological sustainability and material availability. The creation of biocomposites has resulted in notable advancements in the green industry within the materials science area this century, owing to concerns regarding sustainability and the environment. Globally, there is a surge in the creation of highly efficient materials derived from natural resources. In aviation applications, plant-fiber-supported polymer composite materials are becoming increasingly popular. Aerospace materials are typically used in aircraft construction as structural materials to support loads throughout different flight phases. There are many diverse mechanical qualities of natural fibres; therefore, selecting one for the interior parts of an aircraft cabin based only on its attributes leads to a multiple-attribute decision-support issue. In this paper, the effective natural fibre and polymer choice for use as reinforcing materials in composite materials is represented for the composite materials’ improvement to aircraft cabin luggage for aerospace implementations. This study can guide material designers in investigating different hybrid materials with the most effective natural fiber and polymer obtained by hierarchical strategy by elucidating the effective material choice to meet the criteria determined for the aircraft cabin luggage. For this purpose, the definitive rankings of the twelve polymers and sixteen natural fibers in terms of performance score were assessed using hierarchical strategy methodology.

This research article investigates the application of artificial intelligence (AI) in human resource management (HRM) within the Australian commercial media sector, addressing a critical gap in scholarly knowledge. Despite increasing interest in AI’s potential, there is a lack of consensus among managers and HR professionals regarding its application. Through qualitative interviews with 22 industry professionals, this study examines perceptions, understanding, and the potential for leveraging AI across key HRM functions. Findings indicate significant potential for AI to enhance HRM processes, particularly in job/work design and staffing. This research contributes to a deeper understanding of AI’s role in HRM and offers insights to optimize organizational efficiency and effectiveness in the Australian commercial media sector.

Research on high-volume fly ash concrete, which utilizes large quantities of fly ash, has been actively conducted to promote resource recycling and reduce carbon dioxide emissions. However, while cement mixed with fly ash exhibits different temperature sensitivities compared to traditional cement, its hydration and mechanical properties vary depending on curing conditions. Despite this, there is a significant gap in research on this topic. In this study, high-volume fly ash concrete was produced with fly ash replacement rates of 35 % and 55 %. Compression strength tests were performed at various curing ages under three different curing temperature conditions, and the results were analyzed. Additionally, an isothermal calorimeter was used to analyze hydration characteristics under different curing conditions. The results indicated that concrete with high fly ash content was more sensitive to curing temperature compared to ordinary Portland cement.

This scoping review explores the intersection of Extended Mind Theory and Extended Reality (XR) technologies, focusing on how Virtual Reality, Augmented Reality, and Mixed Reality reshape human cognition and interaction. XR enables users to offload cognitive tasks and engage in embodied experiences, extending cognition beyond the brain into digital environments. The review highlights a wide range of XR applications, from immersive learning in STEM education and medical training, neuropsychological assessment to therapeutic interventions, arts and entertainment, professional skills development, retail and e-commerce, remote work, sports training, architecture and urban planning, and cultural heritage preservation. XR's integration with modalities like haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces further enhances cognitive extension and user engagement. However, alongside these advancements come significant ethical, psychological, and societal challenges, such as data privacy concerns, the psychological effects of prolonged immersion, and social inequality arising from disparate access to XR technologies. This review emphasizes the need for robust ethical frameworks that address these challenges, ensuring that XR technologies enhance human development while maintaining autonomy, privacy, and mental well-being. As XR continues to evolve and integrate with artificial intelligence and other emerging technologies, its role in expanding human cognition will depend on responsible implementation and governance.

To be CMOS-compatible, a low preparation temperature (< 500 °C) of ferroelectric films is required. In this study, BiFeO3 films were successfully fabricated at a low annealing temperature (< 450 °C) on aluminum foils by a metal-organic decomposition process. The annealing atmosphere on performance of BiFeO3 films was assessed at 440 ± 5℃. By replacing an O2-rich atmosphere with an N2-rich one, a better crystalline morphology was achieved in BiFeO3 films, resulting in better electric properties targeted for high-density ferroelectric random-access memory applications. These properties included a large remnant polarization (Pr~78.1 μC/cm2 @ 1165.2 kV/cm) and a high rectangularity (~91.3% @ 1165.2 kV/cm) of the P-E loop, along with excellent charge-retaining ability up to 1.0 × 103 s and a fatigue resistance reaching 1.0 × 109 switching cycles. The enhanced performance of BiFeO3 film in an N2-rich atmosphere maybe originated from the slightly high content of oxygen vacancies during the annealing process. Furthermore, decent electrical properties (Pr~70 μC/cm2 @ 1118.1 kV/cm) of the BiFeO3 films were achieved at a very low annealing temperature of 365 ±5 °C on aluminum foil substrates. These results offer a new idea for lowering the annealing temperature for integrated ferroelectrics in high-density FeRAM applications.

Background. The possible interactions between anger and fear have not been widely explored in the psychological literature. Fear and anger are currently beginning to be studied by looking at their interrelationships, rather than seeing them as simply opposing emotions. Furthermore, there is a tendency to think that anger is more typical of men and fear of women. Our contribution proposes a particular perspective of affective neuroscience. The objectives of the study are: 1) To assess possible differences in affective systems, states, and traits of anger in relation to biological sex; 2) To assess correlations between ANGER, FEAR, and PANIC, state and trait anger in both a female and male sample; 3) To assess possible differences in basic affective systems in relation to different levels of ANGER, FEAR, and PANIC, state and trait anger, in sample of female and in sample of male. Methods. A non-clinical sample of 339 females and 99 males completed the ANPS 3.1 to assess basic affective states and the STAXI 2 to assess anger states and traits. Results. No significant differences were found for ANGER and FEAR scores and for state and trait scores between the male and female samples. Clear correlations emerged (p < .01) between FEAR. PANIC and FEAR in both the female and male samples. Among the differences that emerged in the affective systems, we emphasise that in the female group as FEAR increases, there are significant variations in systems of SEEKING, PLAY, and CARE. Furthermore, only in the female sample as PANIC increases, PLAY progressively decreases, and CARE shows an irregular trend. Discussion. Given the importance of the SEEKING and PLAY systems for well-being and gender equality the hypoactivation of these systems to increases in FEAR and PANIC highlights the need to monitor conditions that may trigger these affective systems.

Fixed-time artificial insemination is an important technique in swine production that can improve reproductive efficiency and meat production quality by making better use of the genetic potential of breeding males and reducing the costs associated with double or multiple inseminations. Our goal was to evaluate the vaginal application of the GnRH agonist triptorelin acetate to synchronize post-weaning sows and facilitate implantation of a single fixed-time insemination. In a first experiment, the efficacy of treatment with the triptorelin in animals with or without signs of estrus was analyzed using a single insemination at a fixed time compared to the control following the standard insemination strategy. Farrowing rate was comparable between triptorelin and control groups (100 vs. 87.50%), but triptorelin without estrus had a lower rate (50%). Litter size did not differ between groups. Estradiol and progesterone levels at 96- and 120-hours post-weaning were similar in the control and triptorelin groups (P>0.05). These results suggest that Triptorelin has the potential to synchronize ovulation in pigs without affecting post-weaning hormonal profiles. In a second experiment, the objective was to evaluate the productive and economic impact of implementing a treatment with triptorelin acetate 96 hours after weaning compared to the standard insemination protocol. Sows were grouped according to treatment (control vs. triptorelin) and estrus onset (≤5 days and >5 days after weaning considered as late estrus). Farrowing rate was lower in the control late estrus group than in the control and triptorelin groups and similar to the triptorelin late estrus group. No differences were found for litter size, live or dead piglets born (P>0.05). We developed an estimation model to assess the cost/benefit of intravaginal triptorelin administration at 96 h post-weaning. The overall result is that the use of triptorelin increases the benefit per inseminated sow in the range of €15-20. This improvement is mainly related to an increase in the reproductive performance of the treated sows compared to the control sows and the reduction in the number of inseminations per sow. These results highlight the potential of triptorelin to optimize reproductive management in pigs, improving efficiency and economic viability.

In this paper, the results of a high-precision control survey carried out in the underground mining of the Oyu Tolgoi project using terrestrial laser scanning technology are presented. The purpose of the measurements was to ensure that the direction, total length and volume of the penetration and excavation works were being implemented in accordance with the planned and designed dimensions. The coordinates of the geodetic traverse checkpoints were also assessed. In addition, we checked a traverse network of 18 checkpoints in the tunnel of the selected underground level. These checkpoints were repeatedly measured four times by two using readings of the Trimble S7 1” total station and were processed using the Compass/Bowditch method with Trimble Business Center software. All points except for one were within the tolerance limits. Based on these points, the excavation length and volumes were determined using the terrestrial laser scanners (TLSs) Trimble X7 and Trimble SX12 [25]. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured using laser scanner was calculated with the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed a length of 20285.72m and a volume of 441128.80m3 for the seven underground mine levels, while their project values were 16158.26m length and 422837.50m3 volume.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

In this very personal hunt for the meaning of the bacterial cell cycle, the snark, I briefly revisit and update some of the mechanisms we and many others have proposed to regulate the bacterial cell cycle. These mechanisms, which include the dynamics of calcium, membranes, hyperstructures and networks, are based on physical and physico-chemical concepts such as ion condensation, phase transition, crowding, liquid crystal immiscibility, collective vibrational modes, reptation, and water availability. I draw on ideas from subjects such as the ‘prebiotic ecology’ and phenotypic diversity to help with the hunt. Given the fundamental nature of the snark, I would expect that its capture would make sense of other parts of biology. The route therefore followed by the hunt has involved trying to answer questions like “why do cells replicate their DNA?”, “why is DNA replication semi-conservative?”, “why is DNA a double helix?”, “why do cells divide?”, “is cell division a spandrel?” and “how are catabolism and anabolism balanced?”. Here, I propose some relatively unexplored, experimental approaches to testing snark-related hypotheses and, finally, I propose some possibly original ideas about DNA packing, about phase separations, and about computing with populations of virtual bacteria.

Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this distinctive power-law statistical property through the lens of agent heterogeneity in trading activity and asset possession. Our study introduces a novel modeling approach that combines fractional Lévy stable motion for limit order inflow with this power-law distribution for cancellation times, significantly enhancing the prediction of order imbalances. This model not only addresses gaps in current financial market modeling but also extends to broader contexts such as opinion dynamics in social systems, capturing the finite lifespan of opinions. Characterized by stationary increments and a departure from self-similarity, our model provides a unique framework for exploring long-range dependencies in time series. This work paves the way for more precise financial market analyses and offers new insights into the dynamic nature of opinion formation in social systems.

The cause of the systematic daily march of barometric pressure in the tropics, notably the late morning and late evening peaks seen almost every day at all locations, was a puzzle that persisted through the nineteenth and much of the twentieth centuries. The efforts to explain the physics of the prominent 12-hour solar tidal variation helped inspire some of the earliest developments in theoretical atmospheric dynamics and ultimately led in the 1960’s to a satisfactory dynamical theory for the atmospheric tides. These important theoretical developments followed the observational discoveries, which date to the late 18th and early 19th centuries, of the surprising character of the barometric daily march and of its resolution into solar and lunar period cycles. These important, if simple, discoveries emerged primarily from the efforts of European scientists to systematically study the environment in remote areas of the globe. The two key figures in initially advancing the scientific community’s understanding of the character of barometric tides were the great German polymath Alexander von Humboldt (1769-1859) and the French naturalist Robert de Lamanon (1752-1787), who each made their discoveries on their most famous and colorful scientific expeditions of their respective careers. This paper examines the history of the early observations of the barometric tide.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

One of the oldest problems in physics is that of calculating the motion of N particles under a specified mutual force: the N-body problem. Much is known about this problem if the specified force is non-relativistic gravity, and considerable progress has been made by considering the problem in one spatial dimension. Here I review what is known about the relativistic gravitational N-body problem. Reduction to one spatial dimension has the feature that gravitational radiation is absent, thereby allowing for a clear comparison between the physics of one-dimensional relativistic and non-relativistic self-gravitating systems. After describing how to obtain a relativistic theory of gravity coupled to N point particles, I discuss in turn the 2-body, 3-body, 4-body, and N-body problems. Quite general exact solutions can be obtained for the 2-body problem, unlike the situation in general relativity in 3 spatial dimensions for which only highly specified solutions exist. The 3-body problem exhibits mild forms of chaos, and provides one of the first theoretical settings in which relativistic chaos can be studied. For N4 other interesting features emerge. Relativistic self-gravitating systems have a number of interesting problems awaiting further investigation, providing us with a new frontier for exploring relativistic many-body systems.

This review is dedicated to sustainable practices in liquid chromatography. HPLC and UHPLC methods contribute significantly to routine analytical techniques. Therefore, the transfer of classical liquid chromatographic methods into sustainable ones is of utmost importance in moving toward sustainable development goals. Among other principles to render a liquid chromatographic method green, substitution of the organic solvent component in the mobile phase with a greener one received great attention. This review concentrates on choosing the best alternative green organic solvent to replace the classical solvent in the mobile phase for easy rapid transfer to a more sustainable normal phase or reversed-phase liquid chromatography. The main focus of this review will be on describing the transfer of non-green to green and white chromatographic methods in an effort to elevate the sustainability best practice in analytical chemistry. The greenness properties and greenness ranking in addition to the chromatographic suitability of seventeen organic solvents for liquid chromatography are mentioned to have a clear insight into the issue of rapidly choosing the appropriate solvent to transfer a classical HPLC or UHPLC method into a more sustainable one. A simple guide is proposed for making the liquid chromatographic method more sustainable.

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. It is pertinent to continue the refinement and discussion of nutrient utilization and requirements for the purposes of enhancing the efficiency of production systems, maximizing resource economy, and minimizing the carbon footprint, among others. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter technique and minimum endogenous loss method. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies had been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size; with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that the guidelines of the major feeding systems for the entire growth phase may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that that metabolizable protein synthesis in the rumen can be predicted from energy intake using combined equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. Species, level of Intake, genotype, mature size, age, gender, body composition, use of tissue energy, activity, season, and acclimatization continue to be important considerations for the estimation of nutrient requirements in sheep and goats, with emerging factors such as climate change, heat stress, parasitism and secondary plant compounds. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential and limitation of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement.

Entanglement is one of the most striking features in quantum systems. Today, quantum entanglement plays a vital role in quantum technology, the non-classical correlation of quantum entanglement is an important resource for quantum communication and quantum computing. Entanglement can be divided into two categories: between multi-particle systems and between various degrees of freedom in a single-particle system (intraparticle entangled states). This work first analogized the distinctions and connections between two kinds of entangled states. Then it designed three types of entanglement-swapping methods based on Bell state joint measurement, cross-Kerr medium, and linear optical elements for intraparticle entangled states by analogizing the entanglement swapping between multiple particles. Intraparticle entanglement swapping can be applied in super quantum dense encoding, quantum information transmission, and quantum teleportation. This work is beneficial for promoting the development of quantum information technologies.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Ten new Schiff bases, including thiocarbohydrazone derivatives (1–10), were synthesized using an efficient, uncomplicated, and environmentally friendly approach using the reaction of alkyl-substituted aldehydes. The thiocarbohydrazones that were obtained were derived from o-phthalaldehyde. The chemical structures of the compounds were identified by the utilization of UV-Vis spectroscopy, Fourier transform infrared (FT-IR), ¹H nuclear magnetic resonance (¹H NMR), ¹³C nuclear magnetic resonance (¹³C NMR), and elemental analysis methods. The thermal stability of thiocarbohydrazones was examined using thermogravimetric analysis (TGA-DSC). Investigations were conducted on urease inhibition tests, pharmacokinetic property research, and molecular docking. The collection of samples with the urease enzyme revealed significant molecular docking findings, with MolDock scores ranging from -153 to -189. The compounds have the potential to be effective drug candidates for treating certain diseases, as indicated by the consistent results of molecular docking, urease inhibition tests, and pharmacokinetic characteristics investigations.

Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start-stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for both cold and hot engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper.

This study aimed to investigate the efficacy of manual therapy and exercise versus synchronized telerehabilitation with self-manual therapy and exercise in treating Subacromial Pain Syndrome (SAPS). Sixty individuals diagnosed with SPS, aged 18-50 years, were randomly assigned to home exercise (HE), manual therapy (MT), and telerehabilitation (TR) groups. Treatment protocols were administered over 8 weeks and included specific exercises and therapy interventions. Out-come measures included Visual Pain Scale (VAS), shoulder range of motion (ROM) via gonio-metric measurements, Quick Disability Arm-Shoulder-Hand Problems Survey (Q-DASH), and patient satisfaction. Results revealed that both MT and TR groups exhibited reduced pain, in-creased ROM, lower Q-DASH scores, and higher patient satisfaction compared to the HE group. However, no significant differences were found between MT and TR groups in pain levels, ROM, Q-DASH scores, or patient satisfaction. The study concludes that both telerehabilitation and manual therapy effectively alleviate pain and are well-received by patients with SPS. Addition-ally, manual therapy demonstrates superiority in enhancing functional levels compared to exer-cise-based interventions. (Registration: NCT05200130)

Cars are a highly inefficient mode of urban transport with significant negative impacts. Electric vehicles make it possible to eliminate direct CO2 emissions (though the total over a vehicle’s entire life cycle greatly offsets the advantages) but leave unchanged – or even aggravate – all the other negative effects on the quality of urban environments. Three visions for urban transport were presented: automobility, multimodality and accessibility. The first dominates North American cities, the second European one. The anomalies of these two visions have been examined. The automobility cities cannot manage the congestion and the attempts are self- defeating. The anomalies of multimodality city are related to the public space consumption for mobility and the impossibility to plan spatial proximity. The sustainable vision, that resolve the anomalies, is represented by the accessibility city in the Triple Access System (TAS) version which, however, still has a long way to go to be accepted and implemented.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P&lt;0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

Sesamum indicum is considered an underutilized oil-bearing seed in the semi-arid regions of Ghana. Nonetheless, it is a promising source of food with both nutritional and therapeutic benefits. The aim of the present study was to evaluate the antioxidant properties S. indicum seeds using different extraction solvents. The seeds were obtained from the local farmers and prepared for analysis. The bioactive compounds present in the seeds were extracted using hexane, ethyl acetate, ethanol and water and their yields quantified. Total phenolic content (TPC), Condensed tannin content (TTC) and Total antioxidant capacity (TAC) , and DPPH radical scaveging assay were analyzed using standard methods. Antinutrients such as saponins, alkaloids, phytates and oxalates were also analyzed from the powdered seeds. Two chemometric methods; hierarchical cluster analysis (HCA) and Pearson correlation were employed to evaluate the interdependence of the various parameters to result in their antioxidant properties. The re-sults revealed that the solvents utilized had a significant impact on the extraction yield, phyto-chemical component concentration, and antioxidant activities. Hexane extracts of S. indicum seeds significantly exhibited the highest antioxidant activity (p &lt; 0.05). It was marked with the highest TAC value of 232.6 ± 6.267 mg/g AAE and a strong DPPH scavenging activity with an IC50 of 52.81 ± 2.30 µg/mL. Correlations (p &lt; 0.05) was established between TPC,CTC, TAC and DPPH radical scavenging activity) of the extracts. Antinutrients such as; phytate, oxalate, saponins and alkaloids were found to be 7.691 ± 0.8576, 1.501 ± 0.1375, 21.33 ± 4.619 and 317.33 ± 30.29 mg/g respectively. Data obtained suggest that S. indicum possess rich bioactive compounds that can be used in neutraceuticals and food products.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

Expansion tube is ideal energy absorber which dissipates kinetic energy through plastic deformation and friction. There is an urgent need to understand the influence of key parameter, e.g. semi angle, tube material, and friction coefficient, on the mechanical response and energy absorption characteristics of expansion tube. In the present work, the material properties of the tubes were tested under quasi-static loading condition, and the numerical simulations were carried out by using commercial software ABAQUS. Based on the validated finite element simulation, all the semi angle, tube material, and friction have significant effects on the energy absorption capacity of expansion tube. The expansion tube with high tensile stress of parent material have high energy absorption capacity, while the specific energy absorption is linear with the tensile stress/density of tube material. This work would give a guidance to the structural design and parent materials selection for expansion tubes.

Euler’s product formula over the primes and Euler’s zeta function equate to enshrine the Fundamental Theorem of Arithmetic that every integer > 1 is the product of a unique set of primes. The product formula has no zero, and with a domain ≤1 Euler’s zeta diverges. Dirichlet’s eta function η(s), negates alternate terms of zeta, permitting convergence when s∈C and Re(s) < 1, and its non-trivial zeros {ρ}, have a deep relationship with the distribution of the primes. The Riemann Hypothesis is that all the non-trivial zeros have Re(ρ) = 1/2. This work examines the symmetries in a partial Euler’s zeta series with a complex domain equating it to the difference between two finite vector series whose matched terms have mirror-image arguments, but whose magnitudes differ when Re(s) ≠ 1/2. Analytical continuation generates a modified eta series η_l(s), in which every l^th term is multiplied by (1-l). If the integer l is appropriately determined by the Im(s), similar paired finite vector series have a difference that closely follows η_l (s) and their terminal vectors intersect in a unique way permitting zeros only when Re(s) = 1/2. Furthermore, those vectors tracking the derivatives of the series, have a special relationship permitting zeros of the differential only when Re(s) > 1/2.

The philosophy of testing statistical hypothesis is a natural consequence and functional extension of mathematical analysis of Probability. Along with the concept of recurrence when applied to random sequences and functions, it leads to the analysis of a priori and posterior which implies testing statistical hypothesis. Testing statistical hypothesis also involves algebraic, functional and dimensional considerations, which are found in the works of Laplace. Aspects of mathematical analysis such as universality of solutions, Laws of Large Numbers, Entropy, Information, and various functional dependencies are the main factors explained in the five properties that lead to implication of testing statistical hypothesis. Various interesting examples with modern scientific significance from genetics, astrophysics, and other areas give methodological access to answers of different problems and phenomena which are involved in the logic of testing statistical hypothesis.

Bacterial infection of the lower respiratory tract frequently occurs in mechanically ventilated pa-tients and may develop into life-threatening conditions. Yet existing diagnostic methods have moderate sensitivity and specificity, which results in the overuse of broad-spectrum antibiotics administrated prophylactically. This study aims to evaluate the suitability of volatile bacterial me-tabolites for breath-based test diagnosing Ventilation-Associated Pneumonia (VAP). The in vitro experiments with pathogenic microorganisms most prevalent in VAP patients (i.e., Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa) were performed to identify bacteria-derived metabolites using specially designed cultivation system enabling headspace sam-pling for GC-MS analysis. Thirty-nine compounds were found to be significantly metabolized by tested species and, therefore, selected for monitoring in the exhaled breath of critically ill, me-chanically ventilated (MV) patients. The emission of volatiles from medical respiratory devices was investigated to estimate the risk of spoiling breath results with exogenous pollutants. Bacterial metabolites were then evaluated to differentiate VAP patients from non-infected MV controls using receiver operating characteristic (ROC) analysis, with AUC, sensitivity, and specificity calculated. Nine bacterial metabolites that passed verification through a non-parametric ANOVA test for significance and LASSO penalization were identified as key discriminators between VAP and non-VAP patients. The diagnostic model achieved an AUC of 0.893, with sensitivity and specificity values of 87% and 82.4%, respectively, being competitive with traditional methods. Further vali-dation could solidify its clinical utility in critical care settings.

Background: Two explored EDA1 variants regulate the biological behaviors of human dental pulp stem cells (hDPSCs), such as proliferation and migration, as well as their effects on odontogenic differentiation, providing a theoretical basis for further exploration of the mechanisms underlying EDA1-induced tooth development. Methods: The experimental groups included a wild-type EDA1 group (Wt), non-syndromic tooth agenesis variants EDA1 group (NSTA-A259E, NSTA-S374R), syndrome type variant EDA1 group (STA-H252L), and an empty vector control group (PCR3/NC). CCK-8 method was used to investigate the proliferative activity of hDPSCs. The cell cycle distribution of hDPSCs was detected by flow cytometry; The cell migration ability was assessed using the cell scratch and Transwell tests; Odontogenic differentiation medium was used to induce mineralization of hDPSCs, followed by statistical analysis. RNA-Seq revealed that FOS, JUN and other genes were found to be enriched in MAPK signaling pathway and osteoclast differentiation pathway. qPCR was used to detect mRNA expression of hub genes (FOS and JUN). Results: Compared with the NSTA-EDA1 and STA-EDA1, Wt-EDA1 promotes the proliferation and transformation of hDPSCs from the G0/G1 phase to S phase (p<0.01), with no significant difference between NSTA-EDA1 and STA-EDA1 (p>0.05). Wt-EDA1 also upregulated hDPSCs cell migration compared to NSTA-EDA1 and STA-EDA variants, as demonstrated by migration assays (p<0.01). NSTA-EDA1 and STA-EDA1 variants inhibited the odontogenic differentiation of hDPSCs compared to the Wt group, as shown by Alizarin red staining and ALP staining (p<0.01). RNA sequencing revealed that the differentially expressed genes FOS and JUN were enriched in the MAPK signaling pathway and osteoclast differentiation. Compared with Wt-EDA1, EDA1 variants lost the ability to promote c-FOS mRNA expression, as confirmed by qPCR. Conclusions: EDA1 variants fail to the promote c-FOS mRNA expression in hDPSCs, thereby inhibiting odontogenic differentiation and losing the ability to enhance the proliferation of hDPSCs.

Introduction: The COVID-19 pandemic challenged people with spinal cord injury (SCI) regarding a variety of mental and physical issues. New challenges may arise as the effects of the pandemic continue. The objective of the present study was to explore the social participation of Canadians with SCI during the second wave of COVID-19. Methods: Participants with SCI from two Canadian provinces (Quebec and British Columbia) were interviewed. Results: Eighteen participants completed interviews. Facilitators of social participation remain similar since the first wave of COVID-19, such as the use of technology, help received by relatives and the use of delivery services to obtain groceries and other essentials. Obstacles to mobility due to winter conditions and lack of considerations related to COVID-19 public health measures specific to wheelchair users were also discussed by participants. Conclusions: People with SCI perceived participation restrictions, little changes in life habits, and uncertainty about the future during the second wave of COVID-19. The unique living conditions of people with SCI, ability to adapt life habits, and the lived experiences of people with SCI may have contributed to an overall resilience during the pandemic. Adaptive families, social contacts and technology made a difference during the pandemic.

High Education Institutions (HEI) play a key role in fostering sustainability since students will be future active society members. By providing them with the knowledge, skills, and mindset needed to address environmental challenges, they can contribute to increase sustainability in a long-term approach. With the effects of climate change strongly affecting water availability across planet, it is increasingly relevant to understand if higher education students are aware of this situation, to develop adequate measures to promote sustainable behaviors regarding water uses. This study aimed to assess students’ perceptions regarding water efficiency through a questionnaire submitted in 7 HEI in Portugal. Students recognized the importance of water with older students, women and science students demonstrating a greater awareness. Inspite of this 27% students do not know what the average water consumption in Portugal is and only 18% accurately reported the consumption range in Portugal. There is also a lack of knowledge (&gt;50% students) if common water efficiency measures are applied in their HEI. The main information sources identified by the students were “social communication” and “internet/social media”, revealing that water efficiency knowledge transfer in high education can be improved.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring SARS-CoV-2 prevalence in a city in Northern Italy from October 2021 to May 2023. Starting from a widely applied deterministic model, this study proposed a variation to take into account population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated with healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred in the considered period, demonstrating the model’s capacity to predict relevant fluctuations in the number of cases. Population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied for comparing virus load values across cities with varying population demographics and sewer network structures, enhancing the comparability of WBE data for effective surveillance and intervention strategies.

Chlamydia trachomatis (Ct) infections are the most common sexually-transmitted infection (STI). Despite effective antibiotics for Ct, undetected infections or delayed treatment can lead to infertility, ectopic pregnancies, and chronic pelvic pain. Besides humans, chlamydia poses similar health challenges in animals such as C. suis (Cs) in pigs. Based on the similarities between humans and pigs as well as their chlamydia species, we use pigs as a large biomedical animal model for chlamydia research. In this study, we use the pig model to develop a vaccine candidate against Ct. The vaccine candidate consists of TriAdj-adjuvanted chlamydial protease-like activity factor (CPAF) protein. We tested two weekly administration options – twice intranasal (IN) followed by twice intramuscular (IM), or twice IM followed by twice IN. We assessed the humoral immune response not only in serum by CPAF-specific IgG including antibody avidity determination but also in cervical and rectal swabs by CPAF-specific IgG and IgA ELISAs. The systemic T-cell response was analyzed upon in vitro CPAF-restimulation via IFN-γ and IL-17 ELISpots as well as intracellular cytokine staining flow cytometry. Our data demonstrate that while the IN/IM vaccination mainly led to non-significant systemic immune responses, the vaccine candidate is highly immunogenic if administered IM/IN. This vaccination strategy induced high serum anti-CPAF IgG levels with strong avidity as well as high IgA and IgG levels in vaginal and rectal swabs and in uterine horn flushes. In addition, this vaccination strategy prompted a pronounced cellular immune response: Besides inducing IL-17 production, the vaccine candidate induced a strong IFN-γ response by CD4 T cells. In the IM/IN vaccinated pigs, these cells also significantly downregulated their CCR7 expression – a sign for differentiation into peripheral tissue homing effector/ memory cells. Conclusively, this study demonstrates strong immunogenicity of the IM/IN administered TriAdj-adjuvanted Ct CPAF vaccine candidate. Future studies will test vaccine efficacy of this promising Ct vaccine candidate. In addition, this project demonstrates suitability of the Cs pre-exposed outbred pig model for Ct vaccine development. Thereby, we aim to open the bottleneck of large animal models to facilitate the progression of Ct vaccine candidates into clinical trials.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Cystic fibrosis (CF) diagnosis can be a challenge due to the large spectrum of pathogenic variants in the CFTR gene and the effects of complex alleles. Next-generation sequencing has improved our understanding of the contribution of these complex alleles to the wide spectrum of CF clinical symptoms and to the response to medications. Herein, we studied nine CF patients from six unrelated families carrying the complex allele p.[Ile148Thr;Ile1023_Val1024del] with a frequency of 0.18%. All the patients were from Central Mexico. This complex allele was found in trans with Class I and II pathogenic variants (PVs) such as [c.1521_1523del, p.(Phe508del), and c.3231_3232del, p.(Phe1078Profs*77)]. A targeted search of a dataset of 2,217 exomes from healthy individuals revealed that eight individuals (0.18%) carried the p.(Ile148Thr) variant, but only one (0.022%), who was also born in Central Mexico, was a carrier of the complex allele. These findings led us to hypothesize a founder effect for the p.[Ile148Thr;Ile1023_Val1024del] complex allele in Mexican patients with CF, probably through migration from the French-Canadian population in Mexico. Finally, protein modeling revealed that this complex allele disrupts the secondary structure of the CFTR protein and might alter the ion flow.

This six-year multicentre study investigated acute intentional poisoning with substances of abuse in adolescents to identify changes and patterns in substance use. Data from 568 adolescents were collected from three paediatric poison centres in Romania between January 2017 and December 2022. The study analysed the epidemiological and sociodemographic characteristics of the adolescents, including age, gender, place of residence, history of substance abuse, psychiatric history, and history of institutionalized care. The findings revealed that cannabis and new psychoactive sub-stances (NPSs) are the most commonly implicated substances, each with distinct profiles among adolescents. Cannabis was involved in 45.6% of cases, with a significant association with urban residency. NPSs were identified as the second most prevalent substance, accounting for 38.9% of cases. These were more prevalent in rural areas and among patients with psychiatric disorders. Cannabis and NPSs were also the most commonly implicated substances in acute intentional poisoning cases with substances of abuse. These substances have distinct profiles among adolescents, including age, gender, residency area, history of substance abuse, psychiatric history, and institutional care. These findings underscore the necessity of targeted public health interventions and integrated care approaches to address substance use and related mental health issues in adolescents.

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

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.

The primary therapeutic method for correcting hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but can also act as initiators of adipocyte differentiation in the fetus, which explains the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Changes in the profile of circulating lipids in the maternal bloodstream, depending on adherence to a rational diet among mothers with GDM and the development of fetal macrosomia, is a complex and not fully understood process. The aim of the study was to examine the characteristics of the lipid profile in the blood plasma of pregnant women with GDM in all trimesters of pregnancy based on adherence to diet therapy. The design of the clinical part of the work was carried out within the framework of a 'case-control' study.The study included 110 women, 80 of whom were in the control group, 20 in the group with GDM adhering to the diet, and 10 in the group with GDM not adhering to the diet. The laboratory part was conducted as part of a longitudinal dynamic study with venous blood samples taken at three points: 11-13, 24-26, and 30-32 weeks of pregnancy.A significant impact of diet therapy on the blood lipid composition throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis showed high effectiveness of the developed models with an AUC of 0.98 for the "30-32 weeks" model, with sensitivity and specificity of 1 and 0.9, respectively. An association was found between diet, maternal blood lipid composition at 32 weeks of pregnancy, and newborn weight. The direction of changes in the lipid spectrum during the development of macrosomia and diet therapy is diametrically opposed. This confirms at the molecular level that diet therapy can normalize not only carbohydrate but also lipid metabolism in the mother and fetus. Based on the obtained data, it can be suggested that after validation, the developed models could be used to refine the prognosis of macrosomia development based on the lipid profile of blood plasma at different stages of pregnancy.

Lung cancer, including both non-small cell lung cancer and small cell lung cancer, remains the leading cause of cancer-related mortality worldwide representing 18% of the total cancer deaths in 2020. Many patients are identified already at an advanced stage with metastatic disease and have a worsening prognosis. Recent advances in the genetic understanding of lung cancer have opened new avenues for personalized treatments and targeted therapies. This review examines the latest discoveries in the genetics of lung cancer, discusses key biomarkers, and analyzes current clinical therapies based on this genetic information. It will conclude with a discussion of future prospects and potential research directions.

Placenta accreta spectrum (PAS) disorders are characterized by abnormal trophoblastic invasion into the myometrium, leading to significant maternal health risks. PAS includes placenta accreta (invasion &lt;50% of the myometrium), increta (invasion &gt;50%), and percreta (invasion through the entire myometrium). The condition is most associated with previous cesarean deliveries, and its incidence increases with the number of cesareans. Other risk factors include advanced maternal age, multiparity, and various uterine surgeries. The increasing global cesarean rates heighten the importance of early PAS diagnosis and management. The study reviewed molecular mechanisms driving PAS, focusing on signaling pathways, genetic expression, biomarkers, and non-coding RNAs involved in trophoblastic invasion. It explored genetic expression and key regulatory processes such as apoptosis, cell proliferation, invasion, and inflammation. The manuscript was compiled from recent scientific literature (2014 – 2024) from the Scopus, PubMed, Google Scholar, and Web of Science databases. Placenta Acreta Spectrum; acreta; increta; percreta; trophoblast invasion; miRNAs, lncRNAs; genetic expression; biomarkers; signaling pathways were the employed keywords to compile relevant literature. The PAS is an important obstetric health problem that challenges maternal health risks associated with abnormal trophoblastic invasion that relies on abnormal placentation, uterine scarring, and impaired decidualization. Identifying new biomarkers like AFP, sFlt-1, β-hCG, PlGF, and PAPP-A aids early detection and management. Understanding genetic expression and non-coding RNAs is crucial for unraveling PAS complexities. Besides, aberrant signaling pathways like Notch, PI3K/Akt, STAT3, and TGF-β offer potential therapeutic targets to modulate trophoblastic invasion. This review underscores the need for interdisciplinary care, early diagnosis, and ongoing research into PAS biomarkers and molecular mechanisms to improve prognosis and quality of life for affected women.

Lecanicillium lecanii KMZW-1, is recognized for its potential as a bio-control agent against agricultural and forestry pests, particularly due to its compatibility with integrated pest management (IPM) strategies. This study aimed to investigate the complete genome of L. lecanii KMZW-1 and assess its pathogenicity against Bactrocera dorsalis. Whole genome sequencing revealed a genome size of 47,239,278 bp, comprising 27 contigs, with a GC content of 51.16%. The genome completeness was assessed as 97.93% using BUSCO analysis, AntiSMASH analysis identified 29 gene clusters associated with secondary metabolite biosynthesis providing insights into the genetic basis of its pathogenic mechanisms and biocontrol potential. The pathogenicity of L. lecanii KMZW-1 was assessed using concentrations ranging from 1×10^4 to 1×10^11 spores/mL. Higher-concentrations (1×10^10 to 1×10^11 spores/mL), resulted in significantly increased cumulative mortality rates of B. dorsalis adults compared to the control group. Notably, the pathogenicity was higher in male adults than in females. Probit regression analysis yielded LogLC50 values as 5.662 for female and 4.486 for male B. dorsalis adults. In summary, L. lecanii KMZW-1 exhibits strong insecticidal activity against B. dorsalis and shows potential as a biocontrol agent with IPM strategies. These findings provide robust genomic evidence supporting the use of L. lecanii KMZW-1in managing against B. dorsalis populations.

Alkaloid-based urea derivatives were produced with high yield through the reaction of anabasine and cytisine with isoxazolylphenylcarbamates in boiling benzene. Their antitumor ac-tivity in combination with the commonly used six anticancer drugs namely cyclophosphane, fluorouracil, etoposide, cysplatine, ribomustine with different mechanisms of action was investi-gated. Based on the quantum chemical calculations data and molecular docking, hypotheses have been put forward to explain their mutual influence when affecting on the С6 rat glioma model cells.

This research concerns in to understand the link between urban heat and cool island, the Local Climatic Zones (LCZ) and atmospheric systems during the winter season in the city of Balneário Camboriú, Southern Brazil. To collect meteorological data on urban environmental, 11 permanent points in the Balneário Camboriú metropolitan region were installed. To understand the atmos-pheric systems operating in the region, a synoptic analysis of the dates was performed, and then, the LCZs map created for the city in World Urban Database and Acess Portal Tools was used to cor-relate the magnitudes of the heat and cool islands found in Balneário Camboriú in the winter period. The results indicate that the increasing verticalization with the construction of skyscrapers in Balneário Camboriú has a significant influence on local conditions for the occurrence of heat and cold islands. The findings indicate that LCZs with sparsely distributed buildings (LCZs 6, 8 and 9) and LCZs with dense vegetation (LCZ A) have lower intensity magnitudes of heat and cool con-ditions. The biggest magnitudes of heat and cool islands were reported in LCZs 1 and 3 during the timeframe. The synoptic analysis supports earlier researches that points to atmospheric stability (Anticyclonic domain) as a favorable atmospheric setting for the emergence of urban hear and coolness islands.

(1) Background: Grapes and their associated by-products (as grape pomace, GP) stand out for their polyphenol content, which makes them a source of bioactive compounds with antioxidant capacity. The aim of this research was to determine if the inclusion of 50 g/kg of GP in the diet of hens could enrich egg with antioxidants and to study its effect on internal and external egg quality parameters. (2) Methods: A trial was conducted with two genetic lines of hens, which were fed either a control diet or a diet containing 50 g/kg of GP. Performance, internal and external egg quality, and egg yolk content of vitamins E and A, and gallic acid were determined; (3) Results: In eggs laid by hens fed GP diet, Haugh units and yolk colour score were enhanced, and eggshell became thinner, but without affecting the breaking strength. No dietary effect was observed on vitamin contents in yolk. Higher gallic acid content was observed in yolk of eggs laid by hens fed the GP diet, suggesting that some dietary phenolic compounds could be transferred to the eggs. Hen genetics influenced egg weight, albumen Haugh units, shell thickness and α- and γ-tocopherol concentration in yolks; (4) Conclusions: Dietary inclusion of GP improved the internal quality of eggs, enriching yolks with a phenolic compound, but reduced shell thickness.

Shigella and Salmonella have been implicated in neonatal sepsis, food poisoning, and bacteremia. Their increasing resistance to antibiotics has undermined treatment options, constituting a public health crisis. Also, the scarcity of new antibiotics in the development pipeline compounds multidrug-resistance (MDR). Hence, there is a need to potentiate antibiotics to mitigate resistance. This study leveraged riboflavin to re-sensitize fluoroquinolones and carbapenems against MDR Shigella and Salmonella spp. Disk diffusion, microbroth-dilution, chequerboard, and time-kill assays were used to determine resistance profiles and riboflavin-antibiotic activity. Biofilm-formation and bacterial viability (after riboflavin-antibiotic challenge) was determined with crystal-violet and Macrophage infection assays, respectively. Strains exhibited &gt;80% resistance to fluoroquinolones and carbapenems, with 160–320 µg MIC ranges and 0.5–2 FIC, indicating no synergy. At 40–80 µg riboflavin, there was two-fold reduction in bacterial survival, with meropenem-riboflavin exhibiting significant reduction and FIC of 0.075 indicating high synergistic activity. Biofilm-formation was altered (from strong to weak phenotype) with 40 µg riboflavin-antibiotic treatment. There was a 40–70% reduction in bacterial survival in THP-1 cells treated with meropenem-riboflavin after 3 hours, and complete clearance between 24-48 hpi. Riboflavin potentiated fluoroquinolones and meropenem, leading to modulation of the host immune response for the clearance of the strains.

Chronic joint inflammatory disorders such as osteoarthritis and rheumatoid arthritis have in common an upsurge of inflammation, and oxidative stress, resulting in progressive histological alterations and disabling symptoms. Currently used conventional medication (ranging from pain-killers to biological agents) is potent, but frequently associated with serious, even life-threatening side effects. Used for millennia in traditional herbalism, medicinal plants are a promising alternative, with lower rate of adverse events and an efficiency frequently comparable with that of conventional drugs. Nevertheless, their mechanism of action is in many cases elusive and/or uncertain. Even many of them have been proved effective in studies done in vitro or on animal models, there is a scarcity of human clinical evidence. The purpose of this review is to summarise the available scientific information on these joint-friendly medicinal plants, which have been already tested in human studies: Arnica montana, Boswelliaspp., Curcuma spp., Equisetum arvense, Harpagophytumprocumbens, Salix spp., Sesamumindicum, Symphytumofficinalis, Zingiberofficinalis, Panaxnotoginseng, Whitaniasomnifera.

A recently developed differential geometric representation of redundant serial robot kinematics is employed to create an extended operational space dynamics and control formulation that explicitly accounts for redundant robot degrees of freedom. This formulation corrects deficiencies in kinematics and dynamics of redundant serial robots that have relied for over half a century on error prone generalized inverse velocity-based kinematics for redundancy resolution. New ordinary differential equations of robot operational space dynamics are obtained, without the need for ad-hoc derivation, in terms of task coordinates and self-motion coordinates that represent robot redundancy. A new extended operational space control approach is presented that exploits ordinary differential equations of motion in term of task and self-motion coordinates. This enables enforcement of desired output trajectories, in tandem with direct enforcement of self-motion dependent operational space trajectories that are subject to obstacle avoidance and performance constraints. Four examples are presented with a one degree of redundancy robot that demonstrate validity and superior performance of the new formulation, relative to traditional task space methods used for redundant serial robot control. Finally, an example with eight degrees of redundancy is presented that further illustrates superior performance of the new formulation.

The establishment of protected areas and the practice of ecotourism are generally considered to be one of the crucial means for achieving sustainable development. This study focused on the development of ecotourism in oceanic island national parks, applying System Dynamics theory to explore the core mechanisms, system behaviors, and promotion strategies of sustainable development. We adopted the driving force–state–response (DSR) indicator framework and combined it with System Dynamics theory to construct a sustainable development model for ecotourism in oceanic island national parks. Through this model, we discovered that the intricate causal relationships between indicator factors form three dominant causal feedback loops that drive system behavior. The three causal feedback loops comprise one reinforcing loop and two balancing loops. Through the interaction and mutual regulation between these three loops, preliminary estimates can be made regarding the specific behavioral trends that promote ecotourism in oceanic island national parks. In addition, we further investigated whether the implementation of fee-based policies in oceanic island national parks contributes to these parks’ sustainability goals. Subsequently, this model could be developed into a numerical simulation program based on System Dynamics theory, thereby enabling the computer simulation of system behavior under various scenarios and conditions.

For the past 20 years, reintroduction efforts have been underway to reestablish a migratory population of Northern Bald Ibises (Geronticus eremita) in Central Europe, which now consists of more than 250 birds. They breed both north and south of the Alps and migrate to a common wintering ground in Tuscany. Recently, the start of autumn migration is increasingly delayed, which correlates with extended warm periods in autumn. Later in the year, however, the birds no longer find sufficient thermals to cross the Alps and remain in the northern Alpine foothills. In order to save their lives, we had to capture the affected birds before onset of winter, which is not a sustainable solution. A new approach to solving the problem is the establishment of a second migration route to a wintering area in Andalusia, Spain, connecting our population with a sedentary population there. The new migration route bypasses mountain barriers and allows the birds to reach the wintering grounds also later in the year. The modelling of a pan-European population will provide the birds with high ecological and spatial flexibility. Our project exemplifies the consequences of advancing global warming for animal populations and the associated challenges for conservation projects.

Educational institutions are increasingly utilizing chatbots to enhance communication and streamline administrative tasks, including answering frequently asked questions (FAQs). Serving as the first review about employing chatbots in educational administration, this systematic review explores the potential of chatbots to improve educational administration. While research on chatbots in education has primarily focused on their role in learning, their application for administrative tasks remains underexplored. This gap necessitates a systematic investigation of how chatbots are utilized and implemented to streamline administrative processes. Following the PRISMA framework, this review analyzes 54 studies to address five key areas: functionalities performed, data employed, technologies used, evaluation methods, and future development challenges. The review reveals a critical need for a comprehensive framework for developing administrative chatbots that are specifically tailored to educational institutions. This review underscores the importance of such a framework in addressing identified gaps in the literature and in incorporating generative AI into educational administrative tasks management. The review also highlights a noticeable lack of research in this domain, including the potential of generative AI, like ChatGPT, to improve administrative efficiency. Furthermore, the study discusses the potential impact, practical applications of administrative chatbots in education and future research directions.