Acacia melanoxylon is highly valued for its commercial applications, with the heartwood exhibiting a range of colors from dark to light among its various clones. The underlying mechanisms contributing to this color variation, however, have not been fully elucidated. In an effort to understand the factors that influence the development of dark heartwood, a comparative analysis was conducted on the microstructure, substance composition, differential gene expression, and metabolite profiles in the sapwood (SW), transition zone (TZ), and heartwood (HW) of two distinct clones, SR14 and SR25. A microscopic examination revealed that heartwood color variations are associated with an increased substance content within the ray parenchyma cells. A substance analysis indicated that the levels of starches, sugars, and lignin were more abundant in SP compared to HW, while the concentrations of phenols, flavonoids, and terpenoids were found to be higher in HW than in SP. Notably, the dark heartwood of the SR25 clone exhibited greater quantities of phenols and flavonoids compared to the SR14 clone, suggesting that these compounds are pivotal to the color distinction of the heartwood. An integrated analysis of transcriptome and metabolomics data uncovered a significant accumulation of sinapyl alcohol, sinapoyl aldehyde, hesperetin, 2′, 3, 4, 4′, 6′-peptahydroxychalcone 4′-O-glucoside, homoeriodictyol, and (2S)-liquiritigenin in the heartwood of SR25, which correlates with the up-regulated expression of CCRs (evm.TU.Chr3.1751, evm.TU.Chr4.654_667, evm.TU.Chr4.675, evm.TU.Chr4.699, and evm.TU.Chr4.704), COMTs (evm.TU.Chr13.3082, evm.TU.Chr13.3086, and evm.TU.Chr7.1411), CADs (evm.TU.Chr10.2175, evm.TU.Chr1.3453, and evm.TU.Chr8.1600), and HCTs (evm.TU.Chr4.1122, evm.TU.Chr4.1123, evm.TU.Chr8.1758, and evm.TU.Chr9.2960) in the TZ of A. melanoxylon. Furthermore, a marked differential expression of transcription factors (TFs), including MYBs, AP2/ERFs, bHLHs, bZIPs, C2H2s, and WRKYs, were observed to be closely linked to the phenols and flavonoids metabolites, highlighting the potential role of multiple TFs in regulating the biosynthesis of these metabolites and, consequently, influencing the color variation in the heartwood. This study facilitates molecular breeding for the accumulation of metabolites influencing the heartwood color in A. melanoxylon, and offers new insights into the molecular mechanisms underlying heartwood formation in woody plants. Full article

Background: This study aimed to investigate the association between abdominal aortic calcification (AAC) and coronary heart disease (CHD) in essential hypertension (EH). Methods: This study included patients diagnosed with EH during the 2013–2014 NHANES survey cycle. The study cohort was categorized into the following four groups based on their AAC-24 score: no AAC (0); mild AAC (1–4); moderate AAC (5–15); and severe AAC (16–24). Logistic regression models were used to assess the association between AAC and CHD. Restricted cubic spline curves (RCS) were used to explore possible nonlinear relationships between AAC and CHD. Results: The prevalence of CHD was found to be higher in the moderate AAC and severe AAC groups than in the group without AAC (40.1% versus 30.9%, 47.7% versus 30.9%). On a continuous scale, the fully adjusted model showed a 7% increase in the risk of CHD prevalence per score increase in AAC [OR (95% CI) = 1.07 (1.03–1.11)]. On a categorical scale, the fully adjusted model showed the risk of CHD prevalence in EH patients with moderate AAC and severe AAC was 2.06 (95%CI, 1.23–3.45) and 2.18 (1.09–5.25) times higher than that in patients without AAC, respectively. The RCS curve suggested a dose-response linear relationship between AAC and CHD. Conclusion: These findings highlight that in patients with EH, a higher severity of AAC is associated with a higher risk of CHD prevalence. Full article

The activity of dental caries, combined with its multifactorial etiology, alters salivary molecule composition. The present systematic review was developed to answer the following question: “Are salivary biomarkers reliable for diagnosis of dental caries?”. Following the “Preferred Reporting Item for Systematic Reviews and Meta-analysis” (PRISMA) guidelines, the review was conducted using multiple database research (Medline, Web of Science, and Scopus). Studies performed on healthy subjects with and without dental caries and providing detailed information concerning the clinical diagnosis of caries (Decayed, Missing, Filled Teeth-DMFT and International Caries Detection and Assessment System-ICDAS criteria) were included. The quality assessment was performed following a modified version of the Joanna Briggs Institute Prevalence Critical Appraisal Checklist. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022304505). Sixteen papers were included in the review. All studies reported statistically significant differences in the concentration of salivary molecules between subjects with and without caries (p < 0.05). Proteins were the most investigated molecules, in particular alpha-amylase and mucins. Some studies present a risk of bias, such as identifying confounding factors and clearly defining the source population. Nevertheless, the 16 papers were judged to be of moderate to high quality. There is evidence that some salivary compounds studied in this review could play an important diagnostic role for dental caries, such as salivary mucins, glycoproteins (sCD14), interleukins (IL-2RA, 4,-13), urease, carbonic anhydrase VI, and urea. Full article

Significant health disparities exist in relation to pathogenic variants in BRCA1/2. This study aimed to better understand the barriers and facilitators to BRCA1/2 genetic testing and intrafamilial communication of risk in racially and ethnically diverse individuals. We conducted qualitative interviews with non-Hispanic White (n = 11) and Black, Indigenous, People of Color (BIPOC) individuals (n = 14) who underwent testing for pathogenic BRCA1/2 variants. We employed template analysis, case study analysis, and comparative case study analysis to examine healthcare experiences related to genetic testing as well as intrafamilial communication of risk. Applying an intersectional lens, we sought to inform more person-centered approaches to precision healthcare and help dismantle disparities in genomic healthcare. Template analysis revealed salient factors at the individual (psychosocial well-being), interpersonal/familial, and healthcare system levels. A two-part case study analysis provided insights into how race/ethnicity, cultural norms, and socioeconomic status interact with systemic and structural inequities to compound disparities. These findings underscore the need for person-centered, tailored, and culturally sensitive approaches to understanding and addressing the complexities surrounding testing and the communication of BRCA risk. Applying an intersectional lens can inform more person-centered approaches to precision healthcare and may help to surmount existing disparities. Full article

Novel biomarkers are needed in diagnosing reliably acute kidney injury (AKI) in dogs and in predicting morbidity and mortality after AKI. Our hypothesis was that two novel tubular biomarkers, urinary clusterin (uClust) and cystatin B (uCysB), are elevated in dogs with AKI of different etiologies. In a prospective, longitudinal observational study, we collected serum and urine samples from 18 dogs with AKI of different severity and of various etiology and from 10 healthy control dogs. Urinary clusterin and uCysB were compared at inclusion between dogs with AKI and healthy controls and remeasured one and three months later. Dogs with AKI had higher initial levels of uClust (median 3593 ng/mL; interquartile range [IQR]; 1489–10,483) and uCysB (554 ng/mL; 29–821) compared to healthy dogs (70 ng/mL; 70–70 and 15 ng/mL; 15–15; p < 0.001, respectively). Initial uCysB were higher in dogs that died during the one-month follow-up period (n = 10) (731 ng/mL; 517–940), compared to survivors (n = 8) (25 ng/mL; 15–417 (p = 0.009). Based on these results, uClust and especially uCysB are promising biomarkers of AKI. Further, they might reflect the severity of tubular injury, which is known to be central to the pathology of AKI. Full article

It is common to use efficient catalysts in the anodes and cathodes of methanol and ethanol fuel cells, such as platinum and ruthenium. However, due to their expansivity and rarity, finding a suitable alternative is important. In this work, multi-component catalysts consisting of tungsten oxide, nickel cobaltite, and activated carbon were synthesized through the hydrothermal method. The performance of catalysts in the processes of methanol and ethanol oxidation reactions (MOR and EOR) were investigated. The addition of activated carbon obtained from wheat husk, with an excellent active surface and acceptable electrical conductivity, to the matrix of the catalyst significantly facilitated the oxidation process of alcohols and enhanced the efficiency of the catalyst. The physical and electrochemical characterization of the NiCo₂O₄/WO₃ hybridized with the wheat husk-derived activated carbon (ACWH) catalyst indicated its successful synthesis and good performance in the alcohol oxidation process. NiCo₂O₄/WO₃/ACWH with an oxidation current density of 63.39 mA/cm² at the peak potential of 0.58 V (1.59 vs. RHE), a cyclic stability of 98.6% in the methanol oxidation reaction (MOR) and 27.98 mA/cm² at the peak potential of 0.67 V (1.68 vs. RHE), and a cyclic stability of 95.7% in the ethanol oxidation reaction (EOR) process can be an interesting option for application in the anodes of alcohol fuel cells. Full article

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy. Full article

This research stems from the increasing use of hand gestures in various applications, such as sign language recognition to electronic device control. The focus is the importance of accuracy and robustness in recognizing hand gestures to avoid misinterpretation and instruction errors. However, many experiments on hand gesture recognition are conducted in limited laboratory environments, which do not fully reflect the everyday use of hand gestures. Therefore, the importance of an ideal background in hand gesture recognition, involving only the signer without any distracting background, is highlighted. In the real world, the use of hand gestures involves various unique environmental conditions, including differences in background colors, varying lighting conditions, and different hand gesture positions. However, the datasets available to train hand gesture recognition models often lack sufficient variability, thereby hindering the development of accurate and adaptable systems. This research aims to develop a robust hand gesture recognition model capable of operating effectively in diverse real-world environments. By leveraging deep learning-based image augmentation techniques, the study seeks to enhance the accuracy of hand gesture recognition by simulating various environmental conditions. Through data duplication and augmentation methods, including background, geometric, and lighting adjustments, the diversity of the primary dataset is expanded to improve the effectiveness of model training. It is important to note that the utilization of the green screen technique, combined with geometric and lighting augmentation, significantly contributes to the model’s ability to recognize hand gestures accurately. The research results show a significant improvement in accuracy, especially with implementing the proposed green screen technique, underscoring its effectiveness in adapting to various environmental contexts. Additionally, the study emphasizes the importance of adjusting augmentation techniques to the dataset’s characteristics for optimal performance. These findings provide valuable insights into the practical application of hand gesture recognition technology and pave the way for further research in tailoring techniques to datasets with varying complexities and environmental variations. Full article

In our study, we investigate the phenomenon of information loss, as measured by the Kullback–Leibler divergence, in a many-fermion system, such as the Lipkin model. Information loss is introduced as the number N of particles increases, particularly when the system is in a mixed state. We find that there is a significant loss of information under these conditions. However, we observe that this loss nearly disappears when the system is in a pure state. Our analysis employs tools from information theory to quantify and understand these effects. Full article

Many biologically active metabolites of the essential amino acid L-tryptophan (Trp) are associated with different neurodegenerative diseases and neurological disorders. Precise and reliable methods for their determination are needed. Variability in their physicochemical properties makes the analytical process challenging. In this case, chemical modification of analyte derivatization could come into play. Here, we introduce a novel fast reversed-phase ultra-high-performance liquid chromatography (RP-UHPLC) coupled with tandem mass spectrometry (MS/MS) method for the determination of Trp and its ten metabolites in human plasma samples after derivatization with 2-bromo-4′-nitroacetophenone (BNAP). The derivatization procedure was optimized in terms of incubation time, temperature, concentration, and volume of the derivatization reagent. Method development comprises a choice of a suitable stationary phase, mobile phase composition, and gradient elution optimization. The developed method was validated according to the ICH guidelines. Results of all validation parameters were within the acceptance criteria of the guideline, i.e., intra- and inter-day precision (expressed as relative standard deviation; RSD) were in the range of 0.5–8.2% and 2.3–7.4%, accuracy was in the range of 93.3–109.7% and 94.7–110.1%, limits of detection (LODs) were in the range of 0.15–9.43 ng/mL, coefficients of determination (R2) were higher than 0.9906, and carryovers were, in all cases, less than 8.8%. The practicability of the method was evaluated using the blue applicability grade index (BAGI) with a score of 65. Finally, the developed method was used for the analysis of Alzheimer’s disease and healthy control plasma to prove its applicability. Statistical analysis revealed significant changes in picolinic acid (PA), anthranilic acid (AA), 5 hydroxyindole-3-acetic acid (5-OH IAA), and quinolinic acid (QA) concentration levels. This could serve as the basis for future studies that will be conducted with a large cohort of patients. Full article

With a global focus on environmental sustainability, hotels worldwide are actively transitioning their services from conventional to eco-friendly practices. This study aims to comprehensively understand the factors that contribute to visitors’ satisfaction in eco-friendly hotels and how this satisfaction influences customers’ future reactions towards such environmentally conscious establishments. Employing the Stimulus-Organism-Response theory, this study collected data from 277 respondents using a robust quantitative research strategy. A combined approach of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) was employed, to uncover deep insights into visitors’ satisfaction and their reactions towards eco-friendly hotels. The PLS-SEM results reveal significant associations between customers’ satisfaction towards eco-friendly hotel services and service quality, green practices, perceived value, and environmental sensitivity. Moreover, this study highlights a positive correlation between satisfaction and crucial outcomes like revisit intention (RVI), willingness to pay a premium (WTPP), and word-of-mouth intention (WOMI). Complementing these findings, the fsQCA analysis uncovers intricate causal relationships among antecedents that influence customer satisfaction in eco-friendly hotels. By offering critical marketing insights, this study provides guidance for hotels, the tourism industry, and policymakers on attracting customers to eco-friendly hotels, to meet the increasing demands for environmental sustainability. Full article

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52). Full article

The strength–ductility mechanism of the low-carbon steels processed by laser cutting is investigated in this paper. A typical gradient-phased structure can be obtained near the laser cutting surface, which consists of a laser-remelted layer (LRL, with the microstructure of lath bainite + granular bainite) and heat-affected zone (HAZ). As the distance from the laser cutting surface increases, the content of lath martensite decreases in the HAZ, which is accompanied by a rise in the content of ferrite. Considering that the microstructures of the LRL and HAZ are completely different from the base metal (BM, ferrite + pearlite), a significant strain gradient can be inevitably generated by the remarkable microhardness differences in the gradient-phased structure. The hetero-deformation-induced strengthening and hardening will be produced, which is related to the pileups of the geometrically necessary dislocations (GNDs) that are generated to accommodate the strain gradient near interfaces. Plural phases of the HAZ can also contribute to the increment of the hetero-deformation-induced strengthening and hardening during deformation. Due to the gradient-phased structure, the low carbon steels under the process of laser cutting have a superior combination of strength and ductility as yield strength of ~487 MPa, tensile strength of ~655 MPa, and total elongation of ~32.7%. Full article

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment. Full article

Farm crop diversity is often overlooked, predominantly indigenous crops’ role in this diversity. The main concentration has been on the contribution or role of exotic crops to household crop diversification. At the same time, the role played by both types of crops in household food security has only been aggregated, failing to show how indigenous crops play a key role in household food security. This research paper uses Tanzanian Panel data from waves 4 and 5 to study the factors influencing indigenous and exotic crop diversification and the role of this diversity in household food security. Using a random effect model, the author found that various factors are crucial in determining household crop diversification. Gender, household size, marital status, and expected harvest quantity are among the key factors influencing indigenous crop diversification. On the other hand, age, education, access to markets, access to irrigation services, and soil quality are the primary factors that affect the diversification of exotic crops. Moreover, the findings show that indigenous and exotic crop diversity significantly influences household food consumption. Thus, policies to increase the production of indigenous crops in order to improve household food consumption should be considered. Full article

In living cells, some reactions can be conducted by more than one enzyme and sometimes it is difficult to establish which enzyme is responsible. Such is the case with proteins from the TET family, capable of converting 5-methyl-2’-deoxycytidine (5-$mdC$) in DNA to 5-(hydroxymethyl)-2’-deoxycytidine (5-$hmdC$) and further to 5-formyl-2’-deoxycytidine (5-$fdC$) and 5-carboxy-2’-deoxycytidine (5-$cadC$). The estimation of the efficiency of particular TETs in particular oxidative reactions and different cell types is important but experimentally difficult. Here, we propose an approach with mathematical modeling in which methylation and known deoxycytidine modification pathways are presented by 343 possible model versions with assumed different combinations of TET1, 2, and 3 activities in different pathways. Model parameters were calculated on the basis of 5-$mdC$, 5-$hmdC$, 5-$fdC$, 5-$cadC$, and 5-$hmdU$ levels experimentally assessed in five human cultured cell lines and previously published. Selection of the model versions that give in simulations the best average fit to experimental data suggested that not all TET proteins participate in all modification reactions and that TET3 activity may be especially important in the reaction of 5-$fdC$ removal. Full article

Foam concrete is a typical cement-based porous material; its special microstructure endows it with excellent properties, such as light weight, energy efficiency, thermal insulation, and fire resistance. Therefore, it is widely used as a thermal insulation material for buildings. The heat transfer modes of foam concrete include conduction, convection, and radiation. However, previous studies considered conduction to be the dominant mode, often neglecting the effects of convection and radiation. In this study, a stochastic numerical model of the foam concrete microstructure is established based on the statistical parameters of the pore structure. With this model, the heat transfer mechanism of foam concrete is analyzed at the mesoscopic level, and the equivalent thermal conductivity is calculated. By comparing four different working conditions, the influence of conduction, convection, and radiation on the heat transfer of foam concrete is analyzed, and the specific contribution rates of conduction, convection, and radiation are calculated. The results show that the convection effect is weak due to the pore size being smaller than 1 ; so, the influence of convection can be neglected in the heat transfer analysis of foam concrete. The contribution of radiation increases with the decrease in foam concrete density and the increase in temperature difference. When the temperature difference is 40 and the density is 300 , the contribution of radiation exceeds 20. Therefore, for low-density and high-temperature difference situations, the influence of radiation cannot be ignored. The heat transfer in foam concrete is mainly through conduction, but with the decrease in density and the increase in temperature difference, the contribution of conduction shows a downward trend. Nevertheless, the contribution of conduction is still much larger than that of radiation and convection. Full article

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let us delve into the mathematical intricacies of these sequences. We have previously devised a mathematical approach for defining a “healthy” sequence. This sequence is characterized by having at most four distinct nucleotides (denoted as $nt\le 4$). It serves as the generator of a group denoted as $f_p$. The desired properties of this sequence are as follows: $f_p$ should be close to a free group of rank $nt-1$, it must be aperiodic, and $f_p$ should not have isolated singularities within its $S{L}_2\left(\mathbb{C}\right)$ character variety (specifically within the corresponding Groebner basis). Now, let us explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as $S_2^4$. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI Equation. In certain cases, $S_2^4$ degenerates, in the sense that two punctures collapse, resulting in a “wild” dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find many applications including cancer research and the investigation of neurodegenative diseases. Full article

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling. Full article

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. Full article

The current investigation aims to underline the impact of salicylic acid or calcium chloride seed pre-treatments on mineral status and oxidative stress markers, namely levels of electrolyte leakage (EL) and lipid peroxidation levels, measured as thiobarbituric reactive substances (TBARS), and the activity of some antioxidant enzymes in roots and leaves of plants in two barley species grown under various salt treatments. Overall, our results revealed that salinity inhibits essential nutrient absorption such as iron, calcium, magnesium and potassium and stimulates the absorption of sodium. Also, this environmental constraint induced oxidative stress in plants in comparison with the control conditions. This state of oxidative stress is reflected by an increase in TBARS content as well as the stimulation of EL values. In addition, salinity induced disturbances in the activity of antioxidant enzymes, which were mainly dependent on the applied salt concentration and the species. In addition, Hordeum marinum maintained high antioxidant enzyme activity and low levels of oxidative stress parameters, which reinforces its salt-tolerant character. Importantly, salicylic acid or calcium chloride seed priming alleviated the mineral imbalance and the oxidative damage induced by salinity. Moreover, seed priming improves iron, calcium magnesium and potassium content and limitsthe accumulation of sodium. Also, both treatments not only decrease TBARS levels and limit EL, but they also stimulate the antioxidant enzyme activities in the leaves and roots of the stressed plants as compared with stressed plants grown from non-primed seeds. Interestingly, the beneficial effects of the mentioned treatments were more notable on Hordeum vulgare species. Full article

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. Full article