Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine enhancement (LIE) in computed tomography (CT) imaging has emerged as a potential alternative, capitalizing on the similarities in the contrast kinetics between gadolinium and iodinated contrast agents. Studies have investigated LIE-CT’s effectiveness in myocardial infarction (MI) detection, revealing promising outcomes alongside some disparities compared to LGE-CMR. LIE-CT also proves beneficial in diagnosing non-ischemic heart diseases such as myocarditis, hypertrophic cardiomyopathy, and sarcoidosis. While LIE-CT demonstrates good accuracy in detecting certain myocardial pathologies, including acute MI and chronic fibrotic changes, it has limitations, such as the inability to detect diffuse myocardial enhancement. Nonetheless, thanks to the availability of optimized protocols with minimal radiation doses and contrast medium administration, integrating LIE-CT into cardiac CT protocols could enhance its clinical utility, particularly in acute settings, providing valuable prognostic and management insights across a spectrum of cardiac ischemic and non-ischemic conditions. Full article

In order to meet energy and environmental challenges, many countries will implement the replacement of fuel vehicles for the future clean energy transition; so, the number of electric vehicles (EVs) operating in cities will grow significantly. It is crucial to assess the risks of the electric vehicle supply chain (EVSC) and prevent them. Based on this, this paper proposes an EVSC risk research framework with combined weights and an improved matter-element extension model: (i) Firstly, the EVSC evaluation index system is constructed from the six stages of supply chain planning, sales, procurement, manufacturing, distribution, after-sales, and external risks. (ii) The subjective and objective weights are calculated by the decision laboratory method and entropy weight method, respectively, and then the minimum deviation method is used for a combined design to overcome the defects of a single method. (iii) An improved matter-element extension model (MEEM) is constructed by introducing asymmetric proximity degree and risk bias. (iv) The model is applied to a case study and its feasibility and superiority are verified through sensitivity analysis and comparative analysis. The final results show that the method and framework proposed in this paper are in line with EVSC risk assessment standards and superior to other models, which can help EVSC managers to identify potential risks, formulate appropriate risk prevention measures, promote the stable development of electric vehicles, and provide a reference for the development of energy and environment. Full article

Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha⁻¹, and the net primary productivity was 8.30 tC ha⁻¹ year⁻¹. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites. Full article

For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed. Full article

The textile industry is one of the most important industries in the European Union. The main environmental problems of the textile industry are the high water consumption, the generated pollution, the variety of chemicals used and the high energy demand. Recently, adsorbents with a large specific surface area and low weight, such as aerogels, have attracted great interest as promising materials for removing dyes from polluted water. Cellulose aerogels are inexpensive and non-toxic. Langmuir and Freundlich isotherms were chosen as the best method to describe the performance of the adsorbent. In this study, the adsorption efficiency of Congo red, Naphthol green B, Rhodamine B and Methylene blue were determined by using an adsorbent synthesized from paper and cardboard waste. The total organic carbon concentration was chosen as an indicator of the concentration of the dyes in the solutions. The aerogel capsules had 5% cellulose content. It was found that the adsorption capacity of the aerogel in the solutions of Congo red varied from 0.028 mg/g to 14.483 mg/g; in the solutions of Naphthol green B, from 0.013 mg/g to 7.698 mg/g; in the solutions of Rhodamine B, from 0.020 mg/g to 8.768 mg/g; and in the solutions of Methylene blue, from 0.024 mg/g to 13.538 mg/g. Full article

A general mechanism for catalytic urethane formation in the presence of acid catalysts, dimethyl hydrogen phosphate (DMHP), methanesulfonic acid (MSA), and trifluoromethanesulfonic acid (TFMSA), has been studied using theoretical methods. The reaction of phenyl isocyanate (PhNCO) and butan-1-ol (BuOH) has been selected to describe the energetic and structural features of the catalyst-free urethane formation. The catalytic activities of DMHP, MSA, and TFMSA have been compared by adding them to the PhNCO–BuOH model system. The thermodynamic properties of the reactions were computed by using the G3MP2BHandHLYP composite method. It was revealed that in the presence of trifluoromethanesulfonic acid, the activation energy was the lowest within the studied set of catalysts. The achieved results indicate that acids can be successfully employed in urethane synthesis and the mechanism was described. Full article

In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing ergometer. The intensity increase resulted in maximum values of 79.4 ± 2.4 and 69.7 ± 1.5 mL/min/kg for oxygen uptake, 179.3 ± 5.7 and 152.5 ± 2.9 L/min for ventilation, 170 ± 1 and 173 ± 0 bpm for heart rate, 10.6 and 15.8 mmol/L for blood lactate concentration, and 38.1 ± 0.03 and 38.8 ± 0.03 °C for core temperature for the male and female rowers. The percentage of power corresponding to a previously conducted maximum 2000 m rowing ergometer test and the work at each step increased from 49 to 127 and 42 to 103% and from 226.8 to 398.9 J and 174.0 to 250.0 J, from low to extreme intensities, for the male and female. Concurrently, there was a decrease in cycle length and propulsive time, followed by an increase in maximal handle drive velocity, with the rise in rowing intensity. These world-class rowers seem capable of maintaining physiological and technical profiles (and a remarkable capacity to generate substantial power) at this phase of their careers possibly due to long-term engagement in elite-level training. Biophysical data provide valuable referential information for guiding rowers to improve their performance. Full article

The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor sentiment in the stock market and influence market movements. Previous research models have struggled to accurately mine multiple sources of market sentiment information originating from the Internet and traditional sentiment analysis models are challenging to quantify and combine indicator data from market data and multi-source sentiment data. Therefore, we propose a BERT-LLA stock price prediction model incorporating multi-source market sentiment and technical analysis. In the sentiment analysis module, we propose a semantic similarity and sector heat-based model to screen for related sectors and use fine-tuned BERT models to calculate the text sentiment index, transforming the text data into sentiment index time series data. In the technical indicator calculation module, technical indicator time series are calculated using market data. Finally, in the prediction module, we combine the sentiment index time series and technical indicator time series and employ a two-layer LSTM network prediction model with an integrated attention mechanism to predict stock close price. Our experiment results show that the BERT-LLA model can accurately capture market sentiment and has a strong practicality and forecasting ability in analyzing market sentiment and stock price prediction. Full article

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO₂ emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO₂-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO₂ emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO₂ emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO₂ emissions from coal, the three-year average change in CO₂ emissions, GDP, CO₂ emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO₂ emissions changes, contributing to the global efforts to address climate change. Full article

The aim of this study was to identify effective genetic markers for the Antigen Processing Associated Transporter 1 (TAP1), α (1,2) Fucosyltransferase 1 (FUT1), Natural Resistance Associated Macrophage Protein 1 (NRAMP1), Mucin 4 (MUC4) and Mucin 13 (MUC13) diarrhea-resistance genes in the local pig breeds, namely Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs, to provide a reference for the characterization of local pig breed resources in Shanghai. Polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLR) and sequence sequencing were applied to analyze the polymorphisms of the above genes and to explore the effects on the immunity of Shanghai local pig breeds in conjunction with some immunity factors. The results showed that both TAP1 and MUC4 genes had antidiarrheal genotype GG in the five pig breeds, AG and GG genotypes of the FUT1 gene were detected in Pudong white pigs, AA antidiarrheal genes of the NRAMP1 gene were detected in Meishan pigs, the AB type of the NRAMP1 gene was detected in Pudong white pigs, and antidiarrheal genotype GG of the MUC13 gene was only detected in Shanghai white pigs. The MUC13 antidiarrhea genotype GG was only detected in Shanghai white pigs. The TAP1 gene was moderately polymorphic in Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs, among which TAP1 in Shanghai white pigs and Shawutou pigs did not satisfy the Hardy–Weinberg equilibrium. The FUT1 gene of Pudong white pigs was in a state of low polymorphism. NRAMP1 of Meishan pigs and Pudong white pigs was in a state of moderate polymorphism, which did not satisfy the Hardy–Weinberg equilibrium. The MUC4 genes of Shanghai white pigs and Pudong white pigs were in a state of low polymorphism, and the MUC4 genes of Fengjing pigs and Shawutou pigs were in a state of moderate polymorphism, and the MUC4 genes of Fengjing pigs and Pudong white pigs did not satisfy the Hardy–Weinberg equilibrium. The MUC13 gene of Shanghai white pigs and Pudong white pigs was in a state of moderate polymorphism. Meishan pigs had higher levels of IL-2, IL-10, IgG and TNF-α, and Pudong white pigs had higher levels of IL-12 than the other pigs. The level of interleukin 12 (IL-12) was significantly higher in the AA genotype of the MUC13 gene of Shanghai white pigs than in the AG genotype. The indicator of tumor necrosis factor alpha (TNF-α) in the AA genotype of the TAP1 gene of Fengjing pigs was significantly higher than that of the GG and AG genotypes. The indicator of IL-12 in the AG genotype of the Shawutou pig TAP1 gene was significantly higher than that of the GG genotype. The level of TNF-α in the AA genotype of the NRAMP1 gene of Meishan pigs was markedly higher than that of the AB genotype. The IL-2 level of the AG type of the FUT1 gene was obviously higher than that of the GG type of Pudong white pigs, the IL-2 level of the AA type of the MUC4 gene was dramatically higher than that of the AG type, and the IgG level of the GG type of the MUC13 gene was apparently higher than that of the AG type. The results of this study are of great significance in guiding the antidiarrhea breeding and molecular selection of Shanghai white pigs, Fengjing pigs, Shawutou pigs, Meishan pigs and Pudong white pigs and laying the foundation for future antidiarrhea breeding of various local pig breeds in Shanghai. Full article

Seed infection caused by Fusarium spp. is one of the major threats to the seed quality and yield of agricultural crops, including garden peas. The use of Bacillus spp. with multiple antagonistic and plant growth-promoting (PGP) abilities represents a potential disease control strategy. This study was performed to evaluate the biocontrol potential of new Bacillus spp. rhizosphere isolates against two Fusarium strains affecting garden peas. Six Bacillus isolates identified by 16S rDNA sequencing as B. velezensis (B42), B. subtilis (B43), B. mojavensis (B44, B46), B. amyloliquefaciens (B50), and B. halotolerans (B66) showed the highest in vitro inhibition of F. proliferatum PS1 and F. equiseti PS18 growth (over 40%). The selected Bacillus isolates possessed biosynthetic genes for endoglucanase (B42, B43, B50), surfactin (B43, B44, B46), fengycin (B44, B46), bacillomycin D (B42, B50), and iturin (B42), and were able to produce indole-3-acetic acid (IAA), siderophores, and cellulase. Two isolates, B. subtilis B43 and B. amyloliquefaciens B50, had the highest effect on final germination, shoot length, root length, shoot dry weight, root dry weight, and seedling vigor index of garden peas as compared to the control. Their individual or combined application reduced seed infection and increased seed germination in the presence of F. proliferatum PS1 and F. equiseti PS18, both after seed inoculation and seed bio-priming. The most promising results were obtained in the cases of the bacterial consortium, seed bio-priming, and the more pathogenic strain PS18. The novel Bacillus isolates may be potential biocontrol agents intended for the management of Fusarium seed-borne diseases. Full article

The multi-scale study of rock properties is a necessary step in the planning of oil and gas reservoir developments. The amount of core samples available for research is usually limited, and some of the samples can be distracted. The investigation of core reconstruction possibilities is an important task. An approach to the real-size reconstruction of porous media with a given (target) porosity and permeability by controlling the parameters of FFF 3D printing using CT images of the original core is proposed. Real-size synthetic core specimens based on CT images were manufactured using FFF 3D printing. The possibility of reconstructing the reservoir properties of a sandstone core sample was proven. The results of gas porometry measurements showed that the porosity of specimens No.32 and No.46 was 13.5% and 12.8%, and the permeability was 442.3 mD and 337.8 mD, respectively. The porosity of the original core was 14% and permeability was 271 mD. It was found that changing the layer height and nozzle diameter, as well as the retract and restart distances, has a direct effect on the porosity and permeability of synthetic specimens. This study shows that porosity and permeability of synthetic specimens depend on the flow of the material and the percentage of overlap between the infill and the outer wall. Full article

Radiocesium (r-Cs) and radiostrontium (r-Sr) released from nuclear accidents (e.g., Chornobyl, Fukushima) and routine operations (reactors, reprocessing) pose environmental and health concerns. Their primary pathway to humans is through plant uptake and subsequent bioaccumulation within the food chain. While soil amendments with potassium (K) and calcium (Ca) are known to mitigate r-Cs and r-Sr uptake, respectively, the impact on plant growth remains unclear. This study investigates the effects of Cs and Sr on the growth of Holcus lanatus L. seedlings under hydroponic and soil conditions with varying Cs and Sr concentrations. Stable isotopes of Cs and Sr served as non-radioactive analogs. Seedling growth was assessed across a range of Cs and Sr concentrations (≤1 and ≥4 mg L⁻¹). The impact of the addition of K and Ca on Cs/Sr uptake in amended soils was also evaluated. Additionally, this study examined how Cs and Sr amendments affected the influx rates of other nutrients in H. lanatus. Higher Cs and Sr concentrations (≥4 mg L⁻¹) significantly inhibited seedling growth, while lower concentrations had no effect. Notably, H. lanatus exhibited moderate Cs tolerance and strong Sr tolerance. Furthermore, K and Ca supplementation in Cs/Sr-amended soils demonstrably reduced plant uptake of these elements. This study also observed alterations in the uptake rates of other nutrients within H. lanatus due to Cs/Sr addition. This study suggests that H. lanatus exhibits moderate tolerance to Cs and Sr contamination, potentially making it suitable for revegetation efforts in contaminated grasslands. Additionally, K and Ca amendments show promise as a strategy to mitigate plant uptake of these radioisotopes further. These findings contribute to the development of safer revitalization strategies for areas impacted by nuclear accidents. Full article

Background/Objectives: Contrast-induced acute kidney injury (AKI) is associated with early mortality and adverse events. However, in the setting of transcatheter aortic valve implantation (TAVI), previous literature has failed to establish a correlation between the absolute volume of contrast media administered and mortality. We aimed to investigate the impact of contrast volume administered normalised to estimated glomerular filtration rate (CV/eGFR) on the development of AKI and on 30-day all-cause mortality in TAVI patients. Methods: We retrospectively analysed a cohort of 1150 patients who underwent TAVI at our unit between 2015 and 2018. Results: Follow-up was complete for 1064 patients. There were 23 deaths within the follow-up period and 76 cases of AKI, 9 of which required new renal replacement therapy (RRT). Receiver-operating characteristic (ROC) curve analysis showed fair discrimination for 30-day all-cause mortality at a CV/eGFR ratio of 3.6 (area under the ROC curve (AUC) 0.671). Of patients in whom CV data were available, 86.0% (n = 757) had a CV/eGFR < 3.6 and 14.0% (n = 123) had a CV/eGFR ≥ 3.6. In multivariate logistic regression analysis, CV/eGFR ≥ 3.6 was the strongest predictor of 30-day all-cause mortality (odds ratio 5.06, 95% confidence interval [1.61–15.7], p = 0.004). Other independent predictors were procedural urgency (3.28 [1.04–10.3], p = 0.038) and being under general anaesthesia (4.81 [1.10–17.3], p = 0.023). CV/eGFR ≥ 3.6 was also independently associated with significantly increased odds of AKI (2.28 [1.20–4.17], p = 0.009) alongside significant non-left main stem coronary artery disease (2.56 [1.45–4.66], p = 0.001), and diabetes (1.82 [1.03–3.19], p = 0.037). In supplementary ROC curve analysis, a similar CV/eGFR cut point of 3.6 was found to be an excellent predictor for new RRT (AUC 0.833). Conclusions: In conclusion, a CV/eGFR ≥ 3.6 post-TAVI was found to be a strong predictor of 30-day mortality and AKI. The maximum contrast volume that can be safely administered in each patient without significantly increasing the risk of mortality and AKI can be calculated using this ratio. Full article

To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail. In response to the segmentation issues encountered in point clouds of small industrial components, a novel Point Transformer network incorporating a multiscale feature fusion strategy is proposed. This network extends the original Point Transformer architecture by integrating multiple global feature extraction modules and employing an upsampling module for contextual information fusion, thereby enhancing its modeling capabilities for intricate point cloud structures. The network is trained and tested on the self-constructed gear dataset, yielding promising results. Comparative analysis with the baseline Point Transformer network indicates a notable improvement of 1.1% in mean Intersection over Union (mIoU), substantiating the efficacy of the proposed approach. To further assess the method’s effectiveness, several ablation experiments are designed, demonstrating that the introduced modules contribute to varying degrees of segmentation accuracy enhancement. Additionally, a comparative evaluation is conducted against various state-of-the-art point cloud segmentation networks, revealing the superior performance of the proposed methodology. This research not only aids in quality control, structural detection, and optimization of precision industrial components but also provides a scalable network architecture design paradigm for related point cloud processing tasks. Full article

Satellite-based remote sensing enables the quantification of tropospheric NO₂ concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO₂ observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO₂ column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO₂ concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO₂ measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R² = 0.87) and precision (RMSE = 5.36 μg/m³). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO₂ mapping in cloudy and rainy areas. Full article

The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall–runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included the following: CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts; NSE, PCC, MAE, KGE, PBias, and RMSE; and three categorical metrics, CSI, FAR, and POD, for deterministic forecasts. The skill scores, CRPS, relative CRPS, CRPSS, and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE, and RMSE as well as for the categorical metrics. Forecast performance also progressively decreased with higher streamflow volumes. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country—they were generally better in the high-runoff-generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia, and the mid-west of Western Australia. We did not find any association between the model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resource management. Full article

Salinity, one of the major abiotic stresses in plants, significantly hampers germination, photosynthesis, biomass production, nutrient balance, and yield of staple crops. To mitigate the impact of such stress without compromising yield and quality, sustainable agronomic practices are required. Among these practices, seaweed extracts (SWEs) and microbial biostimulants (PGRBs) have emerged as important categories of plant biostimulants (PBs). This research aimed at elucidating the effects on growth, yield, quality, and nutrient status of two Greek tomato landraces (‘Tomataki’ and ‘Thessaloniki’) following treatments with the Ascophyllum nodosum seaweed extract ‘Algastar’ and the PGPB ‘Nitrostim’ formulation. Plants were subjected to bi-weekly applications of biostimulants and supplied with two nutrient solutions: 0.5 mM (control) and 30 mM NaCl. The results revealed that the different mode(s) of action of the two PBs impacted the tolerance of the different landraces, since ‘Tomataki’ was benefited only from the SWE application while ‘Thessaloniki’ showed significant increase in fruit numbers and average fruit weight with the application of both PBs at 0.5 and 30 mM NaCl in the root zone. In conclusion, the stress induced by salinity can be mitigated by increasing tomato tolerance through the application of PBs, a sustainable tool for productivity enhancement, which aligns well with the strategy of the European Green Deal. Full article

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers’ slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword “elderly person” exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques. Full article

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. Full article

This study aims to explore potential mechanisms of ambidextrous leadership (AL) in product innovativeness from the perspective of organizational agility (OA) and entrepreneurial orientation (EO) in firms operating in the artificial intelligence (AI) industry. A quantitative research method was used with 405 questionnaires, and the respondents were randomly selected from reputable databases. Structural equation modeling was employed to evaluate the model fit and conduct hypothesis testing. The findings suggest that ambidextrous leadership demonstrates a significant positive influence on product innovativeness and OA; also, through the mediating role of OA, it is possible to analyze both the direct and indirect relationships among the factors. Additionally, the moderating effect of EO on the intercorrelations among these factors was explored. This study enhances existing knowledge on leadership dynamics in the context of new product development, highlights the importance of adaptability in leadership, and sheds light on the interplay between OA, EO, and new product innovation. This study highlights the role of product innovativeness in sustainable AI product development. Enhanced product innovativeness not only sustains AI product development but also promotes environmental sustainability. This is achieved through the minimization of energy use, reduction in material requirements, and prevention of pollution. Firms are using these insights to develop sustainable and eco-friendly products, as well as create new market opportunities while reducing environmental impact. This research underscores the interconnectedness of factors in this study and sustainability, providing a new perspective on sustainable AI product development. Full article

A very important class of models widely used nowadays to describe and predict, at least in stochastic terms, the behavior of many-particle systems (where the word “particle” is not meant in the purely mechanical sense: particles can be cells of a living tissue, or cars in a traffic flow, or even members of an animal or human population) is the Kinetic Theory for Active Particles, i.e., a scheme of possible generalizations and re-interpretations of the Boltzmann equation. Now, though in the literature on the subject this point is systematically disregarded, this scheme is based on Markov Chains, which are special stochastic processes with important properties they share with many natural processes. This circumstance is here carefully discussed not only to suggest the different ways in which Markov Chains can intervene in equations describing the stochastic behavior of any many-particle system, but also, as a preliminary methodological step, to point out the way in which the notion of a Markov Chain can be suitably generalized to this aim. As a final result of the discussion, we find how to develop new very plausible and likely ways to take into account possible effects of the external world on a non-isolated many-particle system, with particular attention paid to socio-economic problems. Full article