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

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

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

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

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

Due to the diversity of organic molecular structures present in sage extract, sage extract is a promising potential source of a cheap and effective biodegradable green corrosion inhibitor for tinplate in 3% NaCl solution, which was evaluated in this study. HHP proved to be a new and emerging technology for the useful extraction of polyphenols from sage as a functional ingredient from natural sources. Analysis of variance among all tested independent factors (ethanol concentration, HHP parameters and temperature) revealed significant differences (p < 0.05) in total polyphenol content as well as for rosmarinic acid as the major phenolic compound in sage extract, while extraction time had no effect (p ˃ 0.05). The optimum HHP conditions (600 MPa, 30% ethanol, 60 °C and 5 min) gave a maximum extraction yield of total polyphenols of 3811.84 mg/100 g. Sage-leaf extracts were found to be a mixture of phenolic acids, namely rosmarinic and salvianolic acid K, epicatechin and luteolin-7-O-glucuronide glycoside. The corrosion results show that the sage extract at a concentration of 0.6 g/L in 3% NaCl is an effective corrosion inhibitor (93%), forming a passivation layer of sage extract consisting of organic compounds such as polyphenols on the surface of tinplate. Full article

The present study aimed to understand visitors’ perceptions of the ecosystem service functions of the AL-Sunut Forest Reserve, as well as their recreational activities. Here, the impact of respondents’ socioeconomic status on visitors’ perceptions was statistically analyzed by t-tests and ANOVA (SPSS software v26). Meanwhile, multiple regression analysis was conducted to identify the relationships between factors shaping respondents’ perceptions of the AL-Sunut Forest during recreational activities. A total of 441 visitors were randomly selected and questioned through a questionnaire survey during February and March 2020. The results showed that visitors believed the recreational forest site was important and valuable and were willing to revisit it. Visitors also demonstrated a particular understanding of the ecosystem services provided by the forest ecosystem. There were significant differences in perceptions of ecosystem services among visitors of different backgrounds. The findings indicated that 79% of participants responded positively towards the importance of environmental education compared with any other education. In addition, 90% of respondents believed that habitats and natural resources such as forests must be protected. The results of the recreational activities of visitors showed that they acquired the most benefits when experiencing picturesque scenery and walking. Overall, the present findings can pave the way for decision-makers to develop a unique plan focusing on forests to implement an exhaustive approach to assessing the value of ecosystem services while emphasizing the general public’s welfare. The study’s results can also contribute to the future management of the AL-Sunut Forest. Full article

Ketosis, especially its subclinical form, is frequently observed in high-yielding dairy cows and is linked to various diseases during the transition period. Although adipose tissue plays a significant role in the development of metabolic disorders, its exact impact on the emergence of subclinical ketosis (SCK) is still poorly understood. The objectives of this study were to characterize and compare the profiling of transcriptome and lipidome of blood and adipose tissue between SCK and healthy cows and investigate the potential correlation between metabolic disorders and lipid metabolism. We obtained blood and adipose tissue samples from healthy cows (CON, n = 8, β-hydroxybutyric acid concentration < 1.2 mmol/L) and subclinical ketotic cows (SCK, n = 8, β-hydroxybutyric acid concentration = 1.2–3.0 mmol/L) for analyzing biochemical parameters, transcriptome, and lipidome. We found that serum levels of nonesterified fatty acids, malonaldehyde, serum amyloid A protein, IL-1β, and IL-6 were higher in SCK cows than in CON cows. Levels of adiponectin and total antioxidant capacity were higher in serum and adipose tissue from SCK cows than in CON cows. The top enriched pathways in whole blood and adipose tissue were associated with immune and inflammatory responses and sphingolipid metabolism, respectively. The accumulation of ceramide and sphingomyelin in adipose tissue was paralleled by an increase in genes related to ceramide biosynthesis, lipolysis, and inflammation and a decrease in genes related to ceramide catabolism, lipogenesis, adiponectin production, and antioxidant enzyme systems. Increased ceramide concentrations in blood and adipose tissue correlated with reduced insulin sensitivity. The current results indicate that the lipid profile of blood and adipose tissue is altered with SCK and that certain ceramide species correlate with metabolic health. Our research suggests that disruptions in ceramide metabolism could be crucial in the progression of SCK, exacerbating conditions such as insulin resistance, increased lipolysis, inflammation, and oxidative stress, providing a potential biomarker of SCK and a novel target for nutritional manipulation and pharmacological therapy. Full article

The tumor microenvironment (TME) can aid tumor cells in evading surveillance and clearance by immune cells, creating an internal environment conducive to tumor cell growth. Consequently, there is a growing focus on researching anti-tumor immunity through the regulation of immune cells within the TME. Various bioactive compounds in traditional Chinese medicine (TCM) are known to alter the immune balance by modulating the activity of immune cells in the TME. In turn, this enhances the body’s immune response, thus promoting the effective elimination of tumor cells. This study aims to consolidate recent findings on the regulatory effects of bioactive compounds from TCM on immune cells within the TME. The bioactive compounds of TCM regulate the TME by modulating macrophages, dendritic cells, natural killer cells and T lymphocytes and their immune checkpoints. TCM has a long history of having been used in clinical practice in China. Chinese medicine contains various chemical constituents, including alkaloids, polysaccharides, saponins and flavonoids. These components activate various immune cells, thereby improving systemic functions and maintaining overall health. In this review, recent progress in relation to bioactive compounds derived from TCM will be covered, including TCM alkaloids, polysaccharides, saponins and flavonoids. This study provides a basis for further in-depth research and development in the field of anti-tumor immunomodulation using bioactive compounds from TCM. Full article

The gut microbiota plays a crucial role in the host’s metabolic processes. Many studies have shown significant changes in the gut microbiota of mammals during hibernation to adapt to the changes in the external environment, but there is limited research on the colonic epithelial tissue and gut microbiota of the wild chipmunks during hibernation. This study analyzed the diversity, composition, and function of the gut microbiota of the wild chipmunk during hibernation using 16S rRNA gene high-throughput sequencing technology, and further conducted histological analysis of the colon. Histological analysis of the colon showed an increase in goblet cells in the hibernation group, which was an adaptive change to long-term fasting during hibernation. The dominant gut microbial phyla were Bacteroidetes, Firmicutes, and Proteobacteria, and the relative abundance of them changed significantly. The analysis of gut microbiota structural differences indicated that the relative abundance of Helicobacter typhlonius and Mucispirillum schaedleri increased significantly, while unclassified Prevotella-9, unclassified Prevotellaceae-UCG-001, unclassified Prevotellaceae-UCG-003 and other species of Prevotella decreased significantly at the species level. Alpha diversity analysis showed that hibernation increased the diversity and richness of the gut microbiota. Beta diversity analysis revealed significant differences in gut microbiota diversity between the hibernation group and the control group. PICRUSt2 functional prediction analysis of the gut microbiota showed that 15 pathways, such as lipid metabolism, xenobiotics biodegradation and metabolism, amino acid metabolism, environmental adaptation, and neurodegenerative diseases, were significantly enriched in the hibernation group, while 12 pathways, including carbohydrate metabolism, replication and repair, translation, and transcription, were significantly enriched in the control group. It can be seen that during hibernation, the gut microbiota of the wild chipmunk changes towards taxa that are beneficial for reducing carbohydrate consumption, increasing fat consumption, and adapting more strongly to environmental changes in order to better provide energy for the body and ensure normal life activities during hibernation. Full article

Considering the escalating resistance to conventional antifungal medications, it is critical to identify novel compounds that can efficiently counteract this challenge. The purpose of this research was to elucidate the fungicidal properties of secondary metabolites derived from Arcangelisia flava, with a specific focus on their efficacy against Candida species. This study utilized a combination approach comprising laboratory simulations and experiments to discern and evaluate the biologically active constituents present in the dichloromethane extract of A. flava. The in vitro experiments demonstrated that compounds 1 (palmatine) and 2 (fibraurin) exhibited antifungal properties. The compounds exhibited minimum inhibitory concentrations (MICs) ranging from 15.62 to 62.5 µg/mL against Candida sp. Moreover, compound 1 demonstrated a minimum fungicidal concentration (MFC) of 62.5 µg/mL against Candida glabrata and C. krusei. In contrast, compound 2 exhibited an MFC of 125 µg/mL against both Candida species. Based on a molecular docking study, it was shown that compounds 1 and 2 have a binding free energy of −6.6377 and −6.7075 kcal/mol, respectively, which indicates a strong affinity and specificity for fungal enzymatic targets. This study utilized pharmacophore modeling and Density Functional Theory (DFT) simulations to better understand the interaction dynamics and structural properties crucial for antifungal activity. The findings underscore the potential of secondary metabolites derived from A. flava to act as a foundation for creating novel and highly efficient antifungal treatments, specifically targeting fungal diseases resistant to existing treatment methods. Thus, the results regarding these compounds can provide references for the next stage in antifungal drug design. Further investigation is necessary to thoroughly evaluate these natural substances’ clinical feasibility and safety characteristics, which show great potential as antifungal agents. Full article

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine. Full article

Red yeast rice dietary supplements have been proven to ameliorate hyperglycemia, but the mechanism was unclear. In this work, ankaflavin (AK) and monascin (MS), as typical pigments derived from red yeast rice, were found to exert noteworthy inhibitory ability against α-glucosidase, with an IC₅₀ of 126.5 ± 2.5 and 302.6 ± 2.5 μM, respectively, compared with acarbose (IC₅₀ = 341.3 ± 13.6 μM). They also exhibited mixed-type inhibition of α-glucosidase in vitro and caused fluorescence quenching through the static-quenching process. Molecular-docking studies indicated that AK and MS bind to amino acid residues outside the catalytic center, which induces structural changes in the enzyme, thus influencing its catalytic activity. The anti-glycation ability of Monascus-fermented products was evaluated, and they exhibited a high inhibition rate of 87.1% in fluorescent advanced glycation end-product formation at a concentration of 0.2 mg mL⁻¹, while aminoguanidine showed a rate of 75.7% at the same concentration. These results will be significant in broadening the application scope of Monascus pigments, especially AK and MS, in treating type 2 diabetes. Full article

Maintaining sufficient levels of plant nutrients in the soil and controlling certain heavy metals, which can be toxic to the environment, are critical to ensure sustainable agricultural production. The study aimed to assess the linkage of crop-specific agricultural practices established by farmers in the Kvemo Kartli region (Georgia) with metal accumulation in soils of agricultural lands being subject to influence from polluted irrigation water in the past. In particular, we tried to identify the primary sources of micro-nutrients, including iron (Fe), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn), and toxic elements such as cadmium (Cd) and lead (Pb), and the share of the contaminated irrigation water and other factors related to agricultural practices under different land uses, such as intensive and extensive arable farming, vineyards, orchards, and permanent pastures having the least disturbed soil. Based on principal component analysis, five primary sources were identified and categorized according to farmer interviews and previous studies conducted in the region. The results showed that increased concentrations of plant-available Cu, Zn, Cd, and Pb were mainly associated with irrigation water and intensive use of fungicides; Fe, Mn, and Ni were closely linked to several factors, such as the mineralogical composition of soils, minerals, and organic fertilizers inputs; and atmospheric deposition from diffuse sources, where exhausts from transport are probably the primary source. During our study, we attempted to differentiate irrigation water inputs from fungicides using simulation based on irrigation patterns and irrigation water quality on the one hand and fungicide application rates and their metal contents on the other. The simulation revealed that the intensive application of fungicides, especially in vineyards, is more significant in enriching soils with Cu and Zn than irrigation water. Identification of factorial dependences was supported by statistical analysis and application of several contamination assessment methods: contamination factor (CF), pollution load index (PLI), single-factor pollution index (PI), Nemerow’s comprehensive pollution index (PIN), enrichment factor (EF), and geo-accumulation index (Igeo). Applied environmental indices indicate that the soils under the former and existing vineyards are the most enriched with Cu and Zn, highlighting the significance of agricultural practices on heavy metal accumulations in the soils of agricultural lands. Full article

(1) Background: Social isolation, which has numerous adverse effects on health status, is prevalent among cancer patients. This review proposes to identify the influencing factors of social isolation among cancer patients. (2) Methods: Articles published in English or Chinese from six electronic databases before December 2023 were identified via a systematic search. A manual search was also performed. (3) Results: Twenty-eight studies were identified in this systematic review. The factors associated with social isolation can be summarized into the following categories: demographic characteristics, having cancer, health status, coping, social support and social interaction. Despite the heterogeneity, 20 factors were significantly associated with social isolation, including age, gender, comorbidity burden, education level, residence, medical insurance, occupation status, personality, race, smoking status, having children, not living alone, household income level, marital status, the role of primary caregiver, physical health status, mental health status, social health status, coping styles, and the level of social support and social interaction. (4) Conclusions: The systematic review showed that cancer patients’ social isolation was influenced by their demographic characteristics, cancer-related factors, physical condition, psychological status, social health status, coping styles, and level of social support and social interaction. In addition, future group intervention could be considered to improve social isolation. Full article

Background: Flatfeet in children are common, causing concern for parents due to potential symptoms. Technological advances, like 3D foot kinematic analysis, have revolutionized assessment. This review examined 3D assessments in paediatric idiopathic flexible flat feet (FFF). Methods: Searches focused on paediatric idiopathic FFF in PubMed, Web of Science, and SCOPUS. Inclusion criteria required 3D kinematic and/or kinetic analysis during posture or locomotion, excluding non-idiopathic cases, adult feet, and studies solely on pedobarography or radiographs. Results: Twenty-four studies met the criteria. Kinematic and kinetic differences between FFF and typical feet during gait were outlined, with frontal plane deviations like hindfoot eversion and forefoot supination, alongside decreased second peak vertical GRF. Dynamic foot classification surpassed static assessments, revealing varied movement patterns within FFF. Associations between gait characteristics and clinical measures like pain symptoms and quality of life were explored. Interventions varied, with orthoses reducing ankle eversion and knee and hip abductor moments during gait, while arthroereisis normalized calcaneal alignment and hindfoot eversion. Conclusions: This review synthesises research on 3D kinematics and kinetics in paediatric idiopathic FFF, offering insights for intervention strategies and further research. Full article

To address the issue of insufficient deep contextual information mining in the semantic segmentation task of multiple defects in concrete bridges, due to the diversity in texture, shape, and scale of the defects as well as significant differences in the background, we propose the Concrete Bridge Apparent Multi-Defect Segmentation Network (PID-MHENet) based on a PID encoder and multi-feature fusion. PID-MHENet consists of a PID encoder, skip connection, and decoder. The PID encoder adopts a multi-branch structure, including an integral branch and a proportional branch with a “thick and long” design principle and a differential branch with a “thin and short” design principle. The PID Aggregation Enhancement (PAE) combines the detail information of the proportional branch and the semantic information of the differential branch to enhance the fusion of contextual information and, at the same time, introduces the self-learning parameters, which can effectively extract the information of the boundary details of the lesions, the texture, and the background differences. The Multi-Feature Fusion Enhancement Decoding Block (MFEDB) in the decoding stage enhances the information and globally fuses the different feature maps introduced by the three-channel skip connection, which improves the segmentation accuracy of the network for the background similarity and the micro-defects. The experimental results show that the mean Pixel accuracy (mPa) and mean Intersection over Union (mIoU) values of PID-MHENet on the concrete bridge multi-defect semantic segmentation dataset improved by 5.17% and 5.46%, respectively, compared to the UNet network. Full article

Throughout the last decade, chatbots have gained widespread adoption across various industries, including healthcare, education, business, e-commerce, and entertainment. These types of artificial, usually cloud-based, agents have also been used in airport customer service, although there has been limited research concerning travelers’ perspectives on this rather techno-centric approach to handling inquiries. Consequently, the goal of the presented study was to tackle this research gap and explore potential use cases for chatbots at airports, as well as investigate travelers’ acceptance of said technology. We employed an extended version of the Technology Acceptance Model considering Perceived Usefulness, Perceived Ease of Use, Trust, and Perceived Enjoyment as predictors of Behavioral Intention, with Affinity for Technology as a potential moderator. A total of $n=191$ travelers completed our survey. The results show that Perceived Usefulness, Trust, Perceived Ease of Use, and Perceived Enjoyment positively correlate with the Behavioral Intention to use a chatbot for airport customer service inquiries, with Perceived Usefulness showing the highest impact. Travelers’ Affinity for Technology, on the other hand, does not seem to have any significant effect. Full article