Sustainable Development Goal 15: Life on Land (123095)

The rise in performance and health concerns presents a complex challenge to workplace well-being. While existing research highlights the positive impact of exposure to nature on well-being, there is a gap in supporting biophilic building designs in workplaces. This study addresses this issue by introducing a Type-1 Interval Fuzzy Set (IFS) framework to assess workplace health, and recommends Nature-Based Solutions (NbS) for enhancement. The proposed framework comprises two fuzzy subsystems targeting environmental comfort and occupational health that are integrated into a third subsystem to provide tailored NbS recommendations. A case study conducted in a coworking space validated the framework’s practical application, revealing significant improvements in environmental and occupational conditions. Key findings include a reduction in absenteeism, enhancement in employee satisfaction, and creation of more comfortable and restorative environments. Recommendations, such as green walls, indoor gardens, and natural light filters, have been shown to effectively mitigate discomfort and promote well-being. The framework demonstrated flexibility in managing uncertainties, optimizing resource allocation, and providing targeted interventions for workplace conditions. These results highlight the framework’s potential as a robust decision-support tool for improving workplace health and well-being through systematic and evidence-based strategies. Full article

Urmia Lake (UL) is the sixth-largest saltwater lake in the world; however, there is a dearth of geotechnical studies on this region. Geotechnical characteristics of a site are considered important from different engineering perspectives. In this research, the results of 255 laboratory tests and the data of 55 in situ tests were used to determine the geotechnical properties of sediment in UL. The changes of parameters in depth are presented in this study. The results indicate that compressibility, initial void ratio, water content, over-consolidated ratio (OCR), and sensitivity have larger values near the lake bed. Moreover, increasing the sediment depth leads to significant reductions in these values. According to the sediment strength analysis through the vane shear and standard penetration tests and the unit weight of sediments, there is an increasing trend caused by the increased depths of layers. Diverse applied correlations are proposed and can be used as preliminary estimates in similar types of sediments in engineering projects as well as scientific studies. Furthermore, undrained shear strength and compression index trends in depth and the Su/σ’_v Curve against OCR are compared with the literature, and the results reveal similar trends in similar sediments. The main minerals identified in these sediments include calcite, dolomite, quartz, calcium chloride, and halite. The salinity of the lake water is caused by the presence of calcium chloride and halite minerals. The Cao factor observed in chemical compounds can have a significant impact on the cohesion of the soil particles. This research provides comprehensive information on the geotechnical characteristics of UL. Moreover, the results of this study show that UL Sediments are soft and sensitive, especially in shallow depths, and they contain a significant amount of organic content; therefore, it is recommended to use suitable improvement methods in future geotechnical and structural designs. This study and similar surveys can help prepare the groundwork for designing safer marine structures. Full article

Urbanized riverine cities in southern Asian developing countries face significant challenges in understanding the spatiotemporal thermal impacts of land use/land cover (LULC) changes driven by rapid urbanization and climatic variability. While previous studies have investigated factors influencing land surface temperature (LST) variations, gaps persist in integrating Landsat imagery (7 and 8), meteorological data, and Geographic Information System (GIS) tools to evaluate the thermal effects of specific LULC types, including cooling and warming transitions, and their influence on air temperature under variable precipitation patterns. This study investigates LST variations in Islamabad, Pakistan, from 2000 to 2020 using quantile classification at three intervals (2000, 2010, 2020). The thermal contributions of each LULC type across the LST-based temperature classes were analyzed using the Land Contribution Index (LCI). Finally, Warming and Cooling Transition (WCT) maps were generated by intersecting LST classes with 2000 as the baseline. Results indicated a rise in LST from 32.39 °C in 2000 to 45.63 °C in 2020. The negative LCI values revealed that vegetation and water bodies in lower temperature zones (Ltc_1 to Ltc_3) contributed to cooling effects, while positive LCI values in built-up and bare land areas in higher temperature zones (Ltc_5–Ltc_7) exhibited warming effects. The WCT map showed a general warming trend (cold-to-hot type) from 2000 to 2020, particularly in newly urbanized areas due to a 49.63% population increase, while cooling effects (hot-to-cold type) emerged in the newly developed agricultural lands with a 46.46% rise in vegetation. The mean annual air temperature gap with LST narrowed from 11.55 °C in 2000 to 2.28 °C in 2020, reflecting increased precipitation due to increasing yearly rainfall from 982.88 mm in 2000 to 1365.47 mm in 2020. This change also coincided with an expansion of water bodies from 2.82 km² in 2000 to 6.35 km² in 2020, impacting the local climate and hydrology. These findings highlight the importance of green spaces and water management to mitigate urban heat and improve ecological health. Full article

This study investigates the impact of professional competencies (moral, knowledge, and skill) on the occupational identity of government-supported preschool teacher trainees and examines the moderating role of support from significant others, including family, peers, and mentors. A quantitative research methodology was employed, involving a cross-sectional survey of 193 publicly funded teacher trainees. Validated scales were used to measure professional competencies, occupational identity, and perceived support. The results revealed a strong positive correlation between professional competencies and occupational identity (r = 0.61, p < 0.01), supporting the hypothesis that higher competency levels enhance trainees’ professional identity. Furthermore, support from significant others moderated this relationship, with a higher level of support amplifying the positive association (β = 1.412, p < 0.01). These findings highlight the interconnected nature of professional competencies and social support in shaping occupational identity. Based on these results, it is recommended that teacher training programs integrate targeted competency-building strategies alongside structured support systems to enhance professional identity formation. These initiatives are critical for fostering sustainable professional development and improving the quality and stability of preschool education. Full article

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. Full article

Urban water bodies play a critical role in regulating urban climate, mitigating the urban heat island effect, and enhancing ecological environments. This study focuses on five typical heat island cities in Zhejiang Province, systematically analyzing the cooling effects of urban water bodies. Specifically, the study divides urban buffer zones into basic analytical units based on the urban road network and performs land surface temperature inversion and land use classification using the Google Earth Engine platform. Six representative morphology indicators of water bodies are selected, and the contributions of these indicators to the cooling effects of urban water bodies are evaluated using a Gradient Boosting Decision Tree regression model. Additionally, optimization strategies for water bodies in different cities are proposed. The results show the following: (1) Water bodies in central urban areas generally exhibit significant cooling effects, with the average land surface temperature reduction in water bodies exceeding 5.13 °C compared to built-up areas in all cities. (2) The average land surface temperature in urban buffer zones is generally higher than that in central urban areas, with a temperature difference of at least 0.63 °C. (3) In Huzhou and Jiaxing, the high-temperature and low-temperature zones are relatively concentrated, while in Jinhua, Quzhou, and Shaoxing, a more interspersed distribution of high-temperature and low-temperature zones is observed, reflecting a higher spatial heterogeneity. (4) Among the water body morphology indicators, the water edge density, the proportion of landscape area occupied by water patches, the largest patch index of water, and the water landscape shape index exert a relatively larger impact on cooling effects. These findings provide scientific guidance for optimizing the spatial layout of water bodies in urban buffer zones and improving urban thermal environments. Full article

In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the Qinglongshan Irrigation Area (QLS). We aimed to assess the impacts of climate and land use (LULC) changes between 1980 and 2020 on several hydrological parameters in the QLS, including actual evapotranspiration (ET), soil water (SW), soil recharge to groundwater (PERC), surface runoff (SURQ), groundwater runoff (GWQ), and lateral runoff (LATQ). We predicted the trends in hydrological factors from 2021 to 2050. Based on the S1 scenario, the precipitation and the paddy field area decreased by 42.28 mm and 1717.65 km², respectively; hydrological factors increased by 91.53, 104.28, 50.66, 21.86, 55.93, and 0.79 mm, respectively, in the QLS. Climate changes contributed 6.10%, −7.58%, −54.11%, 26.90%, −121.17%, and −31.66% to changes in hydrological factors, respectively; LULC changes contributed −2.19%, 3.63%, 11.61%, −2.93%, 25.89%, and 16.86%, respectively; and irrigation water volume changes contributed 96.09%, 103.95%, 142.50%, 76.03%, 195.28%, and 114.80%, respectively. Irrigation and water intake were the main factors affecting the changes in hydrological elements. This was followed by climatic changes and LULC. In natural development scenarios, the QLS is anticipated to face challenges, including increased actual ET, reduced seepage and groundwater contribution, and declining groundwater levels. Full article

This article analyses how energy sufficiency can be applied in tourism destinations. It begins by highlighting the importance of decarbonizing tourism for climate action, given the sector’s high CO₂ emissions. Energy sufficiency, a key pillar of the energy transition, is defined as the voluntary reduction in energy demand within climate and CO₂ emission constraints. The study investigates how stakeholders interpret and frame this concept, by focusing on strategies that align with the public image of the resort to reduce its energy requirements. The methodology includes semi-structured interviews with key players in Verbier—Val de Bagnes, focus groups with cooperation partners, participant observations at local meetings and events, and analysis of local documentation. Research was undertaken using living lab methods. The results reveal five main categories and eleven subcategories in which local stakeholders frame sufficiency, including oppositional framings, off-framings, selective framings, institutional framings, and disempowering framings. The article concludes with recommendations to reframe sufficiency in order to align with the destination’s strategy and representations. While energy sufficiency is often overshadowed by narratives of economic growth, technological innovation, and material abundance, it appears crucial to reframe these narratives and integrate post-growth strategies that prioritize sustainability in tourism planning. Full article

Currently, methods are being sought to reduce the effects of drought. The conducted research tested the effect of potassium polyacrylate, β-cyclocitral, and Rhizophagus irregularis on the development of maize (Zea mays L.). The first of the substances mentioned was mixed with the soil; the others were used as seed dressings. The effect of substances and microorganisms on the height and weight of plants, chlorophyll fluorescence and the gas exchange between the soil and the atmosphere was tested in greenhouse conditions. Plant development was tested at optimal soil moisture levels and in drought conditions. Field experiment determined the effect of the abovementioned preparations on the height of maize, the level of grain yield and seed parameters. The hydrothermal index was calculated for the entire vegetative season of plants. All preparations used had a positive effect on the development of test plants. Significant improvement was found for many parameters compared to the control combination plants, including maize weight and grain yield. This was confirmed for various soil moisture conditions. An increase in grain yield was noted by 0.6–1.3 t ha⁻¹ compared to the control. The tested substances and microorganisms may, therefore, be a good solution for protecting plants against the effects of drought. Full article

Background/Objectives: Benralizumab is an anti-IL-5 receptor alpha monoclonal antibody that induces the near-complete depletion of eosinophils. This study aimed to evaluate the long-term safety and effectiveness of benralizumab in patients with severe eosinophilic asthma (SEA) over an extended 48-month follow-up period, offering one of the longest real-world perspectives available. Methods: This was a single-arm, retrospective, observational, multicenter study involving 123 SEA patients treated with benralizumab at a dosage of 30 mg every 4 weeks for the first 3 doses and then every 8 weeks. The safety endpoints focused on the frequency and nature of adverse events and the likelihood that they were induced by benralizumab. The efficacy endpoints focused on lung function, asthma exacerbations and control, and oral corticosteroid use. Results: Benralizumab, consistent with its mechanism of action, led to the rapid and nearly complete depletion of eosinophils. In total, 26 adverse events (21.1%) were observed, with 1.6% related to the treatment and 0.8% categorized as serious (vagal hypotension). Bronchitis was the most common unrelated adverse event (15.4%), occurring between months 36 and 38. Importantly, benralizumab effectiveness and safety were maintained consistently across the 48-month duration, resulting in significant improvements in lung function and reductions in oral corticosteroid use and exacerbation frequency. Conclusions: Benralizumab demonstrated a favorable safety profile, comparable to previously published studies, with perdurable effectiveness in controlling SEA and reducing oral corticosteroid use. Finally, this study provides evidence that near-complete eosinophil depletion does not increase long-term safety risks and supports benralizumab as a reliable treatment option for SEA patients. Full article

Mulching practices have demonstrated the potential to increase crop yields and resource utilization efficiency. However, the response of different crops with various growth stages to different mulching practices remains unclear, particularly in the rainfed agroecosystem. Therefore, a two-year field experiment (2013–2015) of different crops (wheat, maize, and potato) was conducted to evaluate the effects of three different mulching treatments: straw strip mulching (SM), plastic film mulching (PM), and conventional planting without mulching as the control (CK), on soil moisture and temperature, evapotranspiration (ET), water use efficiency (WUE), crop yield and economic benefits on the Loess Plateau. The results indicated that both mulching practices significantly increased the soil water content (SM: 4.3% and PM: 3.6%) compared to CK. However, the effects on soil temperature varied between mulching practices, PM increased soil temperature by 4.9% compared to CK, while SM decreased it by 6.3%. The improved soil hydrothermal conditions, characterized by favorable temperatures and higher soil water status would lead to a higher crop daily growth rate (5.3–49.8%), as well as greater dry matter accumulation (4.7–36.7%). Furthermore, mulching practice (SM and PM) has a significant influence on crop yield and its components of various crops, as well as WUE. The mean grain yield of SM and PM was, respectively, increased by 11.4% and 27.1% for winter wheat, compared to CK, 1.8% and 24.3% for spring maize, and 23.0% and 13.9% for potato, respectively. Compared to CK, PM yielded a higher net economic benefit and WUE for winter wheat and spring maize, while SM presented the best economic benefit and WUE for potato. In conclusion, a comprehensive analysis of crop yield, economic benefits, and resource utilization efficiency suggests that straw strip mulching for potato is a more sustainable environmentally friendly mulching practice, recommended for rainfed farming systems on the Loess Plateau and areas with similar climatic conditions. Full article

This article addresses the study of the governance process and the influence of urban heat islands in the city of Quevedo on the coast of Ecuador, and thus contributes to the production of technical and scientific information with a view to their mitigation. To identify the UHI pattern and visualize the temperature distribution on the soil surface, light intensity patterns on the soil surface are identified by the digital processing of the Landsat 7 ETM image. The NDVI, NDSI, and SAVI indices were also calculated, and the AQI was subsequently obtained using a weighted numerical cross-tabulation. The results show that the NDVI and SAVI indicators are correlated with each other and present a strong and positive classification with the neighborhoods and special areas in which there is a high proportion of vegetation, while the NSI and SAVI indicators are positively correlated with the areas. in which there is a greater proportion of built-up areas and roads. From a comprehensive analysis of the reviewed indicators, the authors derived an environmental quality index that explains the beneficial effects of vegetation and negatively explains the detrimental effects of a city covered in cement. Full article

The water environment in estuaries is a crucial factor affecting the biodiversity and self-purification capacity of coastal zones. This study focuses on Sanmen Bay as an example to study the characteristics and temporal variations of the water environment in the turbid coastal waters on the East China Sea coast. The field data of hydrodynamics and water environment from 2018 to 2023 including different seasons in the bay were collected and analyzed. We analyzed the correlation between water environmental factors and sediment and explored the impact of sediment mixing layers on the water environment. Field data indicate that water temperature, dissolved oxygen content, and suspended sediment concentration (SSC) vary seasonally. In summer, the water temperature and SSC are the highest; in autumn, the dissolved oxygen content is the highest. Salinity and pH values showed little variation from 2018 to 2023. The concentration of oils in sediments across the entire area within Sanmen Bay varied from 2018 to 2023, which decreased from (30.6–92.2) × 10⁻⁶ mg/L to below 10⁻⁶ mg/L. Correlational analysis indicates that dissolved oxygen concentration and heavy metal content were correlated with sediment in 2018, with correlation coefficients of approximately 0.5. Sediments impact the water environment through changing stratification and mixing due to suspended particulate matter and through changing water environment parameters (e.g., heavy metal) due to bed sediment erosion. The bulk Richardson number in most areas is larger than 0.25. These results indicate that sediment impacts heavy metals in Sanmen Bay. In highly turbid waters, sediments are more likely to adsorb heavy metals and other pollutants, thereby impacting water quality. Full article

The most effective way to solve urban traffic congestion in mega cities is to develop rail transit, which is also an important strategy for sustainable urban development. Improving the service performance of rail transit equipment is the key to ensuring the sustainable operation of urban rail transit. Automatic fare collection (AFC) is an indispensable system in urban rail transit. AFC directly serves passengers, and its condition directly affects the sustainability and safety of urban rail transit. This study proposes remaining useful life (RUL) prediction framework for AFC systems. Firstly, it proposes the quantification of AFC health state based on health degree, and proposes a health state assessment method based on digital analog fusion, which compensates for the shortcomings of single data-driven or model driven health methods. Secondly, it constructs a multi feature extraction method based on multi-layer LSTM, which can capture long-term temporal dependencies and multi-dimensional feature, overcoming the limitation of low model accuracy because of the weak data features. Then, the SSA-XGBoost model for AFC RUL prediction is proposed, which effectively performs global and local searches, reduces the possibility of overfitting, and improves the accuracy of the prediction model. Finally, we put it into practice of the AFC system of Shanghai Metro Line 10. The experiment shows that the proposed model has an MSE of 0.00111 and MAE of 0.02869 on the test set, while on the validation set, MSE is 0.00004 and MAE is 0.00659. These indicators are significantly better than other comparative models such as XGBoost, random forest regression, and linear regression. In addition, the SSA-XGBoost model also performs well on R-squared, further verifying its effectiveness in prediction accuracy and model fitting. Full article

The present research examined the “production–living–ecological space” (“PLES”) by using land use data, the ecological and environmental quality index model, growth and reduction based on the spectrum, the center of gravity migration model, and the optimal parameter geoprobe model to further evaluate adjustments to ecological quality and the driving mechanisms. The findings indicate that (1) spanning the years 2000–2020, the production space and living area in Panjin increased to 2093 km² and 380 km² respectively, and the ecological area of forest land and water area showed a decreasing trend. (2) The center of gravity of the urban living space and the industrial and mining production space shifted significantly. (3) The negative effect of the contribution rate was of greater value than the positive effect. Thus, it can be concluded that the negative effect of “PLES” is greater than the positive one. (4) The results for this region showed that the mean annual temperature was the strongest explanation for the spatial variation in natural factors, and that social factors such as population density also had a strong effect, so an interaction analysis was carried out to analyze the interaction between the two factors, which showed that the relationship between mean annual temperature and density of population had the strongest explanatory power. Full article

The indiscriminate use of plastic products and their inappropriate management and disposal contribute to the increasing presence and accumulation of this material in all environmental zones. The chemical properties of plastics and their resistance to natural degradation lead over time to the production of microplastics (MPs) and nanoplastics, which are dispersed in soil, water, and air and can be absorbed by plants, including those grown for food. In agriculture, MPs can come from many sources (mulch film, tractor tires, compost, fertilizers, and pesticides). The possible effects of this type of pollution on living organisms, especially humans, increase the need to carry out studies to assess occupational exposure in agriculture. It would also be desirable to promote alternative materials to plastic and sustainable agronomic practices to protect the safety and health of agricultural workers. Full article

Ticks are a major concern for both animal and human health, as they are primary vectors of infectious pathogens. This study focused on ticks found in a nature reserve in southern Italy, highly frequented for recreational activities and inhabited by wild boars. Using molecular techniques, 214 ticks, including questing ticks and those removed from wild boars, were examined for tick-borne pathogens (TBPs), with a focus on zoonotic pathogens. Six tick species were identified: Hyalomma lusitanicum, Rhipicephalus pusillus, Rh. sanguineus s.l., Rh. bursa, Rh. turanicus, and Dermacentor marginatus, several of which are known vectors of zoonotic pathogens. Overall, 14% of ticks were positive for TBPs, mainly bacteria of Rickettsia genus. Molecular analyses detected Rickettsia slovaca, R. massiliae, Candidatus R. shennongii, R. conorii, R. felis, and R. barbariae. Additionally, single detections of Coxiella burnetii and Theileria annulata were recorded. Phylogenetic analyses were conducted on Rickettsia sequences. The range of ticks and TBPs present in this area highlights potential public health concerns. New associations between tick species and TBPs were documented, though vector roles need further investigation. The study highlights the importance of monitoring tick populations in both rural and urban environments to protect public health and prevent tick-borne disease spreading. Full article

Host–parasitoid systems are an essential area of research in ecology and evolutionary biology due to their widespread occurrence in nature and significant impact on species evolution, population dynamics, and ecosystem stability. In such systems, the host is the organism being attacked by the parasitoid, while the parasitoid depends on the host to complete its life cycle. This paper investigates the effect of parasitoid aggregation attacks on a host in a host–parasitoid model with self-diffusion on two-dimensional coupled map lattices. We assume that in the simulation of biological populations on a plane, the interactions between individuals follow periodic boundary conditions. The primary objective is to analyze the complex dynamics of the host–parasitoid interaction model induced by an aggregation effect and diffusion in a discrete setting. Using the aggregation coefficient k as the bifurcating parameter and applying central manifold and normal form analysis, it has been shown that the system is capable of Neimark–Sacker and flip bifurcations even without diffusion. Furthermore, with the influence of diffusion, the system exhibits pure Turing instability, Neimark–Sacker–Turing instability, and Flip–Turing instability. The numerical simulation section explores the path from bifurcation to chaos through calculations of the maximum Lyapunov exponent and the construction of a bifurcation diagram. The interconversion between different Turing instabilities is simulated by adjusting the timestep and self-diffusion coefficient values, which is based on pattern dynamics in ecological modeling. This contributes to a deeper understanding of the dynamic behaviors driven by aggregation effects in the host–parasitoid model. Full article

The extensive development of construction, in which cement concrete remains the key composite, enforces the need for particular environmental concerns. This applies to aspects, including ecological challenges in the cement industry and the rational use of natural construction aggregates. This review article focuses on new trends in the use of waste aggregate, with particular emphasis on concrete recycled aggregate and waste sands. The state of the art was analysed, including many years of own studies on modification of properties of waste aggregate and concrete composites made from it. It was assessed that among possible ways of quality improvement of RCA, the most promising for the macro scale seems to be carbonation, unlike biodeposition. The latter, novel and undoubtedly interesting from a scientific viewpoint, has not been studied sufficiently, and the real obstacle is the cost of its implementation in practice. Multi-recycling, the pioneering proposal of recycled concrete aggregate management, can be viewed only in the ecological context for the moment. The use of waste sands from hydroclassification combined with steel fibres is the closest to implementation for constructional purposes in engineering practice. Full article

Polyethylene mulch film (PEM) is widely utilized in garlic cultivation, significantly enhancing garlic yield. However, the prolonged use of this material leads to serious environmental issues that adversely affect soil health and plant growth. To promote the adoption of biodegradable mulch film (BDM) in garlic cultivation, we investigate the effects of BDMs with thicknesses of 0.006 mm, 0.008 mm, and 0.010 mm on garlic growth and soil properties, comparing them with the commonly used PEM 0.008 mm. The results indicated that the agronomic traits of garlic were significantly improved under both PEM and BDMs compared to no mulching, with yield increases ranging from 75.42% to 90.39%. The highest garlic yield was observed with the BDM 0.008 mm. Most above-ground agronomic traits of garlic did not exhibit significant differences between PEM and BDMs, although a few traits showed slight increases under the BDMs 0.008 mm and 0.010 mm. The quality characteristics of garlic bulbs, including the contents of soluble sugar, vitamin C, and allicin, did not differ significantly between PEM and BDMs. Soil temperature was significantly higher under both PEM and BDMs compared to no mulching. In comparison to PEM 0.008 mm, the application of BDMs 0.006 mm and 0.008 mm significantly enhanced potassium availability in the soil. Furthermore, the activities of catalase, phosphatase, and invertase were notably increased under the BDM 0.008 mm, suggesting that this type of mulch could improve the physicochemical properties of the soil. Additionally, the BDM 0.008 mm remained intact throughout the low-temperature overwintering period, began to partially degrade as temperatures rose in March and April, and exhibited considerable fragmentation during the maturity and harvest periods of garlic. Its degradation rate was well aligned with the growth requirements of garlic. Taken together, these findings suggested that the BDM 0.008 mm is particularly effective, resulting in significant yield increase and an appropriate degradation rate. These results provided a valuable reference for the selection and application of BDM in garlic cultivation. Full article

A type of aquaculture pond called a dike-pond system is distributed in the low-lying river delta of China’s eastern coast. Along with the swift growth of the coastal economy, the water surfaces of the dike-pond system (WDPS) play a major role attributed to pond aquaculture yielding more profits than dike agriculture. This study aims to explore the performance of deep learning methods for extracting WDPS from high spatial resolution remote sensing images. We developed three fully convolutional network (FCN) models: SegNet, UNet, and UNet++, which are compared with two traditional methods in the same testing regions from the Guangdong–Hong Kong–Macao Greater Bay Area. The extraction results of the five methods are evaluated in three parts. The first part is a general comparison that shows the biggest advantage of the FCN models over the traditional methods is the P-score, with an average lead of 13%, but the R-score is not ideal. Our analysis reveals that the low R-score problem is due to the omission of the outer ring of WDPS rather than the omission of the quantity of WDPS. We also analyzed the reasons behind it and provided potential solutions. The second part is extraction error, which demonstrates the extraction results of the FCN models have few connected, jagged, or perforated WDPS, which is beneficial for assessing fishery production, pattern changes, ecological value, and other applications of WDPS. The extracted WDPS by the FCN models are visually close to the ground truth, which is one of the most significant improvements over the traditional methods. The third part is special scenarios, including various shape types, intricate spatial configurations, and multiple pond conditions. WDPS with irregular shapes or juxtaposed with other land types increases the difficulty of extraction, but the FCN models still achieve P-scores above 0.95 in the first two scenarios, while WDPS in multiple pond conditions causes a sharp drop in the indicators of all the methods, which requires further improvement to solve it. We integrated the performances of the methods to provide recommendations for their use. This study offers valuable insights for enhancing deep learning methods and leveraging extraction results in practical applications. Full article

Background: Hepatitis B (HBV) and Delta (HDV) virus infections pose critical public health challenges, particularly in Romania, where HDV co-infection is underdiagnosed. Methods: This study investigates the epidemiology, risk factors, and clinical outcomes of HBV/HDV co-infection in vulnerable populations, leveraging data from the LIVE(RO2) program. Conducted between July 2021 and November 2023, the program screened 320,000 individuals across 24 counties, targeting socially disadvantaged groups such as rural residents, the Roma community, and those lacking health insurance. Results: Among 6813 hepatitis B surface antigen (HBsAg)-positive individuals, HDV antibody prevalence was 4.87%, with active replication confirmed in 75.6% of HDV-positive cases. Regional disparities emerged, with higher HDV prevalence and replication rates in the Eastern region compared to the South. HDV-positive individuals were more likely to be younger, male, and from rural or socioeconomically disadvantaged backgrounds. Clinically, HDV co-infection correlated with increased liver stiffness, advanced fibrosis stages, and lower steatosis levels compared to HBV mono-infection. Psychiatric comorbidities were more prevalent among HDV-positive patients, highlighting the need for integrated care. Conclusions: This study underscores the urgent need for targeted public health interventions, including enhanced screening, education, and access to novel antiviral therapies like bulevirtide to address the significant burden of HBV/HDV co-infection in Romania. Full article

Background/Objectives: This paper examines hunger over time to analyze how food insecurity is impacted by reduced income, including household funding from the government. Federal policies and community-based programs have the ability to prevent increases in food insecurity, particularly for populations that have risk factors, such as households with children; single-parent households; low-income households, especially those in rural areas; Black and Hispanic households; and, households experiencing economic hardships. Methods: This study is bas ed on a cross-sectional survey that was administered in 2018 and 2019 to food pantry clients, an already food insecure population accessing resources, in Eastern Massachusetts. Baseline surveys were matched with a 3-month follow-up survey (n = 308) and multinomial logistic mixed effect models were used to analyze the association between change in household hunger and change in household income. Results: This study found that participants who experienced decreased income compared to no income change from baseline to follow-up had 2.16 times the odds (95% CI: 1.05, 4.46) of experiencing increased household hunger compared to no change in hunger from baseline to follow-up, after adjusting for all other covariates. Conclusions: Food insecurity in the United States remained stable during the beginning of COVID-19, despite prevalence of reduced household income. The expanded government benefits that were implemented early in the pandemic contributed to total household income, which prevented increased food insecurity. Increased food insecurity after the removal of benefits starting in 2022 indicates the importance of continuing support established during times when consistent income is compromised to prevent a delayed rise in food insecurity. Full article

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives. Full article

The dynamics of territorial planning, the management of its tourism products, and the monitoring of demand flows and their impact on the territorial structure (social, economic and environmental) require tools that support the acquisition of reliable quantitative data, as far as possible in real time, that are easy to manage and allow immediate analysis. In the case of structures and equipment anchored in the nature tourism segment, in particular hiking trails, in addition to determining the demand indices in a network of hiking trails and understanding their territorial and temporal dynamics, the data collected through automatic counters is a crucial tool to support territorial management and evaluate the patterns and flows of tourist demand. Based on these assumptions, this research seeks to analyse demand data observed on eleven hiking trails in the Historic Villages of Portugal, collected through automatic monitoring systems (counters). In four years, between 2020 and 2023, the trails analysed generated a demand of almost 190,000 passages, which translates into an annual average of 47,500 passages in the tourism product “Historic Villages of Portugal” (more than 4800 passages for each trail), mostly in the spring and autumn months, mainly on weekends. Full article

In recent years, the rapid development of the construction economy in Foshan City has led to increasingly apparent geological phenomena of ground subsidence, causing damage to buildings and infrastructures, lowering groundwater level, soil degradation, and environmental pollution. These issues have severely constrained the economic development of Foshan City. This paper utilized SBAS-InSAR technology to monitor ground deformation in Foshan City over the 2017–2022 period and identified 214 potential subsidence hazards. Additionally, integrating extensive surface deformation data from a long time series establishes a risk assessment index system for ground subsidence, primarily driven by construction activities. Further, the mechanism of the ground subsidence was analyzed systematically. Meanwhile, introducing the analytic hierarchy process method (AHP) to assess the risk of Foshan City, the results showed that the proportion of area from high-risk to low-risk zones is 8.55%, 8.64%, 11.79%, 22.38% and 48.64%, respectively. Moreover, this paper proposed corresponding measures to prevent and control ground subsidence. The evaluation results can provide a scientific reference for future geological disaster prevention and management in Foshan City. Full article

Cities in cold regions face significant challenges, including high carbon emissions, intense energy use, and outdated energy structures, making them critical areas for achieving carbon neutrality and sustainable development. While studies have explored the impact of spatial structures on urban carbon emissions, the effects of multi-scale spatial structures remain insufficiently understood, limiting effective spatial planning strategies. This research examines Changchun, a city in a severe cold region, using data from 2012 to 2021, including road networks, land use, nighttime light, and energy statistics. Employing spatial syntax, landscape pattern indices, random forests, and segmented linear regression, this research establishes a carbon emission translation pathway to analyze the nonlinear effects of multi-scale spatial structures. Findings reveal a 26.70% annual decrease in carbon emissions, with winter emissions 1.84 times higher than summer ones. High-emission zones have shifted from industrial areas to transportation, commercial, and residential zones, reflecting growing seasonal variability and structural changes. Spatial complexity increased while connectivity declined. Multi-scale analysis identified a “decrease–increase–decrease” pattern, with macro-scale centrality declining and micro-scale hierarchy rising. These results provide both theoretical and practical guidance for urban planning in cold regions, supporting early carbon neutrality and long-term sustainable development goals. Full article

In Northeast China, the establishment of irrigated paddy fields manifests on soil characterized as upland soils. However, the implications of soil conversion from upland soil to paddy soil for soil aggregates, soil organic carbon (SOC), and enzyme activity within soil aggregates remain poorly understood. Exploring the repercussions of soil conversion on SOC is paramount in delineating enhanced strategies for ameliorating soil structure and bolstering organic carbon sequestration within terrestrial ecosystems. Therefore, this study aimed to quantify the impact of land use modifications on SOC content and enzyme activity within soil aggregates. In this study, paddy (rice field) and upland (maize field) plots were selected from Mollisols in Northeast China, which is characterized by akin soil type, level topography, and climatic conditions. The results indicated that microaggregates represented the predominant fraction in both land use types, ranging from 36.96% to 48.99%, with a notably higher proportion in paddy soil compared to upland soil. After 40 years of rice cultivation, a significant decrease of 9.90% and 2.97% was observed in mean weight diameter and geometric mean diameter, respectively. In paddy soils, the SOC content in aggregates of varying sizes had the following order: macroaggregates (26.41 g kg⁻¹) < microaggregates (21.91 g kg⁻¹) < silt + clay (15.55 g kg⁻¹) fractions. Similarly, in upland soil, the highest SOC content was found in macroaggregates, with the following sequence: macroaggregates (21.67 g kg⁻¹) < microaggregates (17.44 g kg⁻¹) < silt + clay (15.03 g kg⁻¹) fractions. β-glucosidase (BG) displayed the highest enzyme activities, with average values of 95.99 nmol h⁻¹ g⁻¹ in paddy soil and 85.34 nmol h⁻¹ g⁻¹ in upland soil. Macroaggregate fractions exhibited the highest BG activity in both soil types (paddy: 112.49 nmol h⁻¹ g⁻¹, upland: 96.71 nmol h⁻¹ g⁻¹). In conclusion, the conversion from upland fields to paddy fields changes the occurrence mechanism of SOC in the aggregate, which is an important way of sustainable C sequestration in cropland ecosystems. Full article

Urbanization is a major driver of land-use change, with significant implications for sustainable development in rapidly growing cities. In this study, we assess the spatiotemporal patterns of urban expansion in Lucknow, India, from 1991 to 2021, using geospatial techniques and Landsat satellite imagery. The key objective is to analyze urban growth patterns, identify contributing factors, and evaluate the sustainability implications of these trends. Results reveal a nearly fivefold increase in built-up area, from 53.86 km² in 1991 to 261.45 km² in 2021. Edge development accounted for 55.99% of total urban expansion from 1991 to 2001, while ribbon development increased from 18.06% to 21.03% by 2021. Leapfrog development, considered unsustainable, rose from 12.35% in 1991 to 17.35% in 2021. Compared to previous studies, findings highlight the significant influence of transportation infrastructure and industrialization on urban sprawl, particularly in the northeastern and northwestern zones. This study contributes by offering a zone-wise analysis of growth patterns, which provides deeper insights into the dynamics of urban expansion. The findings are crucial for urban planners, as they suggest the need for more stringent zoning regulations and targeted infrastructure investments to manage future growth sustainably while minimising environmental impact. Full article

Prince Mohammed bin Salman Royal Reserve (PMBSRR or the Reserve) is located in the northwest of Saudi Arabia and covers an area of 24,500 km² of both marine and terrestrial ecosystems. Bat surveys have been undertaken between 2022 and 2024 across PMBSRR as part of the Reserve’s groundbreaking conservation efforts and this is the first published checklist of the bat species present within the Reserve. Survey methods included bat capture, roost inspection, and acoustic recordings. Tissue and faecal samples were taken during the surveys and compared to a global database to confirm the species present. The survey results confirmed the presence of 18 species belonging to 14 genera of eight families. This research has greatly expanded our knowledge on the diversity of bats within the Kingdom, increasing the reported bat species in the Tabuk region by seven species, with the addition of Rhyneptesicus nasutus, Rhinolophus hipposideros, Nycteris thebaica, Tadarida aegyptiaca, Rhinopoma microphyllum, Taphozous nudiventris, and Taphozous perforatus and increasing the number of species within Saudi Arabia by one species, with the addition of Vansonia rueppellii. Full article

Groundwater vulnerability in arid regions is increasingly influenced by land use changes and climate variability. This study evaluates groundwater vulnerability and contamination risk in the arid/semi-arid Verde River Basin and Prescott Active Management Area using four models: DRASTIC, DRASTIC-LUCC, DRASTIC-LUCC-AHP2, and DRASTIC-LUCC-AHP4. Modifications to the traditional DRASTIC model, including the integration of land use and climate factors and the application of AHP (Analytic Hierarchy Process) to refine variable weighting, significantly enhanced predictive accuracy. Results demonstrate that models incorporating land use and climate data outperform the traditional approach. The DRASTIC-LUCC model identified elevated nitrate concentrations in high-vulnerability areas, while DRASTIC-LUCC-AHP2 exhibited the greatest sensitivity in classifying vulnerability. Karst aquifers were particularly susceptible due to their high permeability and rapid contaminant transport. Recommendations include routinely integrating land use and climate data into vulnerability assessments, enforcing land use controls in high-risk zones, promoting adaptive management practices, and raising public awareness to mitigate contamination risks. This framework offers actionable strategies for improving groundwater protection and sustainable management in arid and semi-arid regions facing water security challenges. Full article

Polyethylene glycol (PEG), especially at high molecular weights, is highly soluble in water, and these solutions have reduced water potential. It is convenient to use PEG in hydroponics (liquid nutrient solution) for experiments with plants. However, some authors have been found to describe the application of PEG to plants incorrectly, such as drought, dehydration, osmotic, or water stresses, which can mislead readers. The presented opinion paper shows our arguments for a terminology in such experiments that is strictly limited to ‘PEG-induced’ or ‘simulated’ or ‘mimicked’ dehydration, and osmotic or water stresses, with the best option being ‘PEG-induced dehydration’. The most popular term, ‘drought’, is inappropriate to be used for hydroponics at all, with or without PEG. Traditionally, drought stress study was related to only plants in soil or other substrates mixed with soil. Based on 139 published papers, the examples presented in our opinion paper can demonstrate differences in gene expression between plants grown in containers with soil and under PEG-induced stress in hydroponics. Researchers can carry out any type of experiments suitable for the purposes of their study. However, clear and correct description of experiments and careful interpretation of the results are strongly required, especially with PEG, to avoid incorrect information. In all cases, at the final stage, results of experiments in controlled conditions have to be verified in field trials with naturally occurring drought. Full article

The study area is a densely populated residential zone located in central Mexico, characterized by a semi-arid climate and diverse land uses, including domestic, commercial, and services. In the study area, water demand is assessed based on land use requirements set by national and local regulations, and an urban configuration pinpoints spaces suitable for rainwater harvesting for self-consumption. This research proposes indicators based on urban, demographic, and hydrological parameters to assess the effectiveness of low-impact development (LID) techniques, such as rooftop rainwater harvesting, aimed at reducing water scarcity in a residential area vulnerable to water shortages in a semi-arid city in the Global South. Additionally, hydrological design conditions are defined to estimate the potential volumes of recoverable water. Indicators for infiltrated water, available water for consumption, runoff water, and sufficient water are established. This framework enables the development of strategies to mitigate water scarcity and improve water management in the area. Full article

Climatic and environmental issues have attracted considerable attention worldwide. Clarifying the interactions between urban land use efficiency (ULUE), industrial structure (IS), and carbon emissions intensity (CEI) is of considerable importance in promoting resource–economy–environment coordination. The temporal and spatial characteristics of ULUE, IS, and CEI were analyzed based on panel data from 309 cities in China from 2006 to 2021. A PVAR model was established to analyze the long-term and short-term dynamic and causal relationships among the three variables. ULUE, IS, and CEI showed an upward trend, but significant spatial heterogeneity existed. The three variables had a long-term cointegration relationship. Overall, ULUE had a positive effect on IS, and IS had a promotional effect on ULUE. ULUE and IS had bidirectional inhibitory effects on CEI. This indicates that improving ULUE, upgrading IS, improving energy efficiency, and reducing CEI may be necessary measures to mitigate the environmental impact of human activities. These research results can provide theoretical and policy support for promoting the coordination of resources, the economy, and the environment, and for achieving the promotion of urban high-quality green and sustainable development. Full article

Larch wood, a prevalent cultivation medium for Ganoderma tsugae, has yet to be scrutinized concerning the differential impacts of sterilized and non-sterilized substrates on the growth and development of this fungus. Our present investigation sought to elucidate these effects in a forest-like environment. After larch wood segments were sun-dried, they were divided into two groups; one group was bagged and autoclaved, while the other group was bagged without any treatment. Subsequently, all segments were inoculated with the G. tsugae strain HLXL1 and ensconced under the canopy of a Pinus koraiensis forest, thereby approximating the conditions of natural growth. Wild G. tsugae was used as the control. Data on agronomic traits, production days, fruiting body yield, and effective constituent content were analyzed. The results indicated no significant differences between sterilized and non-sterilized substrates in terms of agronomic traits. However, the mineral content and bioactive compounds in G. tsugae fruiting bodies significantly differed across various growth stages. The outcomes were optimal for non-sterilized substrates, followed by sterilized substrates, while the wild strains were markedly less effective than the cultivated ones. Full article

Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, non-invasive stress monitoring. This study aims to classify environmental stress symptom levels in cucumber seedlings using ML models by extracting critical color, texture, and morphological features from RGB images. In a controlled plant factory setup, two-week-old cucumber seedlings were subjected to varied environmental conditions across five chambers with differing temperatures (15, 20, 25, and 30 °C), light intensities (50, 250, and 450 µmol m⁻² s⁻¹), and day-night cycles (8/16, 10/14, and 16/8 h). A cost-effective RGB camera, integrated with a microcontroller, captured images from the top of the seedlings over a two-week period, from which sequential forward floating selection (SFFS) and correlation matrices were used to streamline feature extraction. Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. KNN achieved the highest accuracy at 0.94 (94%), followed closely by SVM and RF, both at 93%, while NB reached 88%. Findings suggested that color and texture features were critical indicators of stress, and that the KNN model, with optimized hyperparameters, provided a reliable classification for stress symptom monitoring for seedlings under controlled environments. This study highlights the potential of ML-driven stress symptom detection models for controlled seedling production, enabling real-time decision-making to optimize crop health and productivity. Full article

Leaf water potential ($\psi$_leaf) is a key indicator of plant water status, but its measurement is labor-intensive and limited in spatial coverage. While remote sensing has emerged as a useful tool for estimating vegetation water status, $\psi$_leaf remains unexplored, particularly in mixed forests. Here, we use spectral indices derived from unmanned aerial vehicle-based hyperspectral imaging and machine learning algorithms to assess $\psi$_leaf in a mixed, multi-species Mediterranean forest comprised of five key woody species: Pinus halepensis, Quercus calliprinos, Cupressus sempervirens, Ceratonia siliqua, and Pistacia lentiscus. Hyperspectral images (400–1000 nm) were acquired monthly over one year, concurrent with $\psi$_leaf measurements in each species. Twelve spectral indices and thousands of normalized difference spectral index (NDSI) combinations were evaluated. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were used to model $\psi$_leaf. We compared the machine learning model results with linear models based on spectral indices and the NDSI. SVM, using species information as a feature, performed the best with a relatively good $\psi$_leaf assessment (R² = 0.53; RMSE = 0.67 MPa; rRMSE = 28%), especially considering the small seasonal variance in $\psi$_leaf ($\pm \sigma$ = 0.8 MPa). Predictions were best for Cupressus sempervirens (R² = 0.80) and Pistacia lentiscus (R² = 0.49), which had the largest $\psi$_leaf variances ($\pm \sigma$ > 1 MPa). Aggregating data at the plot scale in a ‘general’ model markedly improved the $\psi$_leaf model (R² = 0.79, RMSE = 0.31 MPa; rRMSE = 13%), providing a promising tool for monitoring mixed forest $\psi$_leaf. The fact that a non-species-specific, ‘general’ model could predict $\psi$_leaf implies that such a model can also be used with coarser resolution satellite data. Our study demonstrates the potential of combining hyperspectral imagery with machine learning for non-invasive $\psi$_leaf estimation in mixed forests while highlighting challenges in capturing interspecies variability. Full article

Global climate change and the associated increasing impact of droughts on crops challenges researchers to rapidly assess plant health on a large scale. Photosynthetic activity is one of the key physiological parameters related to future crop yield. The present study focuses on the search for reflectance parameters for rapid screening of wheat genotypes with respect to photosynthetic activity under drought conditions. The development of drought stress modelled in laboratory conditions by stopping irrigation caused changes in chlorophyll fluorescence parameters that corresponded to a decrease in photosynthetic activity. In particular, a decrease in the photochemical quantum yield of photosystem II (Φ_PSII), which characterizes the rate of linear electron transport in the photosynthetic electron transport chain and is one of the most sensitive parameters responding at the early stages of drought stress, was observed. Along with the measurement of the photosynthetic activity, spectral characteristics of wheat plants were recorded using hyperspectral imaging. Normalized difference indices (NDIs) were calculated using the reflectance intensity of wheat shoots in the range from 400 to 1000 nm. Four NDIs that showed a strong correlation with the level of photosynthetic activity estimated by Φ_PSII were selected from different wavelength ranges (NDI_610/450, NDI_572/545, NDI_740/700, and NDI_820/630). The indices NDI_572/545 and NDI_820/630 showed the best combination of sensitivity to soil moisture deficit and strong relationship with photosynthetic activity under drought stress. Possible molecular and physiological causes of this relationship are discussed. The use of the proposed indices will allow to monitor in detail the specific features of wheat plant response and can serve as one of the criteria for selection of the most promising genotypes in breeding of drought-tolerant cultivars. Full article

This study investigated the thermo-hydrodynamic groundwater environment of a sandy beach where a unique sand bathing method attracts many visitors. The discussed temperatures covered a wide range, from the normal to the boiling temperature of water. We, at first, examined the feasible conditions for sand bathing and found that the volumetric water content was the crucial factor. Comprehensive field observations were implemented to elucidate two physical quantities: the groundwater flow and the temperature in the sand layer. The latter one was found to be governed by the groundwater level and tidal fluctuations. The characteristics obtained were found to be consistent with the feasible conditions in the landward area. While in the offshore area, the temperature was proved to have suddenly dropped. These results strongly suggest that the underground heat source is distributed in specific spots. A numerical model to describe the groundwater flows and the heat transfer mechanism was developed based on a saturated/unsaturated seepage flow model. The computational results were found to adequately reproduce the observed spatial temperature distribution. The reproduction ability of the model was found to be limited in terms of temporal variations; it was good for the groundwater level, but not for the temperature in the sand. Full article

This study examines the factors that lead students to consider or avoid a career in sales, focusing on behaviors and preferences during the transition period following the peak of the COVID-19 pandemic. Conducted in 2021, the study captures how the pandemic has changed traditional aspects of sales work, such as face-to-face interaction, and explores the lasting impact of these changes on young professionals. A sample of 671 business and engineering students participated in an online survey; data analysis was performed by using Partial Least Squares Structural Equation Modeling (SEM-PLS). Results show that intrinsic and social motivations enhance the perceived attractiveness of a sales career, which, in turn, impacts career intentions. Although empathy and COVID-19-related fears lack a direct effect on the intention to pursue a sales career, digital skills reinforce the connection between job attractiveness and career intentions in a digital-centric environment, having a moderating role. These findings emphasize the evolving nature of sales careers, highlighting the need to align career development strategies with young people’s intrinsic motivation and digital competencies. This study adds to the understanding of motivational factors in sales career decisions and offers valuable insights for employers seeking to attract motivated talent in a shifting industry landscape. Full article

Accurate prediction of soybean yield is important for safeguarding food security and improving agricultural management. Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing Remote Sensing (RS) data for this purpose. However, most of these models do not fully consider multi-source RS data for prediction, as processing these increases complexity and limits their accuracy and generalizability. In this study, we propose the Multi-Residual Attention-Based Multi-Stream 3D-ResNet-BiLSTM (MHRA-MS-3D-ResNet-BiLSTM) model, designed to integrate various RS data types, including Sentinel-1/2 imagery, Daymet climate data, and soil grid information, for improved county-level U.S. soybean yield prediction. Our model employs a multi-stream architecture to process diverse data types concurrently, capturing complex spatio-temporal features effectively. The 3D-ResNet component utilizes 3D convolutions and residual connections for pattern recognition, complemented by Bidirectional Long Short-Term Memory (BiLSTM) for enhanced long-term dependency learning by processing data arrangements in forward and backward directions. An attention mechanism further refines the model’s focus by dynamically weighting the significance of different input features for efficient yield prediction. We trained the MHRA-MS-3D-ResNet-BiLSTM model using multi-source RS datasets from 2019 and 2020 and evaluated its performance with U.S. soybean yield data for 2021 and 2022. The results demonstrated the model’s robustness and adaptability to unseen data, achieving an R² of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R² of 0.72 and MAPE of 12% in 2022. This performance surpassed some of the state-of-the-art models like 3D-ResNet-BiLSTM and MS-3D-ResNet-BiLSTM, and other traditional ML methods like Random Forest (RF), XGBoost, and LightGBM. These findings highlight the methodology’s capability to handle multiple RS data types and its role in improving yield predictions, which can be helpful for sustainable agriculture. Full article

Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R² = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R² = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R² = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization. Full article

Wheat (Triticum aestivum) is grown on more arable acreage than any other food crop and has been well documented to produce allelochemicals. Wheat allelochemicals include numerous benzoxazinoids and their microbially transformed metabolites that actively suppress growth of weed seedlings. Production and subsequent release of these metabolites by commercial wheat cultivars, however, has not yet been targeted by focussed breeding programmes seeking to develop more competitive crops. Recently, the Commonwealth Scientific and Industrial Organisation (CSIRO), through an extensive recurrent selection programme investment, released numerous early-vigour wheat genotypes for commercial use, but the physiological basis for their improved vigour is under investigation. In the current study, we evaluated several early-vigour genotypes alongside common commercial and heritage wheat cultivars to assess the impact of improved early vigour on the production and release of targeted benzoxazinoids by field-grown wheat roots over a two-year period. Using UPLC coupled with triple quadrupole mass spectrometry (LC-MS QQQ), we quantified common wheat benzoxazinoids and their microbially produced metabolites (aminophenoxazinones) in soil collected from the rhizosphere and rhizoplane of wheat plants over two growing seasons in the Riverina region of New South Wales, Australia. The benzoxazolinone MBOA and several aminophenoxazinones were readily detected in soil samples, but actual soil concentrations differed greatly between years and among genotypes. In contrast to 2019, the concentration of aminophenoxazinones in wheat rhizosphere soil was significantly elevated in 2020, a year receiving adequate rainfall for optimal wheat growth. Aminophenoxazinones were detected in the rhizosphere of early-vigour genotypes and also parental lines exhibiting weed suppression, suggesting that improved early vigour and subsequent weed competitiveness may be related to increased root exudation and production of microbial metabolites in addition to changes in canopy architecture or other root-related early-vigour traits. As previously reported, MBOA was detected frequently in both the rhizoplane and rhizosphere of wheat. Depending on the year and genotype, we also observed enhanced biotransformation of these metabolites to several microbially transformed aminophenoxazinones in the rhizosphere of many of the evaluated genotypes. We are now investigating the role of early-vigour traits, including early canopy closure and biomass accumulation upon improved competitive ability of wheat, which will eventually result in more cost-effective weed management. Full article

Durian, often regarded as the “king of fruits”, plays a significant role in Thailand’s economy, with durian production expanding rapidly due to its profitability and high demand in both domestic and international markets. This growth has introduced challenges, particularly for geographic indication (GI)-certified durians like those from Pak Chong, where the unique soil, climate, and cultivation practices contribute to the fruit’s distinctive quality. Maintaining these standards is crucial to preserving GI certification, but farmers face increasing risks related to pests, diseases, climate variability, and cultivation practices. Effective risk management is essential to ensure the quality and sustainability of GI-certified durian production. This study analyzes risks in Pak Chong GI durian cultivation and proposes strategies to mitigate these risks. The House of Risk (HOR) method was used to identify potential risks at various stages of durian cultivation, including planting, maintenance, pre-harvest, harvest, and postharvest, and to recommend proactive mitigation strategies. This case study focuses on Pak Chong GI durian farmers. Thirty-one risk events driven by 17 risk agents were identified throughout the durian cultivation process. Key risk agents included observation of durian tree behavior, physical characteristics of the planting area, irrigation quantity, understanding of nutrient management, soil nutrients, and soil pH. The three most significant mitigation strategies identified were the implementation of targeted training and learning programs, improved data collection and plating progress tracking ability, and investment in advanced cultivation technology. This study analyzes the critical risks in Pak Chong GI-certified durian cultivation and proposes targeted mitigation strategies using the House of Risk (HOR) method. By identifying risks (HOR1) and developing proactive solutions (HOR2) across key cultivation stages, this research offers practical insights to enhance the quality and sustainability of GI-certified durian production. The findings aim to support farmers, policymakers, and stakeholders in preserving the economic and cultural value of Pak Chong durians. Full article

This study aims to investigate the impact of salt field waste-generated bio-organic fertilizer application on the bacterial community structure within the rhizosphere soil of tea plants. After the administration of salt field waste-generated bio-organic fertilizer, the content of tea polyphenols in tea decreased, while the content of caffeine and free amino acids increased. The results showed that the dominant bacterial species in the rhizosphere soil of tea plants were Chloroflexi, Acidobacteriota, and Proteobacteria. The most dominant genus were k__Bacteria__p__Proteobacteria__c__uncultured__o__uncultured__f__uncultured__g__uncultured, k__Bacteria__p__Acidobacteriota__c__Acidobacteriae__o__Subgroup_2__f__Subgroup_2__g__Subgroup_2, k__Bacteria__p__Chloroflexi__c__AD3__o__AD3__f__AD3__g__AD3, k__Bacteria__p__Chloroflexi__c__Ktedonobacteria__o__Ktedonobacterales__f__Ktedonobacteraceae__g__HSB_OF53-F07, and g__Acidothermus. Following the application of bio-organic fertilizer, g__AD3, g__Subgroup_2, and g__HSB_OF53_F07 in the rhizosphere soil of the tea plants exhibited a decreasing trend compared to the control group. p_Proteobacteria was significantly increased, and p_Chloroflexi was considerably decreased in soils treated with bioorganic fertilizers, indicating that bio-organic fertilizers might influence the soil microbial structure in the rhizosphere soil of tea plants. Network association analyses showed a strong positive correlation between g_Candidatus_Solibacter and g_Bryobacter and a significant negative correlation between g_AD3 and g_ADurb.Bin063_1. Applying salt field waste-generated bio-organic fertilizers might effectively adjust the bacterial community structure in tea plants’ rhizosphere soil, improving the quality of tea. This study provides valuable insights into the potential benefits of utilizing bio-organic fertilizer from salt field waste in tea plantations. Full article

Climate change will lead to widespread adverse global impacts on nature, people and economies. The importance of gender in tackling climate change is becoming increasingly evident, with the Paris Agreement highlighting this. According to the evidence base, women typically show higher levels of concern and action towards climate change within the general public, within politics and on company boards. However, few studies have investigated the influence of gender on climate concerns and action within the workforce. The construction industry is one of the biggest emitters of greenhouse gases; therefore, this is a key sector to focus on with respect to decarbonisation. Simultaneously, construction is dominated by men and has significant gender inequity issues. Given the importance of including women in climate change decisions, it is important to assess construction employees’ knowledge and awareness regarding this key issue. Quota and snowball sampling techniques were used to recruit UK-based participants (n = 30) from a large engineering/construction company to explore the perceived concern, importance and action towards climate change, and qualitative analysis using NVivo 14 software was employed to identify key themes. This qualitative work provides initial important insights for an industry with one of the largest carbon footprints in the world and that also reports significant gender inequities. Full article

The paper addresses the issue of forest fires and critical points in activities related to extinguishing and transport of extinguishing agent to the fire site. With the increasing incidence of forest fires, there are also serious implications for the environment, ecosystems and communities. The relevance of this topic is indisputable, as forest fires are becoming more frequent and intense, with a consequent need for systematic analysis. In this paper, critical sites are identified and assessed, and a description of the equipment used to extinguish a particular fire is provided, with a description of the firefighting strategy in a difficult-to-access site in forest firefighting. This paper shows the effective solution in extinguishing forest fires and then in the design of measures to minimize this risk. We have also assessed the risk activities in this paper. The intent of this article is to show how to effectively extinguish a forest fire. The knowledge gained and recommendations made are aimed at improving firefighter preparedness, techniques and tactics to extinguish forest fires. Full article

The study highlights the increasing significance of understanding sediment sources and their contributions within a watershed, particularly in relation to different land use types. As the demand for effective source apportionment grows, this research aims to quantify how various land uses—specifically rangeland, rainfed agriculture, irrigated agriculture, and orchards—contribute to sediment yield over time. To achieve this, the researchers employed geochemical tracers and ¹³⁷Cs to assess sediment contributions in a small sub-basin located in western Iran. The methodology involved creating a working unit map by overlaying land use maps from 1967 and 2021 with a slope map of the region. A total of 75 and 31 soil samples were systematically collected across different land uses to ensure a representative analysis of ¹³⁷Cs and geochemical methods, respectively. The study utilized specific models to calculate the average contributions of each land use type. For non-agricultural lands, a diffusion and migration model was applied, while agricultural lands were analyzed using a mass balance type II model. The FingerPro program in R software 4.2.2 facilitated the selection of suitable tracers and allowed for the determination of sediment source contributions through a multivariate mixed model algorithm. The findings revealed significant changes in sediment yield contributions over the past 60 years. In 2021, rainfed agriculture accounted for 72.26% of sediment yield, down from 85.49% six decades earlier. Conversely, irrigated agriculture showed an increase from 1.80% to 15.06%. Rangeland and orchard contributions remained relatively stable but low, at approximately 8% and 4%, respectively. The total erosion rate for the sub-basin was estimated at 526.87 t y⁻¹, with rainfed agriculture being responsible for the majority at 450.43 t y⁻¹. Full article

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants. Full article