Field Study Research Articles

Unlocking the Secrets of Soil Health: Exploring the Influence of Natural and Synthetic Nutrients on Soil Organic Carbon and Active & Passive Carbon Pool in Vertisols

The experiment carried out in this field study was between 2022 and 2023 at the (AICRP) research farm in the outward-bound farm of the College of Agriculture, Indore. Indore is a small city in the western part of Madhya Pradesh, Central India, situated on the Malwa Plateau. An experiment was conducted with three replication by using different combination of N, P levels and crop residues in RBD design. Experiment field was medium to black soil with pH of 7.63 and medium in organic carbon 0.58%. The soil was low in available nitrogen (208.0 kg/ha) medium in available phosphorus (21.0 kg/ha) and high in potassium (585 kg/ha) and divided into gross plot size of 10 x 7.2 m2 and a net plot size of 9 x 6.4 m2, respectively. During crop harvest four different depths were used to gather the soil samples: 0–10, 10–20, 20–30, and 30–40 cm. All the collected samples are examined separated for soil organic carbon, active (highly labile, labile SOC), and passive (non-labile, less labile SOC) carbon pools. The finding showed that maximum soil organic carbon (%) was observed with the application of treatment T6 treatment (FYM 6 t ha-1 + N20 P13. Similar trend was found with active (very labile, labile) and passive carbon (less labile, non-labile) pools of soil.

Recommendations for light-dosimetry field studies based on a meta-analysis of personal light levels of office workers

Daytime light levels are important for human physiology. Office workers spend most of their daytime inside buildings where it is unclear whether they receive healthy light levels. A recent publication presented recommendations for the minimum daytime light level for optimal human health, expressed in melanopic equivalent daylight illuminance (EDI). The current paper assesses whether this recommended daytime light level is achieved by office workers through a meta-analysis of personal light levels of office workers, obtained from light-dosimetry field studies. From our literature search, we identified nine eligible papers. These papers comprised data from studies in one or multiple office buildings, over one or more seasons, from 1 to 62 participants, and for 19 to 564 workdays. After analysing the data, we found that in none of the offices the recommended minimum light level of 250 melanopic EDI was met for the entire day. Only 1 out of 6 median and 6 out of 13 mean reported personal light levels were above this recommended value. Unfortunately, these conclusions are less groundbreaking than we hoped for, due to large differences between study protocols. This resulted in a large variety of (unreported) study characteristics (i.e. light data, light-dosimeter, participant, building and environment) which complicated a fair comparison between the different studies. To facilitate meta-analyses of light-dosimetry field studies, we introduce recommendations for data collection and reporting in light-dosimetry field studies. We based these recommendations on the gaps identified from our meta-analysis, supplemented by recommendations from other papers.

Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future

The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.

The effect of green influencer message characteristics: Framing, construal, and timing

The effect of green influencer message characteristics: Framing, construal, and timing

Ameliorating saline‐sodic soils: A global meta‐analysis of field studies on the influence of exogenous amendments on crop yield

AbstractAs the demand for food continues to rise, soil salinization and sodification pose an increasingly pressing challenge. Currently, there is a knowledge gap regarding how to effectively improve saline‐sodic soils to support sustainable agricultural production, especially the lack of systematic analysis on the effects of different amendments at a global scale. To address this gap, this study aims to explore the feasibility of using exogenous amendments to ameliorate saline‐sodic soils, conducting a global‐scale meta‐analysis based on 685 data pairs from 70 published studies. Our results showed that applying amendments to saline‐sodic soils significantly reduced electrical conductivity of saturated paste extract (ECe) by 33.0% and exchangeable sodium percentage (ESP) by 44.6%, while simultaneously increasing crop yield by 50.7%. Heterogeneity analysis further unveiled significant variations (p < 0.05) in the overall effect size, driven by factors such as initial soil properties (salinity and ESP levels), amendment type, climate and practical management conditions (application dose and experimental duration). The categorical variable analysis showed that, compared to soils with other salinization levels, the application of amendments in severe salinization soils was most effective in reducing soil ECe and enhancing crop yield. Considering the goals of mitigating soil salinity, sodicity, and increasing crop yield, the study suggests the application of mixed‐type amendments in practical settings. It is noteworthy that while extremely high doses (greater than 40 t ha−1) effectively increased crop yield, they also posed a risk of salt accumulation. In conclusion, this research offers critical insights for sustainable agriculture, guiding future work on soil health and food security in the context of global environmental challenges.

Nature contact promotes prosociality: The mediating roles of self-transcendence, nature connectedness, and materialism

Current research proposes that nature contact promotes prosociality via enhancing nature connectedness and self-transcendence while decreasing materialism. These hypotheses were examined in five multimethod studies in laboratory and field settings. In Study 1, the self-reported nature contact was positively associated with general prosocial tendencies, and nature connectedness (Study 1a), self-transcendence, and materialism (Study 1b) mediated this relationship. In the lab-based Studies 2 and 3, through manipulating different types of contact, it was found that nature contact led to greater prosociality (i.e., donation willingness, cooperation, and helping behaviors) than urban contact and the control condition. The enhanced self-transcendence (Studies 2 and 3) and reduced materialism (Study 3) mediated the effect of nature contact on prosociality. Finally, in the field study (Study 4), it was found that engaging in nature (vs. urban) contact in daily life led to greater prosocial behavior (i.e., cooperation in the public goods game), and self-transcendence, nature connectedness, and materialism mediated this process. The present findings are valuable for advancing the conceptual understanding of the nature-human behavior relationship.

Functional renormalization group study of thermodynamic geometry around the phase transition of quantum chromodynamics

We investigate the thermodynamic geometry of the quark-meson model at finite temperature, T, and quark number chemical potential, μ. We extend previous works by the inclusion of fluctuations exploiting the functional renormalization group approach. We use recent developments to recast the flow equation into the form of an advection-diffusion equation. We adopt the local potential approximation for the effective average action. We focus on the thermodynamic curvature, R, in the (μ,T) plane, in proximity of the chiral crossover, up to the critical point of the phase diagram. We find that the inclusion of fluctuations results in a smoother behavior of R near the chiral crossover. Moreover, for small μ, R remains negative, signaling the fact that bosonic fluctuations reduce the capability of the system to completely overcome the fermionic statistical repulsion of the quarks. We investigate in more detail the small μ region by analyzing a system in which we artificially lower the pion mass, thus approaching the chiral limit in which the crossover is actually a second order phase transition. On the other hand, as μ is increased and the critical point is approached, we find that R is enhanced and a sign change occurs, in agreement with mean field studies. Hence, we completely support the picture that R is sensitive to a crossover and a phase transition, and provides information about the effective behavior of the system at the phase transition. Published by the American Physical Society 2024

Managing Dust Mite Oligonychus afrasiaticus (McGregor) (Acari: Tetranchidae) Infesting Date Palm Orchards By Using Lemongrass Extract and Nanosulfur

The dust mite Oligonychus afrasiaticus (McGregor 1939) (Acari: Tetranychidae) poses a significant threat to date palm Phoenix dactylifera L. productivity in Iraq. To address this issue, a field study was conducted in a private date palm orchard in Al-Zubair district, Basrah province, Iraq, to assess the efficacy of some treatments in controlling the dust mite population on date palm fruits. The treatments included lemongrass extract, nanosulfur, a combination of lemongrass extract and nanosulfur, liquid sulfur, and a distilled water as control treatment. Treatments were applied by spraying on leaves and fruits at three dates: five weeks, eight weeks, and eleven weeks after pollination. Dust mite mortality rates were measured at intervals of 1, 3, 5, and 7 days after treatment application. Results revealed that the second date, eight weeks after pollination, exhibited the highest mortality rate of 76.77%, significantly outperforming other dates. Among the treatments, liquid sulfur showed the highest efficiency, with an 87.83% mortality rate, followed closely by the combination of lemongrass extract and nanosulfur (84.14%). Nanosulfur alone recorded a mortality rate of 79.26%, while lemongrass extract alone showed a mortality rate of 49.15%. The mortality rate increased over time, reaching 80.79% after 7 days and 65.76% after 1 day. In conclusion, liquid sulfur emerged as the most effective treatment for controlling the dust mite population on date palm fruits. The second date, eight weeks after pollination, was the optimal time for treatment application. These findings offer valuable insights into sustainable and environmentally friendly pest control strategies for date palm cultivation in Iraq.

New sustainable strategy for rehabilitating phosphate mining sites using phosphate industry by-products and sludge integrating Argan, Carob, and Olive trees

In semi-arid context, the development of anthroposol from phosphate industry by-products and sewage sludge could be a new approach for sustainable phosphate mining soils. The objective of this 2 years field study carried out at Benguerir phosphate mine, was to evaluate nutrients and heavy metals dynamics in the soil-plant system of three species planted on anthroposol consisting of a mixture of 65% phosphogypsum (PG), 30% phosphate sludge (PS), and 5% sewage sludge (SS). The experimental layout was a randomized complete block design with six replicates and three species: argan, olive, and carob. No fertilizer was applied to the different plots. Heights and diameters of the different species were measured. Species were pruned, and leaves and stems were separated. The nutrients (N, P, K, Ca, and Mg) and heavy metals (Cd, Cu, Cr, Pb, Ni and Zn) contents in both soil and plant part were determined. The results of the Pearson correlation showed significant correlations between height and diameter of the different species. The strongest correlation was observed for the argan tree. Furthermore, nutrient contents in the rhizosphere of species were significantly reduced, excepted Ca. Also, nutrient contents in leaves were significantly higher than those in the stems. In addition, the concentration of heavy metals in argan, olive and carob leaves, were below international standards. In semi-arid contexts, argan tree could be the best candidate for revegetating mining sites and this anthroposol (65% PG – 30% PS – 5% SS) could be a new approach to rehabilitate mining site on large scale.

In help-seekers' shoes: First-person pronouns entitled charitable fundraising campaigns and individual donating behavior in China

Both first-person and third-person pronouns are widely used in charitable fundraising campaigns, but which type of pronouns is more effective in soliciting donations is still to be explored. In this study, we conduct a field study and five experiment studies to examine the effect of personal pronouns (first-person vs. third-person) entitled charitable fundraising campaigns on viewers' donations and its underlying mechanisms. We find robust results to show that, compared to third-person pronouns, adopting first-person pronouns (both singular and plural) in the title of charitable fundraising campaigns significantly increases viewers' donating willingness and behavior. Viewers' perceived self-other overlap acts as an important mediating mechanism: using first-person pronouns in the title of charitable fundraising campaigns first increases viewers' sense of self-other overlap, which in turn promotes their donating behavior. In addition, viewers' thinking style seems to bend the relationships between the use of first-person pronouns and both perceived self-other overlap and donating behavior in such way that individuals with emotional-thinking style (vs. rational-thinking style) experience stronger self-other overlap and demonstrate higher level of donating willingness when a charitable campaign is named in first-person pronouns other than in third-person pronouns. These findings contribute to the literature on charity marketing, personal pronouns and metaphor representation, and have substantial practical implications.

Corporate social responsibility in hospitality and tourism: a systematic review

ABSTRACT Scholars have increasingly focused on the importance of corporate social responsibility (CSR) in the hospitality and tourism (H&T) industry due to its rapid growth and expansion. This study examined 192 CSR-focused empirical studies that were published in H&T journals. The report provides a comprehensive analysis by categorizing the data into several parts, such as publishing trends of CSR, segmentation by journals, theoretical frameworks, techniques, and CSR measurement scales. This study also develops a complex nomological network of CSR, examining its connections with the precursor factors, mediating variables, moderating impacts, and outcome effects. Key themes were identified using advanced thematic analysis techniques, providing crucial insights into the current status and future directions of CSR using Leximancer software. The results provide significant information for sustainable practices and influence prospective studies in the crucial field of corporate responsibility.

Two days before Friday versus 2 days from today: How future (vs. present) anchors in deadlines increase goal achievement

AbstractWhile deadlines are routinely used as a strategy to achieve goals, the way these deadlines are described has been relatively overlooked in terms of their influence on decision‐making. We examined the unique effect of future (vs. present) anchors in deadlines on ones' goal achievement. A field study shows that a deadline with a future anchor encourages people to complete a task more effectively within the deadline than a deadline with a present anchor (Study 1). In Study 2, we further demonstrated the positive effect of future (vs. present) anchors on individuals' goal achievement in a more controlled lab setting. Study 3 provided support for our mechanism by examining individuals' perceived time availability until the deadline underlies this effect. Studies 4 and 5 delved deeper, investigating our mechanism by examining individuals' dispositional time value as an important moderator. This research enriches our understanding of time perception and decision‐making, and offers valuable insights on how to strategically deal with deadlines to enhance one's goal achievement.

Research on the Population Flow and Mixing Characteristics of Pelleted Vegetable Seeds Based on the Bonded-Particle Model

In order to precisely reproduce the precise seeding process of the population in the air-suction seed-metering device, it is necessary to execute accurate modeling of seed particles using the bonded-particle model, in combination with the discrete element method (DEM) and computational fluid dynamics (CFD). Through the repose angle, slope screening, rotating container, and particle sedimentation experiments, in this paper, the influence of the filling accuracy of the bonded-particle model on the flow behavior and mixing characteristics of the seed population was first explored based on EDEM software. The viability of the suggested modeling approach for pelleted vegetable seeds, as described in this study, was confirmed by comparing experimental and simulation outcomes. The surface roughness values obtained from the studies above were utilized to assess the accuracy of the bonded-particle model in filling. Additionally, a mathematical technique for determining the surface roughness was provided. Furthermore, an analysis of the multiple contacts in the bonded-particle model was also performed. The results indicated that the simulation results closely matched the experimental data when the number of sub-spheres in the bonded-particle model was equal to or more than 70, as measured by the standard deviation. In addition, the most optimal modeling scheme for the pelletized vegetable seed bonded-particles, based on the cost of coupling simulation, was found to be the bonded-particle surface roughness (BS) with a value of 0.1. Ultimately, a practical example was utilized to demonstrate the utilization of the pelleted vegetable seed bonded-particle model and the DEM-CFD coupling approach in analyzing the accuracy of the seeding process in the air-suction seed-metering device. This example will serve as a valuable reference point for future field studies.

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

Food ethnobotany of forest resource in the high-altitude Himalaya Mountains: Enhancing the food sovereignty of ethnic groups

The vital knowledge of traditional foraging techniques used in indigenous peoples' cuisines may be preserved through gastronomical field studies before it is lost forever. Certain plants are appreciated for their gastronomic, social, economic and ecological importance in the High Himalaya because they play such a significant role in the daily life of the local indigenous populations. To determine the plant cultural indicators for each ethnic group, the data were analyzed using an indicator species analysis. Semi-structured interviews of 167 participants, focus group discussion, and field observations were used to gather information from February 2019 to August 2022 for a comprehensive and systematic gastronomical survey. Across four ethnic groups, a total of (N = 99) wild edible plant species and (N = 9) fungi species from 38 families were documented. Tender leaves were the most commonly used, while tubers were the least commonly used. The majority of the species reported were used as vegetables, followed by species used as fruits and herbal tea. For the first time, we examined cross-cultural similarity and dissimilarity for gastronomic usage: vegetables and savoring plants,fruits, and herbal tea across the cultures studied from the perspective of food ethnobotany. We discovered a clear difference in how different cultures used forest resources, with Gujjar and Pahari showing the most species similarity. This is because these ethic groups are culturally linked with one another and share a common social structure, geography, and language. We calculated plant cultural markers for each species, and 19 of them had significant p-values among the four ethnic groups. The findings revealed a positive relationship between the Gujjar and Pahari ethnic groups and the gastronomical indicator values, while the Kashmiri ethnic group had a negative relationship. The Kashmiri ethnic group experienced the loss of traditional knowledge because of the economic transition that caused people to prefer purchasing vegetables from the market rather than foraging for them in the wild. Traditional knowledge was also dwindling between the Gujjar and Pahari ethnic groups because of social changes brought on by developmental activities that changed their way of life. The identified plant cultural markers were significant to local residents and frequently had adaptations to extremely harsh climatic and environmental conditions. Our finding that 47% of species had market value suggests that these species have a sizable market demand and a promising economic value. They have the potential to boost food security, local community sovereignty, and people's living standards. Food production systems may become more resilient and sustainable by incorporating these indicator species into conventional agriculture. The current study raises awareness about forest resources and enables ethnic groups to live in a natural environment without food scarcity.

The impact of herbicide-resistant canola systems on the weed community dynamics in the Canadian Prairies

Increased adoption of no-till and extensive cultivation of herbicide-resistant (HR) crops in low diversity rotations are some of the transformations that occurred during the 1970s-2000s in western Canada. How these transformations in canola-based crop rotations impacted weed communities are not comprehensively studied. Historical data analysis of weed survey data ( Saskatchewan, Manitoba and Alberta) in canola crops and a canola rotation field experiment were used to understand how tillage, herbicide-resistant cropping systems, and frequency of canola in the rotation can impact weed abundance, composition, and diversity in the Canadian prairies. Weed survey data revealed that the frequency of many weed species declined in years after 1995 compared with before 1995, the year HR cultivars were introduced. Gallium spurium (false cleavers) and volunteer wheat (Triticum aestivum L.) were the only species showing a continuous increase in frequency throughout the surveyed years. Species richness showed no trajectories but varied depending on climatic conditions. Glyphosate-resistant (G.R.) systems were associated with green foxtail irrespective of their frequency in the rotation. Wild oat was predominant in the longer rotations with glufosinate and imidazolinone-resistant canola. In the field study, weeds associated with pre-herbicide weed control timing were relatively controlled when herbicides were applied (glyphosate and glufosinate), except for foxtail barley (Hordium Jubatum L), which was not controlled in most HR systems. Overall, both weed survey data and field experiment data revealed greater control of most weed species due to growing of HR canola cultivars.

Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Exploring the impact of high-power infrared lasers on electro-optical systems performance: A field study with different wavelengths

The performance of different electro-optical (EO) and infrared (IR) imaging systems under the impact of high-power pulsed infrared lasers has been well evaluated and studied. The infrared lasers operating at 1.06 µm, 3.7–4.2 µm, and 9.6 μm wavelengths, including short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) sensors used in television, infrared imaging, and tracking systems. Our research is focused on the intended waveband of the infrared imagers at significant distances relevant to tactical security operations, with a range of 6 km–10 km. The study compares the effects of both dazzling and damaging laser radiation on EO systems using a straightforward theoretical framework supported by atmospheric modeling. It was observed that increased humidity and temperature diminish both dazzling and damage distances, with less impact at 1.06 µm and more significant at 9.6 µm, compared to 3.7–4.2 µm. The effect of atmospheric conditions on IR laser energy was studied using MODTRAN software to determine the effective laser parameters for EO imaging systems at different distances. The study also investigates the critical parameters of practical high-power pulsed infrared laser wavelengths to dazzle and damage imaging systems at different distances. Field investigations revealed that dazzling on TV seekers can extend beyond 10 km by 1.06 µm, with damages up to 8 km, depending on the sensor field of view, laser source pulse frequency, and environmental conditions. Lasers with wavelengths of 3.7–4.2 μm can produce high-dazzling spots at the center of images of MWIR seekers up to 6 km. Lasers with wavelengths of 9.6 μm can rapidly damage LWIR seekers’ pixels due to their varied energy outputs and pulse frequency-range responses up to 8 km. System functions were lost under the dazzling and damaging effects. The study revealed a strong correlation between experimental results and predictions, with a strong agreement between experimental and theoretical effective distances under similar environmental and atmospheric conditions.

The Progress/Status of Ecological Assessment on the Intensive Land Use in Selenge and Darkhan-Uul Province, Mongolia

Abstract. The study area, it includes Darkhan-Uul and Selenge provinces of Mongolia, is included in the most favourable natural-geographical areas, and the migration of people with livestock from the peripheral areas has led to an increase in the population, as well as a sharp increase in the number of grazing animals, resulting in the effects of natural and human activities. give an evaluation, develop the basis for the proper use of the land in the future. In Mongolia, the methods and principles of land evaluation differ depending on the general classification and purpose of land, so considering these characteristics, land evaluation is carried out by (1) the Department of Agriculture, (2) the Department of Urban Development, Industry and Mines, (3) the Department of Roads and Networks, (4) it is divided into types of land with forest reserves (Tserenbaljir, B. Naranchimeg, 2004). In the "instructions for land evaluation" issued by the United Nations Food and Agriculture Organization (FAO) in 1976, in the assessment of land quality, in addition to the main indicators of soil fertility and moisture, climate, land cover and use, chemical pollutions such as an alkaline acidity. In the ecological assessment study, data on land use, soil, vegetation, climate, natural conditions, resources, socio-economic, satellite and field studies were collected in numerical and tabular form. The Ecological assessment in intensive land use is divided into qualitative and quantitative assessment. The Qualitative assessment predicts ecological properties. The Quantitative methods use multi-species numerical methods to record ecological elements and calculate the overall percentage of ecological characteristics.

Results of a monocentric field study: value of histology compared to sonication method and conventional tissue culture in the diagnosis of periprosthetic joint infection (PJI).

To confirm the diagnosis of periprosthetic joint infection (PJI), the Infectious Diseases Society of America (IDSA) and the International Consensus Meeting (ICM) have defined criteria that include histology as a minor criterion and the sonication method only as an additional criterion. The aim of this monocentric, retrospective study was to investigate the value of histology and whether sonication leads to a more accurate diagnosis. All revision surgeries for knee and hip arthroplasty between 2017 and 2020 were included. With regard to microbiological diagnostic, conventional culture of periprosthetic biopsies and sonication of explant material were performed. In addition, histology and non-specific inflammatory markers (CRP, leukocytes) were recorded. A total of 78 patients with PJI and 62 aseptic controls were included. From both microbiological methods (conventional culture / sonication), Staphyloccus (S.) epidermidis and S. aureus were detected most frequently. However, compared to the conventional microbiology, a higher sensitivity was calculated for sonication, albeit with a lower specificity in relation to a PJI. In two logistic regression models for the significance of all diagnostic parameters in PJI, the AUC was 0.92 and 0.96 with histology in particular making the decisive contribution in both models (p < 0. 001, both models). Since histology showed the highest accuracy in the current study, its importance in the PJI criteria should be reevaluated. Sonication shows a high sensitivity for germ detection with a lower specificity and should only be used in combination with the conventional culture for microbiolgical diagnostics.