Multiple Analysis Method Research Articles

AGRIBISNIS KOPI DALAM MENDUKUNG PENGEMBANGAN AGROWISATA DI DESA COLOL, KECAMATAN LAMBA LEDA TIMUR, KABUPATEN MANGGARAI TIMUR (Coffee Agribusiness In Supporting Agrotourism Development In Colol Village, Lamba Leda Timur District, East Manggarai District)

Colol Village is a wilaya located in East Manggarai Regency, East Nusa Tenggara Province, which has good environmental and geographical characteristics, fertile soil and a suitable climate for coffee cultivation. The coffee varieties that are being developed are Arabica coffee and Robusta coffee. The people of East Manggarai have a cultural tradition that is close to coffee. The aim of this research is to analyze the factors that influence coffee production in Colol Village, determine farmers' perceptions of agrotourism development in Colol Village and describe the implementation of the coffee agribusiness system in Colol Village, East Manggarai Regency. This research used mixed methods with a sample size of 56 people using multiple linear regression data analysis methods, Likert scale analysis and descriptive qualitative analysis. The results of the research show that in coffee production the factors of land area, farming experience, farmer age, education level, household income, and coffee sales income level influence the development of agrotourism in Colol Village, East Manggarai Regency and based on Likert scale analysis, Colol coffee farmers agree with the development of coffee agrotourism in Colol Village and coffee production and agrotourism potential are related, the potential for coffee farming as a tourist attraction thereby strengthening the local economy and creating sustainable opportunities for the development of Colol village, East Manggarai

Reaction kinetics and product evolution of hydrolysis in copper-chlorine thermochemical cycle for hydrogen production

By-products of the hydrolysis reaction in the copper-chlorine thermochemical cycle for hydrogen production are key to the final thermal and hydrogen efficiency. The current study focuses on the influences of reaction time, temperature, and steam partial pressure on the hydrolysis reaction and by-product formation mechanism via experimental and multiple analytical methods. The result demonstrates that the hydrolysis reaction of CuCl2 and steam converts to CuCl and Cu2OCl2, with a high conversion of 97%, while the Cu2OCl2 and CuCl2-CuO product mixtures exhibit interconversion capabilities. The increase of temperature increases the CuCl product content, while the decrease of steam partial pressure increases the by-product CuCl2. Particle morphology and reaction thermodynamics are analyzed for main and by-products formation. Finally, the homogeneous reaction model and shrinking core model are used and compared for the activation energy of the formation of Cu2OCl2 for the hydrolysis reaction and the effect of by-products.

Orna face analysis

Aim: Facial aesthetics is a dynamic field requiring precise professional assessment, considering beauty concepts, variability in cultural and ethnic facial features, and patient perception. This study aimed to develop the Orna Face Analysis (OFA), an integrated tool for detailed facial evaluation to improve the planning of aesthetic treatments. Methods: The OFA integrates multiple analysis methods, considering key aspects such as youthfulness, average appearance, symmetry, and dimorphic features to create a comprehensive evaluation protocol based on a critical review of the scientific literature searched through PubMed, Scielo, Web of Science, and Scopus databases without language or time restrictions. Results: The OFA was designed as an electronic form that can generate a printed file, allowing for a deep understanding of patients faces by highlighting these essential aspects. This approach enables detailed and personalized diagnostics essential for effectively planning facial aesthetic treatments and meeting patient expectations. The OFA emerges as a complementary and integrative tool in facial aesthetics, promoting a personalized and evidence-based approach to facial harmonization. After constructing the OFA prototype in electronic and printed formats, the tool was tested in a clinical setting (TRL 6 – technology readiness level) to assess its objectivity, clarity, coherence, precision, and usability. TRL 6 indicates that the system has been demonstrated in a relevant environment but requires further testing for full validation. Conclusion: A preliminary evaluation by a facial harmonization specialist confirmed that the OFA enables the creation of highly personalized treatment plans, accurately reflecting a range of aesthetic outcomes. Further validation will solidify its applicability across diverse patient populations and settings.

Spatiotemporal Evolution and Driving Factors of Land Use Carbon Emissions in Jiangxi Province, China

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide.

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

The Influence of Attractions, Facilities and Accessibility on Tourist Satisfaction at Parai Tenggiri Beach, Bangka Regency

This research aims to determine the influence of attractions, facilities and accessibility on tourist satisfaction. This type of research is quantitative descriptive research with multiple analysis methods. The total population in this study was 23,112 people based on January 2021 to December 2021, while the sample used the Slovin formula to be 100 respondents. The research results show that facilities and accessibility have a significant effect on tourist satisfaction. The natural beauty of the beach, with white sand and granite rocks, as well as recreational activities such as fishing and diving, add to the visitor experience. Adequate facilities and good accessibility also increase tourist comfort and satisfaction, encouraging them to return. Managers are expected to improve natural attractions, introduce new activities, and improve the quality of facilities such as toilets and eating places. Accessibility must also be considered with the development of transportation infrastructure and clear information about locations. Keywords: attractions, accessibility facilities, tourist satisfaction

Assessment of heavy metal concentration in pelagic fish species and associated health risks along the Kanyakumari coast, India – a baseline study

ABSTRACT This research examines heavy metal concentration in fish from the Kanyakumari coast of India, emphasising its ecological consequences and possible health hazards for consumers. The study investigates the levels of several heavy metals such as zinc, manganese, iron, cadmium, copper, chromium, cobalt, lead, and nickel in different organs of multiple pelagic fish species obtained from the Kanyakumari fish landing site. Concentrations of heavy metals exhibit considerable variation among different fish organs and species, with distinct patterns identified for each metal. Muscle tissue often demonstrates reduced amounts of heavy metals in comparison to other organs. Except Zn and Fe, other heavy metals in fish muscle were above the acceptable limits set by health organisations. Cadmium was found to be 36-fold higher than the permissible limit, followed by nickel (30.4) and lead (7.6). Copper, nickel, and cadmium provide possible health hazards according to the Target Hazard Quotient (THQ). Lead and cadmium exhibit Excessive Lifetime Cancer Risk (ELCR) markedly above tolerable thresholds. Large predatory fish, such as Thunnus thynnus, exhibit elevated levels of contaminants such as cadmium. Environmental conditions, aquatic habitat, and dietary intake affect metal build-up in fish. The research employs multiple analytical methods and statistical methods, such as Atomic Absorption Spectrophotometry, correlation and Principal Component Analysis, to evaluate heavy metal concentrations and their interrelations. The study underscores the critical necessity for monitoring and mitigation techniques to combat heavy metal pollution in coastal waters and its possible effects on human health through fish intake.

A genetic study to identify pathogenic mechanisms and drug targets for benign prostatic hyperplasia: a multi-omics Mendelian randomization study

Benign prostatic hyperplasia (BPH) as a common geriatric disease in urology, the incidence and prevalence are rapidly increasing with the aging society, prompting an urgent need for effective prevention and treatment of BPH. However, limited therapeutic efficacy and higher risk of complications result in the treatment of BPH remaining challenging. The unclear pathogenic mechanism also hampers further exploration of therapeutic approaches for BPH. In this study, we used multi-omics methods to integrate genomics, transcriptomics, immunomics, and metabolomics data and identify biomolecules associated with BPH. We performed transcriptomic imputation, summary data-based Mendelian randomization (SMR), joint/conditional analysis, colocalization analysis, and FOCUS to explore high-confidence genes associated with BPH in blood and prostate tissue. Subsequently, three-step SMR was used to identify the DNA methylation sites regulating high-confidence genes to improve the pathogenic pathways of BPH. We also used cis-instruments of druggable genes to conduct SMR analysis to find potential drug targets for BPH. Finally, we used MR analysis to explore the immune pathways and metabolomics related to BPH. Multiple analytical methods identified BTN3A2 (Blood: TWAS Z score = 5.02912, TWAS P = 4.93 × 10–7; Prostate: TWAS Z score = 4.89, TWAS P = 1.01 × 10–6) and C4A (Blood: TWAS Z score = 4.90754, TWAS P = 9.22 × 10–7; Prostate: TWAS Z score = 5.084, TWAS P = 3.70 × 10–7) as high-confidence genes for BPH and identified the cg14345882-BTN3A2-BPH pathogenic pathway. We also used druggable gene data to identify 30 promising therapeutic target genes, including BTN3A2 and C4A. For MR analysis of immune pathways, we identified immune cell surface molecules as well as the inflammatory factor IL-17 (OR = 1.25, 95% CI = 1.09–1.43, PFDR = 0.12, Maximum likelihood) as risk factors for BPH. In addition, we found that disulfide levels of cysteinylglycine (OR = 1.11, 95% CI = 1.05–1.18, P = 5.18 × 10–4, Weighted median), oxidation levels of cysteinylglycine (OR = 1.09, 95% CI = 1.04–1.14, P = 3.87 × 10–4, Weighted median), and sebacate levels (OR = 1.05, 95% CI = 1.02–1.08, P = 3.0 × 10–4, Maximum likelihood) increase the risk of BPH. This multi-omics study explored biomolecules associated with BPH, improved the pathogenic pathways of BPH, and identified promising therapeutic targets. Our results provide evidence for future studies aimed at developing appropriate therapeutic interventions.

Deciphering aroma complexity between melting flesh and stony hard peach (Prunus persica L.) fruit through integrative analysis of volatile contributions

Stony hard peach is favored in production for resistance to storage and transportation, but presents a challenge of lacking fragrance compared to traditional fragrant peach i.e., melting flesh peach. Addressing this concern necessitates material composition analysis from the perspective of volatile composition and aroma characteristics. However, the complex relationship between volatile compounds and aroma profiles in melting flesh and stony hard peach cultivars remains poorly understood. To address this gap, we examined contribution of volatiles to their aroma. The application of multiple analysis methods, including distribution, odor importance, and partial least squares discriminant analysis facilitated a comprehensive understanding of the contribution of 30 volatiles to aroma; and hierarchical clustering analysis revealed relationships among the compounds within the two types of peaches. The primary differential volatiles between them were cis-3-hexenol, pentyl acetate, prenyl acetate, trans-2-hexenal, cis-3-hexenyl acetate, dihydro-β-ionone, benzaldehyde, and a range of lactones (γ-decalactone, γ-hexalactone, γ-octalactone, δ-decalactone and γ-undecalactone). Overall, the melting flesh peach exhibited more pronounced aroma than the stony hard type. This study underscores the significance of understanding the intricate interplay between volatile compounds and aroma characteristics across cultivars with different textures, offering valuable insights into cultivar selection, flavor enhancement, and consumer preference assessment within the peach industry.

Relationship between type 1 diabetes and autoimmune diseases in european populations: A two-sample Mendelian randomization study.

Previous studies have suggested an association between Type 1 diabetes (T1D) and autoimmune diseases (AIDs), but the causal relationship remains unclear. Therefore, this study utilizes publicly available Genome-Wide Association Studies (GWAS) databases and employs a two-sample Mendelian Randomization (MR) approach to explore the causal relationships between T1D and systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and inflammatory bowel disease (IBD). Summary GWAS data for T1D, SLE, RA, and IBD were downloaded from open GWAS databases and the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). We employed a series of methods to select instrumental variables closely related to T1D. To enhance the reliability of our conclusions, we applied multiple robust analytical methods, with the inverse variance weighted (IVW) method as the primary approach. Validation and meta-analysis were conducted using the FinnGen consortium. Additionally, we assessed heterogeneity, pleiotropy, and sensitivity to ensure the robustness of our conclusions. A potential causal association was found between T1D and SLE (OR = 1.37, 95% CI = 1.26 - 1.49, P < 0.001), which was further confirmed by meta-analysis. Similarly, a potential causal association was found between T1D and RA (OR = 1.32, 95% CI = 1.17 - 1.50, P < 0.001), and this was also confirmed by meta-analysis. Although the association between T1D and IBD showed P < 0.05, the leave-one-out test did not pass, and further meta-analysis indicated no significant statistical association between them. Our study reveals the relationships between T1D and three clinically common autoimmune diseases (SLE, RA, and IBD). This research supplements previous studies and provides a reference for future clinical work.

Proportional-derivative control and motion stability analysis of a 16-pole legs rotor-active magnetic bearings system

This paper analyzes the motion stability of a 16-pole rotor-active magnetic bearings (rotor-AMB) system and investigates the complex vibrations under a proportional-derivative (PD) controller. First, electromagnetic theory and Newton’s second law are applied to derive the two-degree-of-freedom differential governing equations for the 16-pole rotor-AMB system, incorporating the PD control terms. The resulting differential equations include parametrically excited, quadratic nonlinear, and cubic nonlinear terms. Subsequently, the multiple time scales perturbation analysis method is performed on the obtained governing equations, yielding four-dimensional averaged equations in both Cartesian and polar coordinates. Finally, numerical simulations including the amplitude–frequency response characteristics, motion trajectories, energy–amplitude relationships, as well as bifurcation and chaotic motion of the system are studied. The results indicate that the PD controller affects the softening and hardening spring characteristics of the system and has significant control effects on the system’s amplitude, energy, and stability. Additionally, increasing the differential gain coefficient [Formula: see text] can change the system’s motion from chaotic to periodic.

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structures, especially machine learning-based techniques. These methods have shown high classification performance, and their combination with feature engineering enhances their capability. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.

In-depth characterization of a cysteine-linked ADC disitamab vedotin by multiple LC-MS analysis methods and cutting-edge imaged capillary isoelectric focusing coupled with native mass spectrometry

The characterization of cysteine-linked antibody‒drug conjugates (ADCs) can be more challenging than that of monoclonal antibodies (mAbs) and lysine-linked ADCs because the interchain disulfide bonds are reduced for payload conjugation, and the chains are noncovalently bonded to each other. Furthermore, payload conjugation and disulfide bond reduction/scrambling may introduce additional charge heterogeneity to biomolecules. This study illustrates an innovative workflow employing multiple separation techniques and tandem high-resolution mass spectrometry for comprehensive and in-depth characterization of disitamab vedotin, a recent-generation cysteine-linked ADC, including reversed-phase liquid chromatography (RPLC), ion exchange chromatography (IEX) and image capillary isoelectric focusing (icIEF). RPLC was employed for reduced chains analysis, subunit analysis and peptide mapping. IEX and icIEF were used for charge heterogeneity analysis. The innovation of the integrated methodology emphasizes the importance of cutting-edge icIEF-MS online coupling under near-native conditions to reveal the heterogeneity of disitamab vedotin.

Unveiling dynamics of language visibility and vitality in café menus near Labrang Monastery.

This study explores the dynamics of Linguistic Landscapes (LL) in the commercial tourist district near Labrang Monastery by analyzing the use of Tibetan, Mandarin, and English on café menus. Traditionally, LL research has focused on language visibility as an indicator of vitality, overlooking contextual complexities. We challenge this approach by considering geocultural contexts, instigators' language proficiency, and intended audience. This study employed a triangulation approach, combining multiple methods of data collection and analysis. First, we conducted a preliminary observation of shop names in the region. Second, with the consent of shop owners, we comprehensively documented visual and textual artifacts both inside and outside the cafés, including signboards, menus, wall billboards, and displayed books. Third, we conducted extensive semi-structured interviews with shop owners and customers. These interviews, transcribed for analysis, provided insights into language usage preferences and decision-making processes. By advocating for a holistic LL research approach, integrating qualitative insights with quantitative data, this study contributes to a deeper understanding of LL dynamics. Our findings show that geocultural contexts, instigators' language proficiency, and intended audience all play a role in shaping language representation and vitality. The absence of a language on signage does not necessarily signify diminished vitality but can reflect strategic decisions influenced by religious, individual and commercial factors. Beyond mere visibility, languages on café menus serve as symbolic markers of ethnic identity and reflect the functions they assume within the speech community, offering insights into language vitality across different usage contexts. This research enriches scholarly discourse on LL, particularly within the Chinese context, by emphasizing the multifaceted nature of language presence and its vitality.

Pengaruh Budaya Organisasi dan Motivasi Kerja Terhadap Kinerja Karyawan di Rumah Sakit Dewi Sri Karawang

This research was conducted with the aim of determining the implications between organizational culture and work motivation and employee performance, especially at Dewi Sri Karawang Hospital. This research was conducted using quantitative methods using a sample of 79 respondents. This data was collected through a survey method using a questionnaire to calculate organizational culture, work motivation and its influence on performance. The survey results were analyzed using the multiple linear analysis method where organizational culture (X1) and work motivation (X2) were the independent variables and employee performance (Y) was the dependent variable. The results of research activities explain the good organizational culture variable, with a score of 2,725 and an average of 301, the work motivation variable with a score of 3,172 and an average of 288, and employee performance, with a score of 3,049 and an average of 304. The results of the research show that employee performance at Dewi Sri Karawang Hospital is partially influenced by work motivation, with a significance value of 0.036 &lt;0.05. Organizational culture is also partially influenced, with a significance value of 0.000 &lt;0.05. Furthermore, there is a significant influence between employee performance and organizational culture, with a significance value of 0.000 &lt;0.05.

Machine learning and design of experiments for optimizing laser-engraved micro fresnel lens mould

This research examines the application of Laser Engraving to produce micro Fresnel Lenses on aluminum plates, a novel application of this non-conventional machining method. The research explores the effects of the scan speed, laser power with number of cycles on the roundness deviation using a L9 orthogonal array. Multiple analytical methods, including the Taguchi method, Random Forest Algorithm with sensitivity analysis, are employed to optimize process and predict the outcomes. In this study, a thorough analysis of the fabrication of a micro Fresnel lens on Aluminum plate (10 mm × 10 mm × 2 mm) using fiber laser of wavelength 1064 nm is presented. The study finds that laser power has most significant effect on the roundness deviation, followed by the number of the cycles and scan speed. Scan Speed ranges from 500 to 700 mm s−1, the Power ranges from 25 to 35 Watts, and the Number of Cycles ranges from 100 to 200. Optimal conditions are identified as 700 mm/s scan speed, 25 W power, and 100 cycles. Microscopic analysis confirms roundness deviation under these conditions. Comparisons between analytical approaches and experimental results reveal that both the Taguchi method and Random Forest Algorithm align closely with experimental outcomes, with the Random Forest Algorithm showing slightly higher accuracy (6.18 percentage points closer to experimental results). This research addresses a gap in comparative studies evaluating traditional statistical methods against modern machine learning algorithms for process optimization in laser machining. It combines knowledge from optics, materials science, and laser machining, utilizing advanced methods and technologies that have only recently become accessible. The findings provide valuable insights for future applications of micro Fresnel lenses on aluminum plates and contribute to the understanding of laser engraving processes for precision optical components. Between the Random Forest Algorithm and the Taguchi method, Random Forest Algorithm fits more closely to the experimental result. Random Forest Algorithm prediction is closer to experimental result by about 6.18 percentage points compared to the Taguchi method prediction.

Quantification of bedload transport in the Hungarian Danube using multiple analysis methods

While bedload transport is crucial to river morphodynamics, quantifying it in large rivers, such as the Danube, poses significant challenges. The current study focuses on the gravel-dominated section of the Danube River in Hungary, where multiple methods have been concurrently applied to evaluate bedload transport. In a representative cross section of the river, the following methods were applied: (i) point-wise direct physical sampling using a BfG-type pressure difference sampler, quality controlled with a video camera mounted on the sampler; (ii) indirect acoustic-based analysis with an Acoustic Doppler Current Profiler (ADCP), evaluating the Bottom Tracking signal; and (iii) indirect acoustic and imaging-based method utilizing Acoustic Mapping Velocimetry (AMV). The ADCP method is calibrated using results from the physical sampling, while the AMV method relies on bedform tracking and is entirely independent from the two other techniques. The results from a series of measurement campaigns, covering a wide range of flow regimes, suggest a robust bedload rating curve for the study section, enabling the estimation of bedload yields and its relation to morphological changes. In addition, the applicability conditions of the implemented methods are discussed.

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling

The circular economy has emerged as a crucial way for companies to achieve their sustainability goals. Numerous businesses, especially small and medium-sized enterprises (SMEs), are integrating circular-economy projects into their operations. However, this undertaking presents multiple challenges as many managers must grapple with constraints in resources and expertise. This study’s primary objective is to develop a process-oriented decision-making system designed to deal with complex circular-economy scenarios. The proposed analysis system can help SMEs identify the driving forces behind circular-economy principles and evaluate the intricate connections between these determinants, using a unique combination of multiple criteria decision analysis methods (i.e., cognitive mapping, and interpretive structural modeling). Collaborative sessions involving circular-economy experts were instrumental in refining the analysis system, and in-depth discussions with other specialists from the International Labor Organization further enriched this decision-support system. The findings include that circular-economy drivers can be grouped into five clusters: products, processes, policies/regulations, attitudes/behaviors, and communication/awareness. This structured breakdown provides SMEs with the tools to comprehend and address the pivotal factors that shape circular-economy initiatives. This pioneering study thus produced a comprehensive decision-making model attuned to the intricacies of the circular economy while highlighting the benefits of collaborative endeavors involving industry experts and global decision makers.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.