Conventional Analysis Methods Research Articles

Abstract A003: CD45 positive circulating tumor cells as a prognostic marker in prostate cancer

Abstract Prostate cancer is one of the most common types of cancer among male patients worldwide, making early diagnosis and effective treatment crucial for improving patient survival rates. In this regard, circulating tumor cells (CTCs) have garnered attention as an important biomarker for cancer staging and monitoring treatment response. In prostate cancer, CTCs are typically identified as keratin+/CD45- cells, with additional markers such as PSA or PSMA also being used. The conventional CTC identification way have dismissed CD45+/keratin+ double-positive cells as artifacts by nonspecific binding during the staining process. However, recent studies have reported the presence of CD45+/keratin+ CTCs in various types of cancers, highlighting metastatic potential and their ability to evade the immune system. These findings require re- examination of the role for the double positive CTCs. Therefore, this study aimed to identify the presence of CD45+ CTCs in prostate cancer and to assess their clinical significance. Blood samples were collected from 80 prostate cancer patients, followed by unbiased isolation of CTCs, which were stained using PSA, keratin, and CD45. The detected CTCs were categorized based on CD45 expression, and the clinical relevance of each group was validated using patient information. Through this study, we aimed to confirm whether the CD45+ CTCs can be a clinical indicator in prostate cancer. The results revealed that CD45+ CTCs accounted for 90% of all detected CTCs, likely due to their high immune evasion capabilities. Additionally, in 17% of the patients, only CD45+ CTCs were detected, indicating the potential to expand CTC detection even in patients where CTCs were previously undetected using conventional criteria. The number of CD45+ CTCs was significantly higher in patients with progressive disease compared to those with complete or partial response, showing greater statistical significance than analyses using CD45- CTCs. Furthermore, the number of PSA+ CTCs was closely linked to changes in plasma PSA levels, suggesting they could be used as a supporting marker to enhance the accuracy of current plasma PSA tests. These findings indicate that CD45+ CTCs in prostate cancer are not merely artifacts of nonspecific binding but are indeed meaningful cells closely related to cancer progression. This study suggests that CD45+ CTCs have the potential to be utilized as a prognostic biomarker in prostate cancer. By including CD45+ CTCs, which were previously overlooked in conventional CTC analysis methods, more sensitive and accurate cancer monitoring could be achieved. In-depth future studies on the clinical applicability of CD45+ CTCs could significantly contribute to predicting prognosis and developing personalized treatment strategies for prostate cancer patients. Citation Format: Hyeongjung Woo, Woon-Hae Kim, Hyun Young Shin, Hyerin Jung, Hyung Ho Lee, Minseok S. Kim, Jae Young Joung. CD45 positive circulating tumor cells as a prognostic marker in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A003.

Nursing students' experiences of professional competence evaluation by Objective Structured Clinical examination method: a qualitative content analysis study.

Clinical education is a significant part of medical education, and paying attention to clinical evaluation is important. One of the challenges in the teaching-learning process is evaluating students' performance. Learners, as the main stakeholders of the educational system, may have different experiences of evaluation quality. Awareness of these experiences is effective in improving the quality of clinical evaluation. Therefore, this study was conducted to "explore the experiences of nursing students for evaluation of professional competence by using the objective structured clinical examination (OSCE)". This study was conducted with a qualitative descriptive research approach and conventional content analysis method in 2022-2024. The participants included 12 undergraduate nursing students at Maragheh University of Medical Sciences, who were selected by purposeful sampling, and their experiences were collected using semi-structured and in-depth interviews until reaching data saturation. The data analysis of the interviews led to the extraction of 268 primary codes, 7 subcategories, 2 categories, and 1 theme: "Credibility and stability". The category "Exam's accuracy in measuring competence " included 3 sub-categories: "Challenges in objective adaptation ", " Communication and organizational challenges for exam preparation " and " Inadequate simulation of stations and exam environment ", and the category " Exam power for repeatability" included 4 sub-categories: " Characteristics of the students ", " Lack of evaluators' skills and mastery ", " Inefficiency of the evaluation tool " and " Disturbance in executive affairs". OSCE can be used in self-evaluation, creating motivation and strengthening different dimensions of students' learning, as well as discovering weaknesses and strengths for planning by managers and faculties. According to the results of this study, many factors such as management before and during the exam, characteristics of the evaluators, prevailing educational conditions in the faculty, and the method of clinical training are effective in achieving the "reliability and sustainability" required in the OSCE.

Exploring the experiences of medical students regarding the factors affecting academic achievement: a qualitative study.

Academic achievement is one of the most important criteria in assessing students' ability to complete their university studies and reach the graduation stage. Students in medical sciences are considered the primary recipients of educational services and are the best source for identifying problems in medical or clinical education, as they are directly and indirectly involved in this interaction process. This study was conducted in the years 2022-2023 using conventional content analysis at Tabriz University of Medical Sciences. A total of 23 students from various faculties of this university were selected through purposive sampling, and their experiences and perspectives were utilized until data saturation was reached. Data was collected through semi-structured, in-depth individual interviews using an interview guide. Content analysis was performed using the conventional content analysis method. Two main themes were extracted with the titles "Facilitators of Academic Achievement" and "Barriers to Academic Achievement ". "Teacher-related Factors," "Assessment and Evaluation," "Student-related Factors," "Learning Environment," and "Support Systems" were the categories derived from the analysis of the data for academic achievement, and "Poor Educational Quality and Teaching Methods," "Lack of Interdisciplinary Collaboration and Integration," "Inappropriate Educational Planning and Administration," "Communication Issues," and "Contextual barriers," were the categories derived for "Barriers to Academic achievement" based on 238 codes and 36 subcategories. Since factors such as satisfaction with educational and welfare facilities and academic motivation impact the quality of education, examining these variables from students' perspectives is beneficial for formulating regulations, providing appropriate facilities, planning for the training of capable students, and selecting suitable educational and learning processes.

Laser ablation inductively coupled plasma mass spectrometry measurements for high-resolution chemical ice core analyses with a first application to an ice core from Skytrain Ice Rise (Antarctica)

Abstract. Conventional methods of inorganic impurity analysis do not provide high enough depth resolution for many scientific questions in ice core science. In this study, we present a setup of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for high-resolution glacier ice impurity analysis to the sub-millimetre scale. This setup enables ice core chemical impurity analysis to a depth resolution of ∼182 µm while consuming only very small amounts of ice. The system performs simultaneous analysis of sodium, magnesium and aluminium incorporated in the ice matrix. In this case study within the framework of the WACSWAIN (WArm Climate Stability of the West Antarctic Ice Sheet in the last INterglacial) project, our method is applied to a selection of samples from the Skytrain ice core (West Antarctica) over a total length of 6.7 m consisting of about 130 single samples. The main goal of this study is to use the new LA-ICP-MS method to extract meaningful climate signals on a depth resolution level beyond the limits of continuous-flow analysis (CFA). A comparison between low-resolution CFA data and the high-resolution LA-ICP-MS data reveals generally good agreement on the decimetre scale. Stacking of parallel laser measurements together with frequency analysis is used to analyse the high-resolution LA-ICP-MS data at millimetre resolution. Spectral analysis reveals that despite effects of impurity accumulation along ice crystal grain boundaries, periodic concentration changes in the Skytrain ice core on the millimetre scale can be identified in ice from 26.8 ka (kiloyears before present, i.e. 1950 CE). These findings open new possibilities for climate data interpretation with respect to fast changes in the last glacial period and beyond, for example within the Beyond EPICA oldest-ice project.

Research on Damage Detection of Dual-Rotor Synchronous Excitation Mine Screen Beams Based on Strain Mode Difference Vibration Mode Analysis

The frame beam structure of the mine screen, subjected to various excitations, is a critical component of mining machinery. Its stress is intricate, the operational environment is severe, and damage can lead to catastrophic failures resulting in machinery destruction and fatalities. Based on the characterization of the vibration response of mine screen frame beams with varying degrees of damage at the same location and with the same degree of damage but at different locations, this paper develops a method of strain modal difference vibration pattern analysis and damage feature extraction for the detection of structural damage in beams. This method is based on the sensitivity of the sudden change in vibration strain modal difference to small deformations. This method solves the problem of using the conventional structural finite element analysis or experimental modal analysis methods to obtain the displacement mode, intrinsic frequency, and other characteristics, which make it difficult to effectively identify the actual engineering, with the damage conditions of the damage state and damage location of the mine screen frame beam problems. The feasibility and validity of the engineering application of the concept are demonstrated through instances.

Cost‐effective amperometric sensor for monitoring levofloxacin in groundwater

AbstractThe presence of water micropollutants, such as antibiotics, has proved the necessity to develop novel and cost‐effective devices for their identification and quantification. These devices aim to save time, reagent usage, and costs associated with conventional analytical methods. In this work, we introduce poly(methylene blue) based screen‐printed electrodes (SPE‐PMB) as electrochemical sensors designed for the quantification of levofloxacin (LVX), given its current prevalence as a micropollutant. Integrating the fabrication and measurement processes into a single electrochemical device is a significant step in creating affordable detection tools. The proposed sensor was assessed using LVX solutions prepared in real groundwater samples, demonstrating its selectivity and achieving a detection limit of 3.3 μM. Finally, we compared the SPE‐PMB sensor and high‐performance liquid chromatography (HPLC) to validate its operation and performance. Consequently, our results suggest that the sensor can be a viable alternative to chromatographic methods for identifying and quantifying micropollutants at very low concentrations in complex matrices.

Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment.

Performance analysis of passive heave compensator operated with inclined piston using analytical method.

When performing operations such as drilling in the ocean, floating platforms undergo vertical motion due to waves, which negatively impacts these operations. So passive heave compensator(PHC) is used to counteract it. In many studies, the analysis of PHC with pneumatic springs has been done. Some of these papers analyzed it using simple second-order differential equations, but there was no case of modeling considering the pneumatic spring installed tilted in the PHC. On the other hand, in some papers, the characteristics were analyzed by numerical methods using multi-body dynamics equations, and it is time consuming. Therefore, in this paper, a second-order ordinary differential equation modeling method for PHC with an inclined pneumatic spring was presented and verified by comparison with the numerical method. Using the model presented in this paper, it is possible to analyze as accurate as conventional analytical methods, and to analyze characteristics faster than numerical methods.

Insights into pharmaceutical co-crystallization using coherent Raman microscopy

Formulating active pharmaceutical ingredients (APIs) as co-crystals requires a thorough understanding of co-crystallization behavior under different process conditions. This study employs two forms of coherent Raman microscopy, narrowband coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) with spectral focusing, to study co-crystallization via liquid-assisted ball milling. Indomethacin and nicotinamide served as the model API and co-former, and the results were compared with established analytical methods. Narrowband CARS, with univariate peak position analysis, was useful to visualize co-crystal formation, but suffered some degree of signal mixing that affected component identification. Hyperspectral SRS imaging, combined with classical least squares multivariate analysis, separated the different components with high confidence and proved to be a robust and rapid tool to qualitatively and quantitatively image co-crystallization. The coherent Raman imaging results explained divergent co-crystallization endpoints obtained with the conventional solid-state analysis methods. CARS and SRS microscopies also revealed the presence of otherwise undetected trace forms. Finally, we also demonstrated the dramatic reversal of partial co-crystal formation during milling, depending on ethanol content. Overall, the study demonstrates the added value coherent Raman microscopy can provide for analysis of co-crystallization processes.

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

Ontology modeling for automation of questionnaire data processing

The object of this study is the analysis of questionnaire data using ontological modeling. The task relates to the fact that conventional methods for processing questionnaire data are often insufficiently effective when working with large volumes of information and do not make it possible to automate many analysis processes. As a result of the study, an ontology was designed that structures and analyzes questionnaire data, which allows for a more accurate identification of hidden relationships between variables. Using these theoretical provisions, an information system for assessing the quality of assimilation of preschool children's competencies was built. 150 children from various preschool organizations were involved in the study as respondents. The data integration method proposed in this paper significantly facilitated the process of data analysis both for a group and for an individual respondent. The key difference of the proposed methodology is the automation of routine data analysis operations based on the ontological structure, which significantly simplifies the processing of large volumes of information. This makes it possible to solve the problem of limitations in conventional analysis methods and makes data analysis more scalable and reproducible. The practical application of the results is possible in marketing for analyzing customer satisfaction, market segmentation, and evaluating the effectiveness of advertising campaigns. In the educational domain, the ontology could be used to evaluate the quality of programs and analyze respondents' opinions, and in sociology – to analyze public opinion and conduct research on social phenomena. Thus, the proposed ontology provides an effective tool for analyzing large volumes of questionnaire data, allowing organizations to make more informed decisions and improve their efficiency

Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study

BackgroundArtificial intelligence (AI) is revolutionizing cephalometric diagnosis in orthodontics, streamlining the patient assessments. This study aimed to assess the reliability, accuracy, and time consumption of artificial intelligence (AI)-based software compared to a conventional digital cephalometric analysis method on 2D lateral cephalogram.Methods408 lateral cephalometries were analysed using three methods: manual landmark localization, automatic localization, and semi-automatic localization with AI-based software. On each lateral cephalogram, 15 variables were selected, including skeletal, dental, and soft tissue measurements. The difference between the two AI-based software options (automatic and semi-automatic) was compared with the conventional digital technique. The time required to produce a complete cephalometric tracing was evaluated for each method using Student’s t-test.ResultsStatistically significant differences in the accuracy of landmark positioning were detected among the three different techniques (p < 0,01). However, it is noteworthy that almost all of these differences were not clinically significant. There was a small difference in accuracy between the semi-automatic AI-based option and conventional digital techniques. Regarding the time used for each technique, the automatic version was the fastest, followed by the semi-automatic option and the conventional digital technique. (p < 0,000).ConclusionsThe study showed a statistical difference in accuracy between the conventional digital technique and two AI-based software alternatives, but these differences were not clinically significant except for specific measurements. The semi-automatic option was more accurate than the automatic one and faster than conventional tracing. Further research is needed to confirm AI’s accuracy in cephalometric tracing.

Long-Term Effects of Thinning in Sub-Mountainous Thermophilic Sessile Oak (Quercus petraea Mill.) and European Beech (Fagus sylvatica L.) Coppices in the Croatian Dinarides

Coppicing has been neglected in recent decades, leaving gaps in knowledge on silvicultural interventions, especially the long-term effects on coppices of Southeast Europe. The recent economic crisis, the sudden increase in energy prices, and the increased demand for assorted wood products have initiated higher interest in coppices in Croatia. Thus, our work aims to define the long-term effects of thinning in sessile oak (Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.) sub-mountainous thermophilic low coppices in the Croatian Dinarides. The experiment includes two localities with thinning and control plots. Thinning was performed in 2002, with 17.71% of wood volume removed in the European beech coppice and 26.09% in the sessile oak coppice. In 2020, 1276 trees were marked, measured (DBH, tree height, number of stems per stump), and assessed for vitality, origin, and six silvicultural features revealing tree quality. In 2022, trees were again measured (DBH, tree height) to gain data on tree growth. Conventional data analysis methods were used. The results show a statistically significant positive long-term effect of thinning on tree growth, stem, and crown features and support findings that thinning, by increasing growth and quality, is a necessary intervention in European beech and sessile oak low coppices. Thinning promoted the healthiest and best quality trees/stems, resulting in a more valuable range of wood products and the potential for higher income. It had a positive impact on tree growth regardless of the tree’s origin (seed or stump) and improved the growth and quality of trees among the supporting tree species. Short-term effects of thinning in trial plots suggested that thinning supported tree species diversity, but this effect diminished over time, showing no positive effect after twenty years. The study points to the need for more frequent thinning in coppices to support tree species diversity (i.e., to allow growth to less competitive tree species) but underlines the need for further research.

Repertory Grid Technique and Honey’s Content Analysis: gathering and processing qualitative data

Purpose This study aims to demonstrate a rigorous approach to applying the Repertory Grid Technique (RGT) and Honey’s Content Analysis (HCA) to obtain and process qualitative data through structured interviews. Design/methodology/approach An illustrative case study using the OpenRepGrid package from the open-source software R facilitates a deeper understanding of these techniques. The study subjects were employees of a corporate charter company. Findings The RGT enables the identification of key attributes as perceived by interviewees regarding the phenomenon, whereas HCA clarifies how these attributes impact the desired analysis outcome. The presented case study identified constructs related to the client–supplier relationship and their impact on service performance from the provider’s perspective. Research limitations/implications This study illustrates the use of qualitative methods based on an interpretative naturalistic approach to rigorously and systematically capture interviewees’ perspectives. Practical implications The combination of RGT and HCA can be a valuable tool for management studies by allowing controlled researcher interference in empirical investigations. In addition, the data-driven selection of constructs by interviewees can lead to the emergence of novel theories. Social implications Using diverse methodologies enables researchers to address complex managerial challenges that often surpass the capabilities of conventional analysis methods. Originality/value The proposed methodology offers a robust understanding of phenomena from the interviewees’ perspectives. Consequently, this study highlights the potential of these techniques for theoretical and empirical research in the field of administration.

Energy Dispersive X-Ray Fluorescence Spectrometry Using a Matrix Corrected Fundamental Parameters Algorithm vs. Acid Digestion with ICP-AES: A Comparison of Two Methods for Soil Elemental Analysis

ABSTRACT Energy dispersive X-ray fluorescence spectrometry (ED-XRF) is evaluated as an alternative to conventional acid-mediated soil digestion methods for analysis of aluminum (Al), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P), silicon (Si), strontium (Sr), titanium (Ti), and zirconium (Zr). The Kellogg Soil Survey Laboratory (KSSL) currently uses microwave-assisted digestion with inductively coupled plasma – atomic emission spectrometry (ICP-AES) to measure the 12 elements (Al, Ca, Fe, K, Mg, Mn, Na, P, Si, Sr, Ti, Zr). The equipment needed to conduct the digestion method is expensive, the materials are hazardous, and the method is laborious. The Kellogg Soil Survey Laboratory (KSSL) has observed variable elemental recoveries for certain elements. The objective of the study was to evaluate an ED-XRF method as an alternative to the KSSL digestion method. The literature offers few examples of X-ray fluorescence (XRF) method development and validation. The candidate ED-XRF method offered a more efficient, less expensive, and less hazardous approach for measuring the 12 elements. Relative to the digestion method, the candidate ED-XRF method showed improved precision and accuracy for specific elements.

Study of vowel formant data using Gabmap: A pilot analysis of a Lithuanian dialect

Abstract The article reviews the possibilities of applying the web application Gabmap developed by researchers at the University of Groningen to the analysis of vowel formant data based on the results of the study of one Lithuanian dialect. The Western Aukštaitian dialect of Kaunas is traditionally considered the basis of Standard Lithuanian. The study was based on the data of 18 male speakers from six dialectal localities representing distinctive areas. The normalized mean values of the first two vowel formants (F1, F2) were analyzed in certain word positions illustrating distinguishing features, which were processed using Gabmap tools designed for analyzing numeric sequences. Gabmap tools most beneficial for the researchers of experimental phonetics along with the aspects of their application to research were emphasized in the study; it also underlined those tools the use of which requires additional application of other conventional methods of experimental sound analysis, such as sound spectrum analysis, planimetric models, etc. In the study of the Western Aukštaitian of Kaunas, the Gabmap analysis confirmed significant horizontal variation within the dialect area and foregrounded the most tightly related sites sharing the greatest number of common features in their vocalism.

Exploring Barriers to Attending Zhengzhou University from the International Nursing Students’ Viewpoints in China: A Qualitative Study

Background: Developing nursing capacity worldwide includes training nurses to the doctorate level. Due to a global shortage of PhD nursing programs, many nurses are pursuing their doctorates abroad. International nurse students frequently face some barriers because of socio-cultural and other differences. As far as we are concerned, there is limited research on the barriers experienced by international nursing students. Objectives: This study aimed to explore the barriers to attending Zhengzhou University from the viewpoint of international nursing students in China. Methods: The study employed a qualitative approach among international nursing students at Zhengzhou University, China. Participants were selected using purposeful sampling, and the sample size was determined by information saturation. A semi-structured interview guide and a conventional content analysis method were used. Results: In this study, ten international nursing students were interviewed. All participants expressed their concerns regarding the barriers experienced in China. Two major categories were identified from the student data: (1) situations outside the school/university (skin color, language, and socio-cultural differences); and (2) those inside the school/university (expectations and realities of learning experiences, supervisory concerns, and lack of support). Conclusions: This study indicated that international nursing students experienced different barriers than international students at Zhengzhou University. A secure learning environment is required to adapt and flourish in an educational teaching society. The university/school should create a culturally competent and suitable environment for international nursing students.

Determination of steady-state for continuous powder mixing: Analysis of mixture properties and signal processing

Continuous powder mixing offers many advantages over batch processes, but its industrial implementation requires control of transient phases due to startup or variations in operating conditions. Detecting the steady-state by observing the mixture properties, obtained through in-line analysis of the mixture composition, is challenging due to the significant fluctuations in these properties. However, there is no quantitative method for identifying the steady-state for continuous powder mixing. We therefore propose in this work a methodology for steady-state detection by comparing several methods of mixture property analysis and signal processing. We show that conventional methods of mixture property analysis cannot reliably and accurately detect steady-state. We conclude that the best methodology is to perform signal processing on the content of the key component in the mixture. To do this, the signal of the content of the key component is first smoothed by the Savitzky-Golay filter. Then, the standard deviation of the filtered signal is compared with a tuning parameter to detect the beginning of steady-state. This method could be used to determine the mixing conditions leading to the smallest startup time, hence avoiding costly waste.

Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district

In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.

Modelling of Steady-State Seepage of an Embankment Dam Using Teaching-Learning Based Optimization Algorithm

The goal of the this study is to investigate the applicability of the teaching-learning based optimization (TLBO) algorithm for modeling seepage in embankment dams. The input parameters selected for the models to be built are the values of permeability (ks), van Genuchten's suitability parameters α and n, whose effect on seepage has been investigated over the years due to their uncertainties. The validity of the TLBO was compared with that of conventional regression analysis (CRA) methods. Both methods were utilized with different regression forms. The parameters chosen as input are modeled as random variables with a log-normal distribution, and total discharge (Q) was obtained. Four statistical indices, that is, root mean square error, mean absolute error, average relative error and coefficient of determination, were used to evaluate the performance of the models. The equations obtained using TLBO algorithms can predict the total discharge in embankment dams better than CRA. In addition, the reliability of TLBO has been demonstrated by conducting analyses using the outputs of CRA as a benchmark.