Identification Of Regions Research Articles

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

An Analysis of the Spatiotemporal Distribution and Influencing Factors of National Intangible Cultural Heritage along the Grand Canal of China

Intangible cultural heritage (ICH) reflects a region’s history and culture, serving as a significant indicator of regional identity and cohesion. The Grand Canal Basin in China is rich in historical traditions, containing a rich array of ICH resources. Analyzing the spatiotemporal distribution characteristics and influencing factors of ICH within the Grand Canal Basin of China can provide a scientific basis for developing cultural industries and promoting sustainable regional economic growth. This study employed GIS-based spatial analysis methods, including kernel density estimation, the mean nearest neighbor index, and standard deviation ellipse, to investigate the spatiotemporal distribution of 504 national-level ICH items (including extensions) in the Grand Canal Basin of China. The results demonstrate the significant spatial clustering of ICH, with concentrations in high-density regions, particularly at the northern and southern ends of the canal. There is significant regional disparity in the distribution of ICH, with an uneven quantity and structure, predominantly featuring traditional skills and traditional drama categories. The average centroid shift of ICH exhibits a north-to-south oscillatory trajectory. However, overall, it demonstrates a southward-moving trend. This study also underscores the impacts of urbanization, population density, economic development, and transportation infrastructure on ICH distribution. Among these factors, urbanization exerts the strongest influence on the spatial distribution of ICH. The impact of the natural environment is relatively minor; however, it remains a significant element that cannot be overlooked during development. This research offers valuable data and insights for local governments and institutions to formulate evidence-based strategies for the protection and sustainable utilization of ICH resources, promoting sustainable cultural and economic development in the Grand Canal Basin.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18 F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence-driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant. In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets.

DNA methylation is an epigenetic mark involved in the regulation of gene expression, and patterns of DNA methylation anticorrelate with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Toward this goal, we developed MethyLasso, a new approach to segment DNA methylation data. We use it as an all-in-one tool to perform the identification of low-methylated regions, unmethylated regions, DNA methylation valleys and partially methylated domains in a single condition as well as differentially methylated regions between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the agreement of the regions across tools, their number, size, level of DNA methylation, boundaries, cytosine-guanine content and coverage using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/bardetlab/methylasso.

Improving turbulence modeling for gas turbine blades: A novel approach to address flow transition and stagnation point anomalies

Accurate prediction of temperature and Heat Transfer Coefficient (HTC) distributions over gas turbine blades is crucial for the design process and life assessment of these components. Numerical studies of flow over gas turbine blades face significant challenges in accurately simulating two complex phenomena: (1) the transition of flow from laminar to turbulent, and (2) stagnation point flow at the leading edge. Many turbulence models tend to overpredict the temperature on turbine blades, leading to incorrect identification of hot-spot regions and, consequently, erroneous estimations of blade life. This paper investigates the performance of various turbulence models in simulating flow and heat transfer over gas turbine vanes. The study includes three full turbulence models, i.e., Spalart-Allmaras (SA), Shear Stress Transport k−ω (SST-kw), and v2−f (V2F), as well as two transitional models, i.e., Transition SST (Trans-SST) and k−kL−ω (k-kl-w). Simulation results indicate that the v2−f, Trans-SST, and k−kL−ω models can detect flow transition. However, the transition length and onset location predicted by the Trans-SST and k−kL−ω models do not align with experimental data. Conversely, the v2−f model suffers from over-predictions at the leading edge due to stagnation point anomaly. To address these issues and due to capacities of the V2F model, this study proposes two modifications to enhance the performance of the V2F model. First, the production term of turbulent kinetic energy is redefined to mitigate the stagnation point anomaly. Second, the model is recalibrated to improve the prediction of flow transition. The new model, named the Production Modified V2F (PMV2F) model, shows promising results in predicting temperature and heat transfer coefficients.

Spatially defined microenvironment for engineering organoids

In the intricately defined spatial microenvironment, a single fertilized egg remarkably develops into a conserved and well-organized multicellular organism. This observation leads us to hypothesize that stem cells or other seed cell types have the potential to construct fully structured and functional tissues or organs, provided the spatial cues are appropriately configured. Current organoid technology, however, largely depends on spontaneous growth and self-organization, lacking systematic guided intervention. As a result, the structures replicated in vitro often emerge in a disordered and sparse manner during growth phases. Although existing organoids have made significant contributions in many aspects, such as advancing our understanding of development and pathogenesis, aiding personalized drug selection, as well as expediting drug development, their potential in creating large-scale implantable tissue or organ constructs, and constructing multicomponent microphysiological systems, together with functioning at metabolic levels remains underutilized. Recent discoveries have demonstrated that the spatial definition of growth factors not only induces directional growth and migration of organoids but also leads to the formation of assembloids with multiple regional identities. This opens new avenues for the innovative engineering of higher-order organoids. Concurrently, the spatial organization of other microenvironmental cues, such as physical stresses, mechanical loads, and material composition, has been minimally explored. This review delves into the burgeoning field of organoid engineering with a focus on potential spatial microenvironmental control. It offers insight into the molecular principles, expected outcomes, and potential applications, envisioning a future perspective in this domain.

Core-Periphery Multi-Modality Feature Alignment for Zero-Shot Medical Image Analysis.

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose structure is organized by the core-periphery (CP) principle. This auxiliary CP network not only aligns medical image and text features into a unified latent space more efficiently but also ensures alignment driven by principles of brain network organization. In this way, our approach effectively mitigates and further enhances CLIP's zero-shot performance in medical image analysis. More importantly, the proposed CP-CLIP exhibits excellent explanatory capability, enabling the automatic identification of critical disease-related regions in clinical analysis. Extensive experiments and evaluation across five public datasets covering different diseases underscore the superiority of our CP-CLIP in zero-shot medical image prediction and critical features detection, showing its promising utility in multimodal feature alignment in current medical applications.

Screening and characterization of indigenous Saccharomyces cerevisiae and non-Saccharomyces yeasts isolated from Sicilian vineyards

The use of Saccharomyces cerevisiae as starter cultures in winemaking is widespread in all over the world. However, a growing interest in exploring native non-Saccharomyces yeast species as co-starters has emerged, driven by their potential to enhance wine complexity and regional identity. The aim of the present study was to investigate indigenous yeasts from Sicily, to select novel strains with promising technological traits. A comprehensive screening of indigenous S. cerevisiae and non-Saccharomyces yeasts was conducted across seven locations in Sicily, including the unique volcanic zone of Mount Etna. A total of 95 yeast isolates were obtained and genetically characterized by RFLP-PCR and sequencing of 5.8S-ITS rDNA. Isolates were screened for oenological traits and the most promising, belonging to Starmerella bacillaris, Hanseniaspora uvarum, and Metschnikowia pulcherrima species, were used for single and sequential fermentations in synthetic must. Results revealed a remarkable diversity of physiological traits within and among the species, regarding stress tolerance, enzymatic activity and growth at different temperatures. Notably, isolates of H. uvarum and St. bacillaris exhibited great ethanol tolerance, and the latter also strong glycerol production. Furthermore, M. pulcherrima exhibited a wide range of aroma-related enzymatic activities, particularly beta-glucosidase. The remarkable diversity, the phenotypic traits of the indigenous yeasts, represents a promise for the improvement of innovative fermentation strategies and for the development of wine indigenous starters related with the region's terroir.

Validating pretrained language models for content quality classification with semantic-preserving metamorphic relations

Context:Utilizing pretrained language models (PLMs) has become common practice in maintaining the content quality of question-answering (Q&A) websites. However, evaluating the effectiveness of PLMs poses a challenge as they tend to provide local optima rather than global optima. Objective:In this study, we propose using semantic-preserving Metamorphic Relations (MRs) derived from Metamorphic Testing (MT) to address this challenge and validate PLMs. Methods:To validate four selected PLMs, we conducted an empirical experiment using a publicly available dataset comprising 60000 data points. We defined three groups of Metamorphic Relations (MRGs), consisting of thirteen semantic-preserving MRs, which were then employed to generate “Follow-up” testing datasets based on the original “Source” testing datasets. The PLMs were trained using a separate training dataset. A comparison was made between the predictions of the four trained PLMs for “Source” and “Follow-up” testing datasets in order to identify instances of violations, which corresponded to inconsistent predictions between the two datasets. If no violation was found, it indicated that the PLM was insensitive to the associate MR; thereby, the MR can be used for validation. In cases where no violation occurred across the entire MRG, non-violation regions were identified and supported simulation metamorphic testing. Results:The results of this study demonstrated that the proposed MRs could effectively serve as a validation tool for content quality classification on Stack Overflow Q&A using PLMs. One PLM did not violate the “Uppercase conversion” MRG and the “Duplication” MRG. Furthermore, the absence of violations in the MRGs allowed for the identification of non-violation regions, confirming the ability of the proposed MRs to support simulation metamorphic testing. Conclusion:The experimental findings indicate that the proposed MRs can validate PLMs effectively and support simulation metamorphic testing for PLMs. However, further investigations are required to enhance the semantic comprehension and common sense knowledge of PLMs and explore highly informative statistical patterns of PLMs, in order to improve their overall performance.

Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained >0.5 % of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6 % using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18 %) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

Intelligent identification of defective regions of voids in tunnels based on GPR data

Quantitatively detecting voids behind tunnel linings presents significant challenges in identifying the range of width and depth. This paper develops an innovative method for identifying defective regions of voids based on Ground Penetrating Radar (GPR) data. This method involves three steps: Firstly, the void-identifying-feature-set (VIFS) is constructed by extracting the Amplitude peak (AT), Signal energy (ET), and Amplitude peak of the first intrinsic mode function (IMF1) component (AH) of every A-scan signal. Secondly, the Support Vector Machine (SVM) is utilized to identify defect signals and normal signals, contributing to the width identification of void in the horizontal direction. Thirdly, an innovative Three-Stage-Boundary-Extraction (TSBE) algorithm is proposed to identify the depth range of voids in the vertical direction. Experimental results conducted on both field data and simulated data demonstrated that the Intersection over Union (IOU) value and consumption time of three groups of GPR data (Data I, Data II, and Data V) are 0.739 and 0.888 s, respectively. The average IOU and consumption time of the TSBE algorithm are 0.739 and 0.058 s, respectively.

Never eat foods that are not in season: The commodification and authentication of gastronomic identity in tourism

Gastronomic identity is an emerging concept with considerable explanatory power in understanding cultural and social dynamics. This study investigates the meaning of gastronomic identity and the cultural and social processes involved in its touristic commodification. The study was conducted in Suzhou, China, using a multi-method qualitative approach comprising participant observation, secondary document analysis, and semi-structured interviews. Analysis reveals that the essence of Suzhou's gastronomic identity lies in seasonality, periodic changes in local food supply and demand. The commodification of this seasonal feature has transformed into a cultural allure for visitors. Stakeholders strategically leverage seasonal selections and culinary presentations to authenticate the regional gastronomic identity. However, these efforts are undermined by unethical food business practices—disregarding seasonality and engaging in premature food production—that threaten seasonal food culture's authenticity. This study enhances our understanding of the place-based meaning of gastronomic identity and its commodification and authentication in tourism.

El Interfluvio Petorca-Quilimarí 30 años después

Examining an area between two zones that research historically regarded separately—Central Chile and the country’s semi-arid north—presents an opportunity to consider, on several socio-spatial scales, the various degrees and characteristics of integration shown by the communities in question. In this article, we use excavation and material analyses from seven archaeological sites in Huaquén, located to the north of the Petorca and La Ligua river mouths. Given current research perspectives, the results are explored in light of their connections to the developments to each zone. There are three distinct components. The first is an Archaic component consisting of a residential camp focused on procuring marine fauna, dated to the late mid-Holocene (5000 cal. B.P.). An Early Ceramic component consisted of diverse extensive, ephemeral occupations, including a burial. Finally, a Historical component included what is likely Hispanic-Indigenous ceramics. In light of recent research perspectives, the article discusses the concept of ‘borders’ and suggests viewpoints that account for the existence of several local communities with different degrees of affiliation to regional social identities.

Exploring the Role of Folklore in Sustaining Cultural Tourism in Meghalaya

Meghalaya, known for its colourful customs, extensive oral history, and breathtaking scenery, has emerged as a major destination for cultural tourism in Northeastern India. The region's identity is fundamentally shaped by the rich history of customs, rituals, and folklore upheld by its indigenous groups, which are predominantly the Khasi, Garo, and Jaintia tribes. In Meghalaya, cultural tourism has grown to be a substantial industry, drawing tourists from both home and abroad who are interested in learning more about the distinctive traditions, celebrations, artwork, folklore storytellers, and way of life of the local tribes. This study explores the function of cultural tourism in Meghalaya, emphasizing how it advances sustainable tourism, cultural heritage preservation, and socioeconomic development. The state’s cultural tourism revolves around festivals, traditions, and folklore, these celebrations provide guests with the excitement of Meghalaya's dynamic culture as ancient myths, stories, songs, enchanting festivals, and traditional ceremonies, which play a vital role in supporting and advancing cultural tourism and provides an unforgettable adventure into the core of indigenous heritage. Furthermore, the study looks at how folklore tourism gives local people the chance to become more economically empowered through guided storytelling and ensuring the preservation of tourism centred around the folklore stories of Meghalaya which is crucial for sustaining the strong cultural legacy of its native communities, providing tourists with a more profound link to the ancient traditions and intriguing legends of the region.

Identification and Spatial Characterization of suburban areas in Chengdu

As transitional territories where urban and rural functions interpenetrate, suburban areas have multiple values such as recreation, ecology, agriculture and landscape. Promoting their benign development is the key to realizing global sustainable development of urban and rural areas. Planning control is based on precisely defining the spatial extent of suburban areas, and understanding suburban area differentiation and its driving forces scientifically is a key component in raising the bar for focused planning. However, there are currently few research on fine-scale quantitative identification of suburban regions because of their spatial complexity, ambiguous boundaries, and structural dynamic features. Based on this, this paper develops a multi-dimensional identification index system for suburban areas by using multi-source big data and remote sensing information technology, employs the random forest model and the K-Medoids clustering algorithm, identifies the distribution of suburban areas and their subdivided types, analyzes spatial differentiation characteristics, and conducts empirical research using Chengdu City as an example. The findings demonstrate that: (1) The majority of suburban areas in Chengdu City are concentrated in groups or belts surrounding the urban centers of various districts and counties, and the higher the functional class of a district or county, the denser the distribution of suburban areas. (2)The distribution pattern of suburban areas in Chengdu basically conforms to its planned urban hierarchy system.(3)Urban-oriented, suburban-interacted and rural-oriented suburban areas have their own characteristics in land use level, economic development and regional population characteristics.(4)Regional openness, government behavior, social development and geographical location are the core driving factors of spatial differentiation of suburban areas in Chengdu, and the interaction between urban and rural systems, the flow of social resources and the transformation of development models are the spatial mapping dimensions that affect its differentiation.(5)Chengdu can draw up detailed regulatory planning of suburban units to standardize and guide the overall development of suburban areas. In support of the global sustainable development goals, this study offers a methodology for precisely and impartially defining suburban areas, assisting in the implementation of integrated urban-rural development globally.

Systematic Review of Traditional Plant Motifs for National Identity Based on Digital Technology Landscape Platform

The incorporation of Malaysian character into landscape design via traditional landscape characteristics reflecting the region's identity has been inhibited by designers' lack of initiative in incorporating natural resources. This article investigates the plant traits that create a symbiotic relationship between native plants, notably flowers, and the feeling of place they inspire. Furthermore, the study emphasizes (a) the importance of traditional plant motifs in shaping national identity and landscape awareness, (b) their use in landscape design to create a profound sense of place, and (c) the role of traditional plant motifs in promoting sustainable landscapes, biodiversity, and environmental conservation. A thorough assessment and analysis of current studies on traditional plant motifs and their contribution to landscape identity were done to attain this goal. Scopus, Econbiz, and Mendeley indexed the reviewed literature. This research gives insights into the evolution of plant motifs for identification, especially concerning the landscape and cultural heritage, by reviewing historical methods published from 2013 till 2023. The results highlight the necessity of using traditional plant themes in landscape design to develop a strong national identity and raise environmental awareness in the digital age.

Multi-phase weighted stator current tracking using a hyper-plane partition of the control set

This paper presents a new optimization technique for Finite Control Set Model Predictive Control (FCS-MPC) of multi-phase induction machines. The proposal allows for a fast computation of the control signal while simultaneously considering stator currents in the α−β (torque producing) plane and the x−y (harmonics) plane. The combinatorial search of the optimization phase is eliminated. Also, unlike previous approaches, the proposed region identification algorithm can be utilized in conjunction with weighting factors in the cost function. The optimization is done using a hyper-plane partition of the control set. The partition provides the weighted FCS-MPC solution with significantly less computation needs allowing for shorter sampling periods. The method is tested experimentally in a 5-phase induction motor showing enhanced results.

The Role of Language in Forming Identity Crisis in Belgaum District of India

The Belgaum District in India provides a unique case study of phonetic and identity challenges. This area, characterized by its diverse linguistic landscape, is marked by significant cultural and political tensions rooted in language differences. The Marathi-Kannada linguistic divide has been a central issue, affecting the socio-political dynamics and identity of its residents. This paper explores the historical, cultural, and political factors contributing to the ongoing conflict and investigates the pivotal role of language in shaping the identity crisis in Belgaum District. By analysing the relationship between language, regional identity, and socio-political movements, the study highlights the broader implications for regional unity and identity development, emphasizing the role of language in forming group identities and the challenges posed by linguistic diversity in building a cohesive regional identity.

The Observed Spatiotemporal Variability of Antarctic Winter Water

AbstractThe Southern Ocean (SO) is central to the global overturning circulation. South of the Antarctic Polar Front, Antarctic Winter Water (WW) forms in the wintertime mixed layer (ML) and becomes a subsurface layer following summertime restratification of the ML, overlaying upwelled deep waters. Model simulations show that WW acts as a conduit to seasonally transform upwelled deep waters into intermediate waters. Yet, there remains little observational evidence of the distribution and seasonal characteristics of WW. Using 18 years of in situ observations, we show seasonal climatologies of WW thickness, depth, core temperature, and salinity, revealing a distinct regionality and seasonality of WW. The seasonal cycle of WW characteristics is tied to the annual sea ice evolution, whereas the spatial distribution is impacted by the main topographic features in the SO driving an equatorward flux of WW. Through the identification of these localized northward export regions of WW, this study provides further evidence suggesting an alternative view from the conventional “zonal mean” perspective of the overturning circulation. We show that specific overturning pathways connecting the subpolar ocean to the global ocean can be explained by ocean‐topography interactions.