Identification Of Regions Research Articles

Cellular and molecular mechanisms that shape the development and evolution of tail vertebral proportion in mice and jerboas.

Despite the functional importance of the vertebral skeleton, little is known about how individual vertebrae elongate or achieve disproportionate lengths as in the giraffe neck. Rodent tails are an abundantly diverse and more tractable system to understand mechanisms of vertebral growth and proportion. In many rodents, disproportionately long mid-tail vertebrae form a 'crescendo-decrescendo' of lengths in the tail series. In bipedal jerboas, these vertebrae grow exceptionally long such that the adult tail is 1.5x the length of a mouse tail, relative to body length, with four fewer vertebrae. How do vertebrae with the same regional identity elongate differently from their neighbors to establish and diversify adult proportion? Here, we find that vertebral lengths are largely determined by differences in growth cartilage height and the number of cells progressing through endochondral ossification. Hypertrophic chondrocyte size, a major contributor to differential elongation in mammal limb bones, differs only in the longest jerboa mid-tail vertebrae where they are exceptionally large. To uncover candidate molecular mechanisms of disproportionate vertebral growth, we performed intersectional RNA-Seq of mouse and jerboa tail vertebrae with similar and disproportionate elongation rates. Many regulators of posterior axial identity and endochondral elongation are disproportionately differentially expressed in jerboa vertebrae. Among these, the inhibitory natriuretic peptide receptor C (NPR3) appears in multiple studies of rodent and human skeletal proportion suggesting it refines local growth rates broadly in the skeleton and broadly in mammals. Consistent with this hypothesis, NPR3 loss of function mice have abnormal tail and limb proportions. Therefore, in addition to genetic components of the complex process of vertebral evolution, these studies reveal fundamental mechanisms of skeletal growth and proportion.

Reimagining the Indo-Pacific: a critical Chinese perspective on regional identity and International Relations

Reimagining the Indo-Pacific: a critical Chinese perspective on regional identity and International Relations

Flow Field Analysis within Industrial Quenching Tanks Operated with Aqueous Polymer Solutions in the Steel Industry

Abstract Quenching with polymer solutions for steel heat treatment offers adjustable performance and improved ecological impact compared to conventional water or oil quenching. The submersion cooling operation poses challenges due to the intense vapor/polymer film collapse on the component surface, potentially destabilizing batch components. Therefore, controlled recirculation is critical to preventing tank and specimen damage during cooling and ensuring effective steel hardening. However, flow structure analysis is often overlooked and disregarded in industrial tank design. Thus, this study evaluates the tank geometries of two partner industries to determine if they provide the necessary homogeneous flow for optimal quenching. The analysis combines experimental velocity measurements with a numerical model, with the identification of different flow intensity regions being the main outcome of this work. The results suggest that geometric modifications could improve flow recirculation, enhancing quenching performance.

Aerosol Vertical Turbulent Mass Flux Retrievals Through Novel Remote Sensing Algorithm

AbstractIntegrated measurements of aerosol, radiation, cloud, and turbulent transport in the planetary boundary layer (PBL) are essential for understanding and modeling climate and air quality. Here, we developed a new technique for the identification of convective turbulent regions and deriving the vertical distribution of aerosol turbulent mass fluxes within PBL. The algorithm uses retrievals from coherent Doppler lidars and a high spectral resolution lidar. The technique was applied to study particle mass fluxes over 2 months (November–December 2020) during the campaign conducted at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Lamont, Oklahoma. The algorithm developed here is capable of continuously deriving vertically resolved (curtains) aerosol mass fluxes. Our data analysis shows that at the site, the 30‐min averaged fluxes at 135 m above the surface were mainly positive (upward) at ∼1 μg m−2 s−1, suggesting that the surface is the primary source of the particle mass supplied to the boundary layer at the SGP site. Analyses of the individual case studies have revealed that not all the derived fluxes can be linked to surface emissions. Both positive and negative values in a range of ±5 μg m−2 s−1 can be caused by convective thermals interacting between the residual layer and the mixed layer and by rotation of the horizontal wind with the height. Large erroneous negative fluxes can also be caused by drizzling/precipitating clouds. We anticipate that the application of the current technique will lead to a more realistic representation of aerosol mass budgets and bidirectional mixing rates.

Identification of SNP and SilicoDArT Markers and Characterization of Their Linked Candidate Genes Associated with Maize Smut Resistance.

The implementation of biological advancements in agricultural production is the response to the needs of the agricultural sector in the 21st century, enabling increased production and improved food quality. Biological progress in the maize breeding and seed industries is unique in terms of their social and ecological innovation aspects. It affects agricultural productivity and the adaptation of cultivated maize varieties to market demands and changing climate conditions without compromising the environment. Modern maize resistance breeding relies on a wide range of molecular genetic research techniques. These technologies enable the identification of genomic regions associated with maize smut resistance, which is crucial for characterizing and manipulating these regions. Therefore, the aim of this study was to identify molecular markers (SilicoDArT and SNP) linked to candidate genes responsible for maize smut resistance, utilizing next-generation sequencing, as well as association and physical mapping. By using next-generation sequencing (NGS) and statistical tools, the analyzed maize genotypes were divided into heterotic groups, which enabled the prediction of the hybrid formula in heterosis crosses. In addition, Illumina sequencing identified 60,436 SilicoDArT markers and 32,178 SNP markers (92,614 in total). For association mapping, 32,900 markers (26,234 SilicoDArT and 6666 SNP) meeting the criteria (MAF > 0.25 and the number of missing observations < 10%) were used. Among the selected markers, 61 were highly statistically significant (LOD > 2.3). Among the selected 61 highly statistically significant markers (LOD > 2.3), 10 were significantly associated with plant resistance to maize smut in two locations (Smolice and Kobierzyce). Of the 10 selected markers, 3 SilicoDArT (24016548, 2504588, 4578578) and 3 SNP (4779579, 2467511, 4584208) markers were located within genes. According to literature reports, of these six genes, three (ATAD3, EDM2, and CYP97A3) are characterized proteins that may play a role in the immune response that develops in response to corn smut infection. In the case of genotypes belonging to the same origin groups, markers linked to these genes can be used to select varieties resistant to corn smut. These markers will also be tested on genotypes belonging to other maize origin groups to demonstrate their universality.

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.

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.

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.

Identification and characterization of a carbohydrate recognition domain-like region in Entamoeba histolytica Gal/GalNAc lectin intermediate subunit.

Entamoeba histolytica adhesion mainly depends on galactose (Gal)-/N-acetyl-d-galactosamine (GalNAc)-inhibitable lectins, subsequently triggering a series of amebic reactions. Among the three subunits of Gal-/GalNAc-inhibitable lectin, heavy subunit and intermediate subunit (Igl) have exhibited lectin activity, but that of Igl remains poorly understood. In this study, we confirmed a carbohydrate-recognition domain (CRD)-like limiting region in E. histolytica Igl and further identified its two core CXXC motifs responsible for carbohydrate recognition. Moreover, the role of Igl's CRD-like region and its CXXC motifs in hemolysis and pathogenic effects was explored. This is the first study to determine an adhesion-related region in E. histolytica Igl protein, providing a new reference direction for subsequent research studies. Since the potential homogeneity of galectin-2 in several mammals and Igl CRD-like region, it could be meaningful to relate the corresponding pathogeneses and phenotypes of these two proteins. Except for adhesion, studies on the involvement of Igl CRD-like region in different parasite-host interactions are also promising.

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.