Our Work

Ushbu maqolada davlat kadastrlari tizimini zamonaviy texnologiyalar, geoportallar va turdosh ilmiy fanlar integratsiyasi asosida takomillashtirish masalalari yoritilgan. Fotogrammetriya, geodeziya, ka…

Ushbu maqolada davlat kadastrlari tizimini zamonaviy texnologiyalar, geoportallar va turdosh ilmiy fanlar integratsiyasi asosida takomillashtirish masalalari yoritilgan. Fotogrammetriya, geodeziya, kartografiya, geografik axborot tizimlari, kadastr, huquq va iqtisod fanlari o‘rtasidagi aloqalar graflar nazariyasi yordamida matematik modellashtirilgan. Tadqiqotda aerokosmik suratga olish, topografik-geodezik ma’lumotlarni yig‘ish, kartografik ishlarni bajarish, ekologik tadqiqot, obyektlarni baholash va huquqiy ta’minot bosqichlari Kad1–Kad6 ko‘rinishida ifodalangan. Taklif etilgan algoritm yuqori aniqlikdagi kadastr ma’lumotlar bazasi va raqamli kartalarni yaratish, ma’lumotlarni geoportalga uzatish hamda boshqaruv qarorlarini samarali qabul qilish imkonini beradi. Ushbu yondashuv axborot almashinuvini yaxshilaydi, takroriy ishlarni kamaytiradi va yer resurslaridan oqilona foydalanishni ta’minlaydi, mahalliy va respublika boshqaruvi darajalarida ham.

Online shopping has evolved significantly with the integration of augmented reality (AR) technology, offering users the ability to visualize products in their physical space before making a purchase. …

Online shopping has evolved significantly with the integration of augmented reality (AR) technology, offering users the ability to visualize products in their physical space before making a purchase. However, sentiment analysis within AR-enabled platforms faces challenges due to sparse review data, unlike traditional e-commerce platforms. The Consistent Fruitfly Optimization-Based Graph Neural Network (CFO-GNN) proposed in this paper addresses this challenge by combining fruitfly optimization with graph neural networks. This innovative approach allows CFO-GNN to efficiently handle sparse data while capturing the intricate relationships present in AR shopping experiences. By leveraging these techniques, CFO-GNN enables more accurate sentiment analysis, empowering businesses to make informed decisions and enhance user satisfaction in the dynamic landscape of AR-enabled online shopping. Through comprehensive evaluation on a diverse dataset, CFO-GNN demonstrates its effectiveness in improving sentiment analysis within AR environments, highlighting its potential to drive advancements in user experience and competitiveness for businesses operating in AR-enabled online retail.

El presente estudio expone una revisión sistemática de la literatura con el objetivo de analizar la relación existente entre el desarrollo de las habilidades blandas y la reducci&…

El presente estudio expone una revisión sistemática de la literatura con el objetivo de analizar la relación existente entre el desarrollo de las habilidades blandas y la reducción del estrés laboral en las organizaciones, con énfasis en las pequeñas y medianas empresas. Se utilizó la metodología PRISMA 2020 para el análisis de artículos publicados durante el período 2019 - 2024, obtenidos de bases de datos internacionales que se refieren a competencias como la comunicación, la motivación, el liderazgo y el trabajo colaborativo. Los resultados posicionan a las habilidades blandas como un recurso clave que lleva, principalmente a la reducción y prevención de riesgos psicosociales, a la mejora del ambiente laboral y a la promoción de la sostenibilidad organizacional. Así, la falta de dichas competencias en el ambiente laboral se vincula con el aumento del nivel de estrés, la falta de motivación y la pérdida de competitividad; por el contrario, si se apuesta por su desarrollo, se favorece la innovación, la cohesión y el bienestar emocional de los colaboradores. Se concluye en que es necesaria la incorporación de estas capacidades como parte de la estrategia organizacional y se recomienda la realización de investigaciones futuras en el contexto latinoamericano.

Initial release for Zenodo

Globalization, modernization, and the limited digital documentation of indigenous heritage have created an urgent need for information technology-based approaches that can preserve, represent, and cre…

Globalization, modernization, and the limited digital documentation of indigenous heritage have created an urgent need for information technology-based approaches that can preserve, represent, and create sustainable value from local culture. Existing metaverse-based heritage studies have mainly emphasized virtual exhibitions or museum extensions, while fewer studies have integrated architectural reconstruction, community validation, digital ownership, and tourism-oriented value creation in one framework for a living indigenous culture. This study addresses this gap by developing a participatory metaverse framework for the restoration of Nias cultural heritage. The research design combines human-centered design, ethnographic documentation, architectural visualization, and applied computing. Data were collected through field observations, interviews with local stakeholders, literature review, and documentation of Nias traditional houses and village layouts. The development process included basic 3D modeling, architectural detailing, material texturing, environmental reconstruction, final rendering, and conceptual integration with blockchain-based authenticity and asset ownership mechanisms. The results show that Nias traditional houses and village compositions can be digitally reconstructed as immersive and culturally meaningful environments. Compared with prior museum-centered metaverse studies, the proposed framework contributes an IT-based heritage restoration model that integrates 3D visualization, participatory validation, blockchain-enabled authenticity, and digital tourism potential. The practical implication is that local governments, cultural institutions, and creative industries can use the framework to support heritage education, virtual tourism, digital asset governance, and regional revenue diversification. Limitations include the absence of large-scale user testing, limited empirical measurement of economic impact, and the need for long-term infrastructure validation. Future work should evaluate user experience, community acceptance, and implementation readiness in real metaverse platforms.

A pesquisa mapeou e avaliou as políticas públicas voltadas à pessoa idosa no município de Viçosa (MG), utilizando como arcabouço analítico o ciclo de p…

A pesquisa mapeou e avaliou as políticas públicas voltadas à pessoa idosa no município de Viçosa (MG), utilizando como arcabouço analítico o ciclo de políticas públicas e as nove dimensões da Estratégia Brasil Amigo da Pessoa Idosa (EBAPI). O estudo concluiu que o município possui uma política local atenta e em consonância com os marcos legais (federais e estaduais), disponibilizando 68 ações, serviços, projetos e programas em todas as dimensões da vida social para a população longeva

The segmentation of kidneys is a challenging task in medical image analysis, particularly for early diagnosis and treatment of renal disorders. Having a clear, well-defined outline of kidney structure…

The segmentation of kidneys is a challenging task in medical image analysis, particularly for early diagnosis and treatment of renal disorders. Having a clear, well-defined outline of kidney structures from computed tomography (CT) and magnetic resonance imaging (MRI) helps doctors tremendously with diagnosis, surgical planning, and beyond, as well as with monitoring disease progression. But there are many problems, such as unevenly outlined boundaries, low image contrast, mottled patterns, and normal anatomical variations that occur from one patient to another, which all decrease the efficiency of the most common segmentation techniques. Therefore, to circumvent these constraints, this study proposes a framework, called Shape-Prior Learning, for kidney segmentation with deep networks enhanced with SOCAE. In principle, the concept is to integrate a convolutional auto-encoder based on shape, SOCAE, and a deep learning architecture to retain anatomical consistency and allow the system to learn structural cues and/or spatial characteristics of the kidneys. The pipeline comprises image pre-processing, feature extraction, shape-prior learning, and, at its end, the segmentation stage. The primary goal of the SOCAE part is to obtain the local texture information and additional global kidney shape representations. Therefore, the network can achieve higher segmentation accuracy and fewer boundary errors, such as misclassifications. Including shape-prior constraints in a deep network greatly reduces shape invariance in addressing the challenging kidney region while preserving edges. Furthermore, features are enhanced and normalized during training to ensure the model remains robust and can be applied to different medical image databases. Finally, experimental validation was performed on kidney benchmark image datasets using different performance metrics, including Accuracy, Precision, Recall, Dice Similarity Coefficient, and F1-Score. Comparing the SOCAE-enhanced deep network to the CNN and the U-Net-based CNN, respectively, in an almost consistent manner, it can be concluded that the proposed network achieved the highest accuracy. For accuracy, the model has a high accuracy rate of 0.9882, precision of 0.9814, recall of 0.9776, and F1-Score of 0.9795. The segmentations are consistent. The Precision improvement indicates fewer false-positive segmentation areas, and the overall higher F1 Score suggests a fairly good balance between Precision and Recall. Overall, these outcomes show the proposed Shape-Prior Learning approach meaningfully boosts kidney segmentation, and it offers a practical method for automated medical image processing, helping more intelligent clinical decision-making systems as well as computer-aided diagnosis tasks.

Description The ImpactAlign SDG Dashboard (Figure 1) visualizes the systematic integration of research outputs with SDG targets and indicators. It displays measurable engagement metrics, including 62 …

Description The ImpactAlign SDG Dashboard (Figure 1) visualizes the systematic integration of research outputs with SDG targets and indicators. It displays measurable engagement metrics, including 62 projects aligned, 85 scholars engaged, and 23 institutions participating. The dashboard high lights thematic clusters—poverty reduction, quality education, climate action, and partnerships through bar graphs, while a circular chart illustrates 72% overall SDG achievement. Each research document is mapped to relevant SDG targets (e.g., 4.7, 9.5, 13.2, 17.16) and indicators (e.g., 4.7.1, 9.5.1, 13.2.1, 17.16.1), enabling transparent tracking of institutional contributions. The bottom panel displays all 17 SDG icons, reinforcing the project’s holistic scope & ensuring visibility across the sustainability agenda.

This record contains the software artifact for the EMSOFT 2026 paper, “Suborbital and Orbital Real-Time Localization of Gamma-Ray Bursts via Utility-Guided Coordination.” The archive inclu…

This record contains the software artifact for the EMSOFT 2026 paper, “Suborbital and Orbital Real-Time Localization of Gamma-Ray Bursts via Utility-Guided Coordination.” The archive includes the experiment code, environment and build files. Detailed installation and experiment instructions are provided in README.md. The required transient datasets and detector models are available separately at https://doi.org/10.5281/zenodo.21497891. Development repository: https://github.com/Daisy0419/GRB-Search-Pipeline