Unique Imaging Research Articles

Peningkatan Hasil Belajar Pendidikan Agama Katolik Melalui Problem Based Learning pada Materi Aku Citra Allah yang Unik Kelas VII SMP Negeri 7 Belimbing

This research aims to improve the learning outcomes of grade VII students of SMP Negeri 7 Belimbing for the 2024/2025 school year in the subject of Catholic Religious Education through the application of the Problem Based Learning (PBL) learning model. This type of research is Classroom Action Research (PTK) which is carried out in two cycles with 2 meetings each cycle. Each cycle consists of several stages, namely planning, implementation of actions, observation and reflection. The subject matter discussed was "I am the Unique Image of Allah". The subjects in this study are grade VII students of SMP Negeri 7 Belimbing for the 2024/2025 school year consisting of 10 students. The data collection technique is in the form of test results carried out at the end of cycle I and cycle II, observation results during learning in cycle I and cycle II. The results of the study show that the application of the Problem Based Learning (PBL) learning model can improve the learning outcomes of grade VII students of SMP Negeri 7 Belimbing starting from cycle I and cycle II. In the first cycle, the percentage of student learning completeness was 80%, and in the second cycle, the percentage of completeness reached 100%. The conclusion of this study is that the application of the Problem Based Learning (PBL) learning model can improve the learning outcomes of Catholic Religious Education students in grade VII of SMP Negeri 7 Belimbing.

Peningkatan Hasil Belajar PAK dan BP Melalui Pembelajaran Berbasis PBL Kelas 7 Fase D di SMP N 2 Tebing Tinggi

The low learning outcomes of students at SMP N 2 Tebing Tinggi in the subject of Catholic Religious Education and Character Building were caused by the monotonous teaching method, it’s the lecture method, which is lack variation. To solve this issue, the researcher applied the Project-Based Learning (PBL) method to improve student learning outcomes. The research question was whether the implementation of PBL can improve learning outcomes in Catholic Religious Education and Character Building. This study used the Classroom Action Research (CAR) method, conducted in two cycles. Each cycle consist of four stages: planning, implementation, observation, and reflection. The topic used in this research is "I Am a Unique Image of God." The instruments used included learning outcome tests (pre-test and post-test) and observation sheets to monitor the students’ engagement during the learning process. The result shows a significant improvement in students' learning outcomes from the pre-test to the post-test after the implementation of PBL. In addition to enhancing students' understanding of the "Image of God" concept, PBL method also successfully to motivate students to actively participate in learning, particularly through projects that relevant to daily life.

Functional Near-Infrared Spectroscopy in neurodegenerative disease: a review.

In recent years, with the aggravation of aging, the incidence of neurodegenerative diseases is increasing year by year, and the prognosis of patients is poor. Functional Near-Infrared Spectroscopy (fNIRS) is a new and non-invasive neuroimaging technology, which has been gradually deepened in the application research of neurodegenerative diseases by virtue of its unique neurooxygen signal brain functional imaging characteristics in monitoring the disease condition, making treatment plans and evaluating the treatment effect. In this paper, the mechanism of action and technical characteristics of fNIRS are briefly introduced, and the application research of fNIRS in different neurodegenerative diseases is summarized in order to provide new ideas for future related research and clinical application.

Salient Region Guided Colour Image Restoration using Deep Learning with Adaptive Compressive Sensing

In this research, a unique colour image reconstruction method based on Compressive Sensing is presented, aimed at enhancing efficiency and quality. The proposed method incorporates deep learning techniques, adaptive measurement rate setting, and salient region detection. The process begins with the collection of an input RGB images, followed by pre-processing to convert them to the Y+ colour space., including optimized Faster R-CNN (OFRCNN), are employed to detect and segment salient regions in the Y-channel. An adaptive measurement rate setting using Deep Q-Network (DQN) dynamically adjusts measurement rates based on input data characteristics. To optimize the Y-channel compression, a Dual attention-based CS Network (CSNet) model is applied. Moreover, adaptive weighted reconstruction is introduced, utilizing salient region masks and a hybrid the Walrus Optimization Algorithm (WaOA) and mountain gazelle optimizer (MGO), called Hybrid Walrus with Mountain Gazelle Optimizer (HWMGO). Finally, the restored Y-channel is combined with the reconstructed U and V channels to reconstruct full-colour in the RGB colour space. Experimental evaluations on a diverse dataset demonstrate the effectiveness of the proposed algorithm in accurately reconstructing high-quality colour images from CS data. This research contributes to the advancement of colour image reconstruction techniques, offering a comprehensive and efficient framework for various applications.

Spotlight on Hemorrhagic Destruction of the Brain, Subependymal Calcification, and Congenital Cataracts (HDBSCC).

Hemorrhagic Destruction of the Brain, Subependymal Calcification, and Congenital Cataracts (HDBSCC) is a rare syndrome caused by biallelic mutations in the JAM3 gene with significant intrafamilial variability in clinical presentation and brain imaging phenotypes. The clinical presentation of HDBSCC includes severe recurrent hemorrhages involving the brain parenchyma and the ventricles beginning in utero and continuing in infancy together with dense central cataracts present at birth. This comprehensive review documents reported cases on this unique condition and describes its genetic, neuroradiologic and ophthalmic features. It should be included in the differential diagnosis of children with congenital cataracts and neurodevelopmental abnormalities. Unique clinical, imaging findings and genetic testing can help the diagnosis.

해안관광도시 브랜드 이미지 디자인 비교분석 - 중·한 해안관광도시 중심으로

With the increasing number of tourists, cities with different characteristics have explored their own urban characteristics and established a unique brand image. Coastal tourism cities have distinctive characteristics and concentrated industries, which tend to cause problems such as the repetition of elements and colors when creating brand images, and it is difficult to reflect the differences and uniqueness between cities. Based on previous relevant theoretical studies, this study makes a comparative analysis of the design elements of the brand image of the coastal tourism cities of China and South Korea in combination with the characteristics of the cities themselves, and proposes the development direction of the brand image of the coastal tourism cities of China and South Korea through the analysis results, so as to promote the further improvement of their brand image and bring more tourism resources.

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The article examines the specifics of product positioning for processing enterprises within the agro-industrial complex (AIC), particularly in the flour and cereal industry. It is demonstrated that the flour and cereal sector holds significant export potential and operates under favorable conditions for sustainable functioning and effective development. Key challenges faced by producers are identified, including the low potential for product differentiation and the monopolized market structure that limits competition. The importance of creating a unique product image in the minds of consumers to enhance competitiveness is substantiated. A multi-attribute model is proposed for analyzing points of parity and differentiation, enabling the formation of a unique selling proposition and supporting informed marketing decisions.

‘Al-Hay As-Sini’ explored: the linguistic and semiotic landscape of Dubai Mall ‘Chinatown’ as a translated space and transnational contact zone

ABSTRACT Dubai in the UAE is a poster child of the region. The ‘Chinatown’ within Dubai Mall is an artificially-made theme-park-style place and a commodified space designed for consumption, which stands in contrast to traditional and naturally-occurring Chinatowns in North America, Europe and Southeast Asia. A new addition to the luxurious Dubai Mall, the ‘Chinatown’ features shops and restaurants in a China-inspired environment. Representing a different ethnolinguistic ecology, the ‘Chinatown’ is a unique existence in a superdiverse and mobile country made up of local Arabs and foreign workers/expats from the Indian subcontinent, Southeast Asia and beyond. This study explores how various linguistic and multimodal elements participate in the making of the linguistic/semiotic landscape of Dubai Mall ‘Chinatown’. This study highlights that both linguistic (Chinese characters) and non-linguistic elements (e.g. red colour and neon lights) are strategically combined as meaning-making resources to give the area authenticity and a unique image and identity. Conceptually, this ‘Chinatown’ is theorised as a case of ‘translation’ and cultural (re)contextualisation, where cultural elements from a far-away land are localised/recontextualised to adopt new social meanings. This study contributes to scholarship in sociolinguistics and applied linguistics, especially in commodified man-made spaces in transnational urban zones in our globalised world.

A Novel Technique for Fluorescence Lifetime Tomography.

Fluorescence lifetime has emerged as a unique imaging modality for quantitatively assessing in vivo the molecular environment of diseased tissues. Although fluorescence lifetime microscopy (in 2D) is a mature field, 3D imaging in deep tissues remains elusive and challenging owing to scattering. Herein, we report on a deep neural network (coined AUTO-FLI) that performs both 3D intensity and quantitative lifetime reconstructions in deep tissues. The proposed Deep Learning (DL)-based approach involves an in silico scheme to generate fluorescence lifetime data accurately. The developed DL model is validated both in silico and on experimental phantoms. Overall, AUTO-FLI provides accurate 3D quantitative estimates of both intensity and lifetime distributions in highly scattering media, demonstrating its unique potential for fluorescence lifetime-based molecular imaging at the mesoscopic and macroscopic scale.

Advances on Molecular Mechanism and Clinical Treatment in Invasive Mucinous Adenocarcinoma

Invasive mucinous adenocarcinoma (IMA) is a special type of lung adenocarcinoma that accounts for 2% to 10% of all lung adenocarcinoma. Surgical treatment is preferred for IMA, and traditional chemotherapy drugs and targeted therapy drugs have poor efficacy in this disease. IMA has unique prognostic, imaging and molecular features. The incidence of IMA is very low, so thoracic surgeons may lack of knowledge to the disease and misdiagnose it as benign diseases such as pneumonia and tuberculosis. This article reviews and discusses the imaging, clinicopathological features, treatment methods and prognosis of IMA. .

Modelling wound area in studies of wound healing interventions

BackgroundExperimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing.MethodsWe derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models.ResultsOverall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were − 0.01 cm2 (-0.77, 0.74), -0.44 cm2 (-1.64, 0.76), and 0.11 cm2 (-0.87, 1.08) using a non-parametric method versus − 0.03 cm2 (-0.14, 0.06), 0.06 cm2 (-0.05, 0.17), and 0.03 cm2 (-0.07, 0.17) using a square-root model.ConclusionsModel-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago.

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Single‐Luminophore Molecular Engineering for Rapidly Phototunable Solid‐State Luminescence

Smart materials enabling emission intensity or wavelength tuning by light stimulus attract attention in numerous cutting‐edge fields. However, due to the generally dense molecular stacking against photoresponsivity in solid states, especially in crystals, developing rapidly phototunable solid‐state luminescent systems remains challenging. Herein, we propose a new luminophore that serves as both a photoresponsive unit and a luminescent group, while possessing enhanced conformational freedom to provide a solution. Namely, photoexcitation‐induced molecular conformational change of an ionized persulfurated arene based on weak intermolecular aliphatic C–H···π interaction was employed. On these basis, rapidly enhanced phosphorescence upon irradiation can be observed in a series of phase states, like solution state, crystal, and amorphous state, especially with a high photoresponsive rate of 0.033 s‐1 in crystal state that is superior to the relevant reported cases. Moreover, a rapidly phototunable afterglow effect is achieved by extending this molecule into some polymer‐based doping systems, endowing the system with unique dynamic imaging and fast photopatterning capabilities. Such a single‐luminophore molecular engineering and the underlying mechanism have implications for building different condensed functional materials, principally for those with stimuli responses in solid states.

Unlocking the relationship between destination brand value and tourists' intentions to visit: A case study in Quang Binh province

This study aims to explore the factors that constitute the concept of destination brand value and analyze the influence of these elements on tourists' intention to choose a destination. The destination is seen as a brand that builds and maintains a unique venue image, enhances its competitive position and attracts visitors to this destination. Vietnam has much potential to attract tourists with its scenic landscapes and historical cultural relics. However, since COVID-19, attracting tourists has yet to be able to compete with other countries in the region such as Thailand, Singapore and the Philippines. How can Vietnamese destination brand value be improved to compete in attracting tourists? To answer the above research question, a quantitative approach was applied. Data was collected from 295 tourists intending to visit Quang Binh, a central province of Vietnam with many famous natural, cultural and historical attractions. Through exploratory factor analysis, research results show that three factors of brand value impact the online travel experience: destination image brand quality, destination brand awareness and destination brand image. This study confirms the positive relationship between online destination brand experience and destination intention to visit. Some implications are proposed for managers in the tourism industry to improve the competitive advantage of the Quang Binh destination brand.

Full-reference calibration-free image quality assessment

Objective Image Quality Assessment (IQA) methods often lack of linearity of their quality estimates with respect to scores expressed by human subjects and therefore IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training presents a challenge in terms of generalization hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. We show that on natural images, application of estimation theory and psychophysical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are linearly correlated to both the subjective scores and the viewing distance. Then, we show that any mainstream IQA methods can be reconducted to the canonical method by converting its metric based on a unique specimen image. The proposed scheme is extended to wide classes of degraded images, e.g. noisy and compressed images. The resulting calibration-free FR IQA methods allows for comparability and interoperability across different imaging systems and on different viewing distances. A comparison of their statistical performance with respect to state-of-the-art calibration prone methods is finally provided, showing that the presented model is a valid alternative to the final 5-parameter calibration step of IQA methods, and the two parameters of the model have a clear operational meaning and are simply determined in practical applications. The enhanced performance are achieved across multiple viewing distance databases by independently realigning the blur values associated with each distance.

Clinical, molecular, and genetic features of spinal meningiomas.

Spinal meningiomas comprise 25%-46% of all primary spinal tumors. While the majority are benign and slow-growing, when left untreated, they can result in significant neurological decline. Emerging clinical, imaging, and molecular data have begun to reveal spinal meningiomas as distinct tumor subtypes compared to their intracranial counterparts. Moreover, recent studies indicate molecular and genetic subtype heterogeneity of spinal meningiomas both within and across the classically defined WHO grades. In the current review, we focus on recent advances highlighting the epidemiological, pathological, molecular/genetic, and clinical characteristics of spinal meningiomas. Furthermore, we explore patient and tumor-specific factors that predict prognosis and postoperative outcomes. We highlight areas that require further investigation, specifically efforts aimed at linking unique molecular, genetic, and imaging characteristics to distinct clinical presentations to better predict and manage patient outcomes.

Plea to radiologists: Please consider Mahvash disease when encountering an enlarged pancreas.

Radiologists play a key role in establishing an early and accurate diagnosis, especially for rare diseases. Mahvash disease (OMIM 619290) is an autosomal recessive hereditary disease caused by inactivating mutations of the glucagon receptor and its main clinical consequences are pancreatic neuroendocrine tumors and in some cases, porto-sinusoidal vascular disease and portal hypertension. Untreated Mahvash disease can be lethal. The diagnosis of Mahvash disease has almost always been delayed in the past due to radiologists' unawareness of or unfamiliarity with the unique imaging features of Mahvash disease which are moderately to enormously enlarge pancreas with preserved pancreas contour and parenchyma without vascular involvement or lymphadenopathy. These features help differentiate Mahvash disease from other etiologies of diffusely enlarged pancreas such as diffuse pancreatic ductal carcinoma, diffuse pancreatic lymphoma, and autoimmune pancreatitis. Invoking Mahvash disease in the differential diagnosis of an enlarged pancreas has recently been shown to facilitate early diagnosis. To prevent missing the diagnosis of this significant disease, I sincerely ask radiologists to consider Mahvash disease in their differential diagnoses of diffusely enlarged pancreas.

The Transformation of Luxury Brands Marketing Strategies in Social Media

The luxury industry is currently undergoing a significant transformation driven by digitalization. Consumer shopping behaviors are increasingly shifting towards online channels, while social media emerges as a key platform for interaction between luxury brands and consumers. The rise of emerging markets and the growing young consumer demographic are driving the trend of digital consumption. Luxury brands are actively expanding into the digital sphere, employing celebrity endorsements and social media marketing strategies. This paper utilizes a literature review and case analysis methodology. It finds that combining celebrity endorsements with social media marketing effectively enhances brand awareness and sales, creating a unique brand image and attracting more consumers. This trend has become a crucial transformation path for the luxury industry, albeit brands need to carefully consider celebrity selection and risk management. Future research could further explore best practices in celebrity endorsements and social media marketing, as well as their long-term impact on consumer behavior and brand benefits.

Eliminating the systematic error of spatial light interference microscopy by the wavefront comparison iterative algorithm

Spatial light interference microscopy (SLIM) is a unique quantitative phase imaging technology. Since the phase of direct light is simply assumed to be 0, it has a non-negligible systematic error. Based on the theory of partially coherent SLIM, here we propose a wavefront comparison iterative (WCI) algorithm to eliminate this error for accurate phase imaging. In this paper, we clarified the existence of this systematic error by phase imaging of oil-immersed polystyrene microspheres, and we demonstrated the feasibility of the WCI algorithm by phase imaging of red blood cells. Furthermore, through computer simulation, we have confirmed that for phase imaging of large adherent cells, if the SLIM system compresses the phase of direct light sufficiently small, the WCI algorithm will be feasible too.