Distinctive Features Research Articles

Long-Term Glycemic Control Improvement After the Home and Self-Care Program for Patients With Type 1 Diabetes: Real-World-Based Cohort Study.

The prevalence of type 1 diabetes (T1D) is increasing worldwide, with a much higher proportion of adult patients. However, achieving stable glycemic control is difficult in these patients. After periodic implementation of structured education for patients with T1D through the Home and Self-Care Program, a pilot home health care project promoted by the Korean government, we evaluated the program's effects on glycemic control. This study was conducted from April 2020 to March 2023. We analyzed 119 participants with T1D aged >15 years. Nursing and nutrition education were provided separately up to 4 times per year, with physician consultation up to 6 times per year. A distinguishing feature of this study compared with previous ones was the provision of remote support using a general-purpose smartphone communication app offered up to 12 times annually on an as-needed basis to enhance the continuity of in-person education effects. Patients were followed up on at average intervals of 3 months for up to 24 months. The primary end point was the mean difference in glycated hemoglobin (HbA1c) at each follow-up visit from baseline. For continuous glucose monitoring (CGM) users, CGM metrics were also evaluated. The mean HbA1c level of study participants was 8.6% at baseline (mean duration of T1D 10.02, SD 16.10 y). The HbA1c level reduction in participants who received at least 1 structured educational session went from 1.63% (SD 2.03%; P<.001; adjustment model=1.69%, 95% CI 1.24%-2.13% at the first follow-up visit) to 1.23% (SD 1.31%; P=.01; adjustment model=1.28%, 95% CI 0.78%-1.79% at the eighth follow-up visit). In the adjustment model, the actual mean HbA1c values were maintained between a minimum of 7.33% (95% CI 7.20%-7.46% at the first follow-up visit) and a maximum of 7.62% (95% CI 7.41%-7.82% at the sixth follow-up visit). Among CGM users, after at least 1 session, the mean time in the target range was maintained between 61.59% (adjusted model, 95% CI 58.14%-65.03% at the second follow-up visit) and 54.7% (95% CI 50.92%-58.48% at the eighth follow-up visit), consistently staying above 54.7% (corresponding to an HbA1c level of <7.6%). The mean time below the target range (TBR) also gradually improved to the recommended range (≤4% for TBR of <70 mg/dL and ≤1% for TBR of <54 mg/dL). The Home and Self-Care Program protocol for glycemic control in patients with T1D is effective, producing significant improvement immediately and long-term maintenance effects, including on CGM indexes.

Chemometric Approach Application in Modern Wine Studies

The wine market is affected by the origin of wines, but the current wine traceability system has some limitations. The idea of geographical authenticity and quality has increased as one of the most important parameters influencing consumers’ preferences. Chemical parameters such as total polyphenolic content (TPC), alcohol and organic acid content (total acids, malic and tartaric acids), and antioxidant activity were observed in this work at 15 dry white wines of varieties Pinot blanc, Riesling, Sauvignon blanc, and Chardonnay, which originated from three different countries. FTIR and spectrophotometry methods (TPC and DPPH) were used and chemometric approaches such as ANOVA and PCA were selected as the most important for evaluation. In general, high levels of malic acid, TPC, and antioxidant activity were detected in Austrian wines. Pinot blanc, Chardonnay, and Sauvignon from the same producing region in Austria showed higher results. The higher overall acid concentration was a distinguishing feature of the Slovakian Sauvignon and Riesling varieties, particularly in the case of tartaric acid. Austrian samples showed significantly higher antioxidant activity compared to Hungarian Chardonnay (p = 0.002) and Sauvignon (p = 0.007), comparable to TPC. The application of statistical analysis was useful in demonstrating many discrepancies, particularly by application of the PCA method.

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model’s performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

Two cases of Dent disease type 1 with Bartter-like phenotype and literature review

The clinical presentation, treatment, and follow-up of two boys with type 1 Dent disease who exhibited a Bartter-like phenotype were retropectively analysed. The related literature of pediatric patients with type 1 Dent disease who had hypokalemia and metabolic alkalosis was screened through databases such as PubMed, CNKI, and Wanfang until February 1, 2024, and common features among these patients were summarized through literature review. A total of 7 literatures were included, and 9 children were included in the analysis. All patients were male, presenting with significant low molecular weight proteinuria and hypercalciuria. Other prominent characteristic phenotypes included short stature (7/8), hypophosphatemia (8/9), and rickets (6/8). Seven previously reported patients had missense or nonsense mutations, while 2 patients in this study carried possible pathogenic mutations in the CLCN5 gene, c.315+2T>A (p.?) and c.584dupT (p.I196Yfs*6), respectively. Five patients were able to maintain blood potassium levels around 3 mmol/L with oral potassium chloride solution combined with non-steroidal anti-inflammatory drugs (ibuprofen or indomethacin). The follow-up showed that 2 patients developed chronic kidney disease stage 4 and stage 3 at the age of 13 and 21 years, respectively. The phenotypic overlap between Dent disease and Batter syndrome is considerable,with the distinguishing feature being the presence of significant low molecular weight proteinuria. Patients with type 1 Dent disease presenting with the Bartter-like phenotype have a high prevalence of short stature, hypophosphatemia, and rickets. Non-steroidal anti-inflammatory drugs can be used to correct hypokalemia in patients under periodic renal function assessment.

Towards artificial intelligence-based disease prediction algorithms that comprehensively leverage and continuously learn from real-world clinical tabular data systems.

This manuscript presents a proof-of-concept for a generalizable strategy, the full algorithm, designed to estimate disease risk using real-world clinical tabular data systems, such as electronic health records (EHR) or claims databases. By integrating classic statistical methods and modern artificial intelligence techniques, this strategy automates the production of a disease prediction model that comprehensively reflects the dynamics contained within the underlying data system. Specifically, the full algorithm parses through every facet of the data (e.g., encounters, diagnoses, procedures, medications, labs, chief complaints, flowsheets, vital signs, demographics, etc.), selects which factors to retain as predictor variables by evaluating the data empirically against statistical criteria, structures and formats the retained data into time-series, trains a neural network-based prediction model, then subsequently applies this model to current patients to generate risk estimates. A distinguishing feature of the proposed strategy is that it produces a self-adaptive prediction system, capable of evolving the prediction mechanism in response to changes within the data: as newly collected data expand/modify the dataset organically, the prediction mechanism automatically evolves to reflect these changes. Moreover, the full algorithm operates without the need for a-priori data curation and aims to harness all informative risk and protective factors within the real-world data. This stands in contrast to traditional approaches, which often rely on highly curated datasets and domain expertise to build static prediction models based solely on well-known risk factors. As a proof-of-concept, we codified the full algorithm and tasked it with estimating 12-month risk of initial stroke or myocardial infarction using our hospital's real-world EHR. A 66-month pseudo-prospective validation was conducted using records from 558,105 patients spanning April 2015 to September 2023, totalling 3,424,060 patient-months. Area under the receiver operating characteristic curve (AUROC) values ranged from .830 to .909, with an improving trend over time. Odds ratios describing model precision for patients 1-100 and 101-200 (when ranked by estimated risk) ranged from 15.3 to 48.1 and 7.2 to 45.0, respectively, with both groups showing improving trends over time. Findings suggest the feasibility of developing high-performing disease risk calculators in the proposed manner.

Half‐Curcumin‐Based Chemiluminescence Probes and Their Applications in Detecting Quasi‐Stable Oxidized Proteins

AbstractNumerous methods have been reported for detecting ROS/RNS in vitro and in vivo; however, detecting methods for the secondary products of the reactive oxygen species (ROS)/reactive nitrogen species (RNS) reactions, particularly quasi‐stable oxidized products, have been much less explored. In this report, we observed that half‐curcumins could generate chemiluminescence (CL). In contrast to other chemiluminescence scaffolds, the distinguishing feature of a half‐curcumin is the formation of a carbanion intermediate of its acetylacetone moiety, opening unique avenues for applications. In this study, we designed a series of half‐curcumins CRANAD‐Xs and found that CRANAD‐164 could be used to detect quasi‐stable oxidized proteins (QSOP) in vivo and in patient serum samples. We illustrated that CRANAD‐164 could be used to monitor the responses of taurine, an amino acid with newly reported anti‐aging capacity, in an inflammatory mouse model. Remarkably, we further demonstrated that the QSOP levels were much higher in the disease serum samples, including Alzheimer's disease (AD), compared to the samples from healthy controls. Moreover, our results revealed that the sera chemiluminescence intensities were higher in aged healthy controls compared to young healthy subjects, suggesting that CRANAD‐164 can be used to monitor the increase of QSOP during aging.

A Deformable and Multi-Scale Network with Self-Attentive Feature Fusion for SAR Ship Classification

The identification of ships in Synthetic Aperture Radar (SAR) imagery is critical for effective maritime surveillance. The advent of deep learning has significantly improved the accuracy of SAR ship classification and recognition. However, distinguishing features between different ship categories in SAR images remains a challenge, particularly as the number of categories increases. The key to achieving high recognition accuracy lies in effectively extracting and utilizing discriminative features. To address this, we propose DCN-MSFF-TR, a novel recognition model inspired by the Transformer encoder–decoder architecture. Our approach integrates a deformable convolutional module (DCN) within the backbone network to enhance feature extraction. Additionally, we introduce multi-scale self-attention processing from the Transformer into the feature hierarchy and fuse these representations at appropriate levels using a feature pyramid strategy. This enables each layer to leverage both its own information and synthesized features from other layers, enhancing feature representation. Extensive evaluations on the OpenSARShip-3-Complex and OpenSARShip-6-Complex datasets demonstrate the effectiveness of our method. DCN-MSFF-TR achieves average recognition accuracies of 78.1% and 66.7% on the three-class and six-class datasets, respectively, outperforming existing recognition models and showcasing its superior capability in accurately identifying ship categories in SAR images.

Usability of a smartphone-compatible, confocal micro-endoscope for cervical cancer screening in resource-limited settings

BackgroundMore efficient methods to detect and treat precancerous lesions of the cervix at a single visit, such as low-cost confocal microscopy, could improve early diagnosis and hence outcomes. We piloted a prototype smartphone-compatible confocal micro-endoscope (SCME) among women presenting to a public cervical cancer screening clinic in Kampala, Uganda. We describe the piloting of the SCME device at an urban clinic used by lower cadre staff.MethodsWe screened women aged 18 and 60 years, who presented for cervical cancer screening at the Kawempe National Referral Hospital Kampala, and evaluated the experience of their providers (nurses). Nurses received a 2-day training by the study doctors on how to use the SCME, which was added to the standard Visual Inspection with Acetic acid (VIA)-based cervical cancer screening. The SCME was used to take colposcopy images before and after VIA at positions 12 and 6 O’clock if VIA negative, and on precancer-suspicious lesions if VIA positive. We used questionnaires to assess the women’s experiences after screening, and the experience of the nurses who operated the SCME.ResultsBetween November 2021 and July 2022, we screened 291 women with a median age of 36 years and 65.7% were HIV positive. Of the women screened, 146 were eligible for VIA, 123 were screened with the SCME, and we obtained confocal images from 103 women. Of those screened with the SCME, 60% found it comfortable and 81% were willing to screen again with it. Confocal images from 79% of the women showed distinguishable cellular features, while images from the remaining 21% were challenging to analyze. Nurses reported a mean score of 85% regarding the SCME’s usefulness to their work, 71% regarding their satisfaction and willingness to use it again, 63% in terms of ease of use, and 57% concerning the ease of learning how to operate the SCME.ConclusionOur findings demonstrate the feasibility of using the SCME by lower cadre staff in low-resource settings to aid diagnosis of precancerous lesions. However, more work is needed to make it easier for providers to learn how to operate the SCME and capture high-quality confocal images.

Half-Curcumin-Based Chemiluminescence Probes and Their Applications in Detecting Quasi-Stable Oxidized Proteins.

Numerous methods have been reported for detecting ROS/RNS in vitro and in vivo; however, detecting methods for the secondary products of the reactive oxygen species (ROS)/reactive nitrogen species (RNS) reactions, particularly quasi-stable oxidized products, have been much less explored. In this report, we observed that half-curcumins could generate chemiluminescence (CL). In contrast to other chemiluminescence scaffolds, the distinguishing feature of a half-curcumin is the formation of a carbanion intermediate of its acetylacetone moiety, opening unique avenues for applications. In this study, we designed a series of half-curcumins CRANAD-Xs and found that CRANAD-164 could be used to detect quasi-stable oxidized proteins (QSOP) in vivo and in patient serum samples. We illustrated that CRANAD-164 could be used to monitor the responses of taurine, an amino acid with newly reported anti-aging capacity, in an inflammatory mouse model. Remarkably, we further demonstrated that the QSOP levels were much higher in the disease serum samples, including Alzheimer's disease (AD), compared to the samples from healthy controls. Moreover, our results revealed that the sera chemiluminescence intensities were higher in aged healthy controls compared to young healthy subjects, suggesting that CRANAD-164 can be used to monitor the increase of QSOP during aging.

Panniculitis: A Cardinal Sign of Autoinflammation.

Panniculitis was first described in the nineteenth century and is characterized by inflammation of the subcutaneous fat. It may be categorized in septal or lobular subtypes, but other histopathological features (e.g., presence of vasculitis, nature of inflammatory infiltrates, characteristics of fat necrosis) are also important for diagnostic purposes. Clinically, panniculitis is characterized by the presence of subcutaneous nodules, and both ulcerative and nonulcerative clinical subtypes have been proposed. In this review, we aimed to describe the occurrence of panniculitis in autoinflammatory disorders (AIDs) and related diseases. Among monogenic AIDs, panniculitis is common in IFN-mediated disorders. Panniculitis is a distinctive feature in proteasome-associated autoinflammatory syndromes (PRAAS), including chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature (CANDLE) syndrome and Nakajo-Nishimura syndrome. On the other hand, erythema nodosum corresponds to the most common clinical form of panniculitis and is common in polygenic AIDs, such as Behçet's syndrome, inflammatory bowel disease, and sarcoidosis. Cytophagic histiocytic panniculitis, lipoatrophic panniculitis of children, and otulipenia are rare disorders that may also present with inflammation of the subcutaneous fat. Therefore, panniculitis can identify a specific subgroup of patients with AIDs and may potentially be regarded as a cardinal sign of autoinflammation.

Acute Macular Neuroretinopathy Associated with COVID-19 Pandemic: A Real-world Observation Study

PurposeTo evaluate the clinical and retinal imaging features of Chinese patients with acute macular neuroretinopathy (AMN) associated with COVID-19. DesignA prospective observational study. MethodsRetinal imaging, including color fundus photography, near-infrared imaging (NIR), swept-source optical coherence tomography (SS-OCT), optical coherence tomography angiography (OCTA), and Humphrey perimetry, were conducted for each case. ResultsAll cases were included within the first three months following the pandemic outbreak. A total of 12 male patients (36.36 %) and 21 female patients (63.64 %) were prospectively recruited, and 29 cases (87.88 %) were bilaterally affected. The median interval between the onset of fever and the appearance of ocular symptoms was two days (range, 0.5–5.0 days). Apart from the outer retinal changes typical of AMN, changes in the inner retinal layers were observed, including intraretinal hemorrhage (8.06 %), cotton wool spots (9.68 %), and paracentral acute middle maculopathy (PAMM) (8.06 %). Smaller retinal inner nuclear layer hyperreflective speckles (RIHS) (41.94 %) were identified as a distinguishing feature from typical PAMM. Voids of vessel signals were found in the superficial (11.54 %), intermediate (82.69 %), and deep capillary plexus (98.08 %), and in the choriocapillaris (19.23 %) on OCTA. Humphrey perimetry illustrated central, paracentral, and peripheral scotomas. The occult lesions associated with AMN, PAMM, and some of the RIHS illustrated by OCT were visualized topographically and further confirmed by OCTA as perfusion defects. ConclusionAn increase in AMN cases correlated with the SARS-CoV-2 outbreak. Additional features, including widespread inner retinal perfusion deficits, were observed and may serve as potential biomarkers for systemic microcirculation dysregulation in COVID-19.

HLA class I expression on human platelets is highly variable and correlates with distinct allele group frequencies.

Human leukocyte antigen (HLA) class I molecules are expressed on platelets and can represent a source of alloimmunization in recipients of platelet transfusions. HLA mismatch between donors and recipients may be associated with the induction of anti-HLA antibodies, which can culminate in refractoriness to platelet transfusions. In the present study we analyzed HLA allele group frequencies and HLA expression levels on human platelets from blood donors. Platelet-rich plasma was collected from 139 donors to monitor platelet HLA class I expression by flow cytometry. DNA from donors with high and low platelet HLA expression was used in the genotype studies. Frequencies of large and normal-sized platelet subpopulations were determined and HLA class I expression was studied. Mean platelet volume (MPV) and platelet large-cell ratio (P-LCR) were analyzed in both groups of donors. The analysis showed variable platelet HLA class I expression with significant differences among donors. HLA class I allele group frequencies in donors with high and low platelet HLA expression showed distinctive genotypic features strictly related to expression level. The main allele groups found in samples with high platelet HLA class I expression were HLA-A*02, -A*68, -B*15, -B*49, and -C*03. Platelet HLA class I expression did not change over time or during freezing-thawing cycles. The analysis of platelet subpopulations showed a statistically significant higher expression of HLA class I molecules on large platelets than on normal-sized platelets. Moreover, donors with high HLA class I expression showed a higher frequency of large platelets (p<0.0001). The analysis of P-LCR in both groups of donors showed a statistically significant difference (p<0.05) within high HLA-expressing donors. Our data suggest an allele-dependent expression of HLA class I molecules on human platelets with distinct HLA allele group frequencies and different platelet subpopulation frequencies among blood donors.

Fully Distributed Event-Triggered Anti-Windup Consensus of Heterogeneous Systems With Input Saturation and an Active Leader.

This article addresses the event-based fully distributed consensus problem for linear heterogeneous multiagent systems (MASs) subject to input saturation. A leader with unknown but bounded control input is also considered. Based on an adaptive dynamic event-triggered protocol, all the agents can reach output consensus without knowing any global knowledge. Moreover, by applying a multiple-level saturation technique, the input-constrained leader-following consensus control is achieved. The given event-triggered algorithm can be utilized for the directed graph containing a spanning tree with the leader as the root. One distinct feature compared with previous works is that the proposed protocol can achieve saturated control without any a priori condition, instead, the local information is needed. Finally, the numerical simulations are illustrated to verify the performance of the proposed protocol.

Characterization of the gut butyrate-producing bacteria and lipid metabolism in African green monkey as a natural host of simian immunodeficiency virus infection.

Natural hosts of simian immunodeficiency virus (SIV), such as the African green monkey (AGM), possess the ability to avoid acquired immune deficiency syndrome (AIDS) despite lifelong infection. The underlying mechanisms are not completely understood. This study aimed to characterize the gut microbiome and metabolite profiles of different nonhuman primates (NHPs) to provide potential insight into AIDS resistance. Fresh feces from Cynomolgus macaques (CMs), and Rhesus macaques (RMs), SIV- AGMs (AGM_N), and SIV+ AGMs (AGM_P) were collected and used for metagenomic sequencing and metabonomic analysis. Compared with CMs and RMs, significant decreases in the abundances of Streptococcus , Alistipes , Treponema , Bacteroides , and Methanobrevibacter ( P < 0.01), and significant increases in the abundances of Clostridium , Eubacterium , Blautia , Roseburia , Faecalibacterium , and Dialister ( P < 0.01) were detected in AGM_N. Compared with AGM_N, a trend toward increased abundances of Streptococcus and Roseburia were found in AGM_P. The levels of metabolites involved in lipid metabolism and butanoate metabolism significantly differed among AGM_P, AGM_N and CM ( P < 0.05). Our data, for the first time, demonstrated distinguishing features in the abundances of butyrate-producing bacteria and lipid metabolism capacities between different NHP hosts of SIV infection. These findings may correlate with the different characteristics observed among these hosts in the maintenance of intestinal epithelial barrier integrity, regulation of inflammation, and provide insights into AIDS resistance in AGMs.

Floral ontogeny reveals potential synapomorphies for Senegalia sect. Monacanthea p.p. (Leguminosae).

Senegalia was recently described as non-monophyletic; however, its sections exhibit robust monophyletic support, suggesting a potential reclassification into separate genera-Senegalia sect. Monocanthea p.p. is the largest section. It contains 164 species of pantropical distribution and includes all of the current 99 neotropical species of Senegalia; however, no morphological characteristics are available to differentiate this section. To characterize this section, we examined floral developmental traits in four species of Senegalia sect. Monocanthea p.p. These traits were previously considered as potentially distinguishing features within Acacia s.l. and include the onset patterns of the androecium, the timing of calyx union, the origin of the staminal disc, and the presence of stomata on the petals. Furthermore, we analyzed previously unexplored traits, such as corolla union types, inflorescence development, and micromorphological features related to the indumentum, as well as the presence and location of stomata. The characteristics proposed as potential synapomorphies of the group include the postgenital fusion of the corolla and the presence of a staminal disc formed at the base of the filaments. The other analyzed floral characteristics were not informative for the characterization of the group. Future studies of floral ontogeny will help to establish more precise patterns, mainly whether corolla union and staminal tube formation occur similarly in African and Asian sections of Senegalia.

Geographical range extension of two penaeid shrimp Penaeus pulchricaudatus Stebbing, 1914 and Kishinouyepenaeopsis incisa (Wang &amp; Liu in Liu &amp; Wang, 1987) in Bangladesh waters

Shrimps are among the most intriguing groups within the Decapoda order. In Bangladesh, penaeid shrimps hold significant economic value as a major export commodity. This study aimed to analyze the morphometric and molecular characteristics of penaeid shrimps in Bangladesh. Two species of penaeid shrimp, Penaeus pulchricaudatus and Kishinouyepenaeopsis incisa were identified as new records in Bangladesh waters. A distinguishing feature of P. pulchricaudatus is its dark brown transverse bands that do not extend to the lower half of the carapace. The identification of these species was confirmed using both morphometric characteristics and DNA barcoding techniques. Two sequences were generated for these species utilizing the 16S rRNA gene marker. The findings reveal two new records of shrimp species in Bangladesh, expanding the known diversity of shrimp in the region and highlighting the presence of previously undocumented species. Dhaka Univ. J. Biol. Sci. 33(2): 111-117, 2024 (July)

Assessing Delirium Severity in Dementia: Item Development and Psychometric Validation for a Delirium Severity Instrument.

Despite the importance of measuring delirium severity in patients with Alzheimer's disease and related dementias (ADRD), no validated instrument currently exists. Our goal was to generate items for use in such an instrument. An interdisciplinary expert panel developed items to assess seven domains and 21 subdomains of delirium severity. Nursing experts provided input on items. Experts achieved consensus on 21 items, including best respondents for each item, and appropriate look-back periods. Nursing experts emphasized the need for educating nursing staff and obtaining information from caregivers. Careful, nuanced identification of distinguishing features is key for generating items for measuring delirium severity in persons with ADRD. Once developed, engaging nurses will be essential to facilitate adoption and relevance of the tool. [Journal of Gerontological Nursing, 50(9), 24-30.].

문화마케팅 관점에서 본 대학박물관의 활성화 방안

This study focuses on the issue of the declining status of university museums and conducts an in-depth analysis of its causes, offering alternatives from a cultural marketing perspective. Specifically, it aims to understand the perceptions and attitudes of university students and identify distinguishing features that could enhance competitiveness when compared to other museums. The ultimate goal is to elevate the status of university museums in the future. To achieve this, the study selected the Museum of Konkuk University as a case study and conducted in-depth interviews with 14 university students who had visited the museum within the past year. The results of the study are as follows: First, the perceptions and attitudes toward the university museum before the visit were generally negative or indifferent, which appeared to be due to a lack of high-quality promotion of the university museum. Second, most respondents rated the quality and engagement level of the exhibits and experiential content as average or below. Third, respondents felt that the university museum was not contributing to the university's cultural and artistic activities or regional revitalization, and they did not foresee positive changes in the future under the current conditions. Therefore, based on the responses related to the university founders, it is necessary to utilize independence movement content and introduce new technologies in the internal presentation. Additionally, online and offline promotions and product placement (PPL) through channels frequently accessed by university students should be proposed.

Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks

As facial modification technology advances rapidly, it poses a challenge to methods used to detect fake faces. The advent of deep learning and AI-based technologies has led to the creation of counterfeit photographs that are more difficult to discern apart from real ones. Existing Deep fake detection systems excel at spotting fake content with low visual quality and are easily recognized by visual artifacts. The study employed a unique active forensic strategy Compact Ensemble-based discriminators architecture using Deep Conditional Generative Adversarial Networks (CED-DCGAN), for identifying real-time deep fakes in video conferencing. DCGAN focuses on video-deep fake detection on features since technologies for creating convincing fakes are improving rapidly. As a first step towards recognizing DCGAN-generated images, split real-time video images into frames containing essential elements and then use that bandwidth to train an ensemble-based discriminator as a classifier. Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. Python tool is used for implementing this proposed study and the accuracy obtained for proposed work is 98.23 %.

Epistemic Logics for Relevant Reasoners

We present a neighbourhood-style semantic framework for modal epistemic logic modelling agents who process information using relevant logic. The distinguishing feature of the framework in comparison to relevant modal logic is that the environment the agent is situated in is assumed to be a classical possible world. This framework generates two-layered logics combining classical logic on the propositional level with relevant logic in the scope of modal operators. Our main technical result is a general soundness and completeness theorem.