SLOW TOURISM: A MEANS-END APPROACH TO THE MOTIVATIONS OF SLOW TRAVELERS

The primary objective of this dissertation was to examine the phenomenon of slow tourism by exploring tourists’ motivations and end-state values of slow tourism. Two research questions were developed: What are the important attributes, consequences/ benefits, and end-state values of slow tourism that travelers perceive? What are the structural relationships among attributes, consequences, and values of slow tourism? To address the questions, this dissertation applied a mixed method design by which both qualitative and quantitative investigations were performed. First, building upon means-end chain theory (Reynolds & Gutman, 1988), in-depth interviews with slow travelers were conducted and were analyzed by laddering and hierarchical value map (HVM). The findings of the qualitative study (Study 1) identified nine important attributes of slow tourism (i.e., hiking, self-paced activities, slow mobility, solo travel, culture/history/art, volunteering, local cuisine/restaurants/cafés, local shops, and concern for the environment) representing local and personal attributes); ten consequences associated with attributes in slow travel experiences (i.e., intimate contact with nature, flexibility in planning and time constraints, exploring local destinations, connections with people, supporting communities, environmental cleanup, mental unwinding and relaxation, fun/enjoyment/excitement, local immersion, and enrichment.) reflecting operative and psychological consequences; and seven personal values driving from the consequences of instrumental and terminal values (slow lifestyle, defying stereotypes, genuine and authentic experiences, happiness, self-awareness, self-confidence, and sense- of achievement) in slow tourism context. Next, based on the findings of Study 1, the survey study (Study 2) tested the proposed conceptual model and hypothesized relationships using Structural Equation Modeling (SEM) analysis. The findings of Study 2 offered overall support for the dynamics of attributes – consequences – values – loyalty intentions links while two paths (local attributes to psychological consequences and operative consequences to terminal values) associations were turned out to be insignificant. Slow tourists may not experience psychological effects from experiencing certain local attributes in that local features may attract travelers to a destination and involved them in travel activities, rather than directly influencing their emotional outcomes. In addition, a variety of slow travel activities may not motivate tourists to achieve end-states

Telomere maintenance through recruitment of internal genomic regions

Cells surviving crisis are often tumorigenic and their telomeres are commonly maintained through the reactivation of telomerase. However, surviving cells occasionally activate a recombination-based mechanism called alternative lengthening of telomeres (ALT). Here we establish stably maintained survivors in telomerase-deleted Caenorhabditis elegans that escape from sterility by activating ALT. ALT survivors trans-duplicate an internal genomic region, which is already cis-duplicated to chromosome ends, across the telomeres of all chromosomes. These 'Template for ALT' (TALT) regions consist of a block of genomic DNA flanked by telomere-like sequences, and are different between two genetic background. We establish a model that an ancestral duplication of a donor TALT region to a proximal telomere region forms a genomic reservoir ready to be incorporated into telomeres on ALT activation.

Synergistic combination therapy using cowpea mosaic virus intratumoral immunotherapy and Lag-3 checkpoint blockade.

Immune checkpoint therapy (ICT) for cancer can yield dramatic clinical responses; however, these may only be observed in a minority of patients. These responses can be further limited by subsequent disease recurrence and resistance. Combination immunotherapy strategies are being developed to overcome these limitations. We have previously reported enhanced efficacy of combined intratumoral cowpea mosaic virus immunotherapy (CPMV IIT) and ICT approaches. Lymphocyte-activation gene-3 (LAG-3) is a next-generation inhibitory immune checkpoint with broad expression across multiple immune cell subsets. Its expression increases on activated T cells and contributes to T cell exhaustion. We observed heightened efficacy of a combined CPMV IIT and anti-LAG-3 treatment in a mouse model of melanoma. Further, LAG-3 expression was found to be increased within the TME following intratumoral CPMV administration. The integration of CPMV IIT with LAG-3 inhibition holds significant potential to improve treatment outcomes by concurrently inducing a comprehensive anti-tumor immune response, enhancing local immune activation, and mitigating T cell exhaustion

Enhanced Efficacy of a TLR3 Agonist Delivered by Cowpea Chlorotic Mottle Virus Nanoparticles

Intratumoral immunotherapies are those that are administered directly into a tumor to remodel the local tumor microenvironment and stimulate systemic anti‐tumor immunity. Small molecule toll‐like receptor (TLR) agonists are undergoing development as intratumoral immunotherapies, and here the TLR3 agonist poly(I:C) is considered. Because small molecule therapeutics often suffer rapid washout effects and ineffective immune cell uptake, poly(I:C) is encapsulated into nanoparticles derived from cowpea chlorotic mottle virus (CCMV). The particles (but not the separate components) stimulate the activity of macrophages in vitro and are able to reduce tumor growth and prolong survival in mouse models of colon cancer and melanoma. CCMV‐poly(I:C) is also combined with oxaliplatin and found the combination therapy to be even more potent, strongly inhibiting tumor growth and increasing survival. The analysis of immune markers reveals that CCMV‐poly(I:C) VLPs with oxaliplatin promoted the infiltration and activation of CD4+ and CD8+ cells and the production of IL‐4 and IFN‐γ, indicating a synergistic immunogenic effect. The combined treatment also enhances the rate of apoptosis and immunogenic cell death (ICD). The data support the development of combination therapies involving immunomodulatory plant virus nanoparticles and antineoplastic drugs to attack tumors directly and via the activation of innate and adaptive immune responses

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network–long short-term memory (CNN-LSTM) and CNN–gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance

PM_2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient

Fault Detection Method via k-Nearest Neighbor Normalization and Weight Local Outlier Factor for Circulating Fluidized Bed Boiler with Multimode Process

In modern complex industrial processes, mode changes cause unplanned shutdowns, potentially shortening the lifespan of key equipment and incurring significant maintenance costs. To avoid this problem, a method that can detect the fault of equipment operating in various modes is required. Therefore, we propose a novel fault detection method that uses the k-nearest neighbor normalization-based weight local outlier factor (WLOF). The proposed method performs local normalization using neighbors to consider possible mode changes in the normal data and WLOF is used for fault detection. In contrast to statistical methods, such as principal component analysis (PCA) and independent component analysis (ICA), the local outlier factor (LOF) uses the density of neighbors. However, because LOF is significantly affected by the distance between its neighbors, the weight is multiplied proportionally to the distance between each neighbor to improve the fault detection performance of the LOF. The efficiency of the proposed method was evaluated using a multimode numerical case and a circulating fluidized bed boiler. The experimental results show that the proposed method outperforms conventional PCA, kernel PCA (KPCA), k-nearest neighbor (kNN), and LOF. In particular, the proposed method improved the detection accuracy by 20% compared with conventional methods. Therefore, the proposed method can be applied to a real process operating in multiple modes

Systemic Administration of Cowpea Mosaic Virus Demonstrates Broad Protection Against Metastatic Cancers.

The key challenge in cancer treatment is prevention of metastatic disease which is therapeutically resistant and carries poor prognoses necessitating efficacious prophylactic approaches that prevent metastasis and recurrence. It is previously demonstrated that cowpea mosaic virus (CPMV) induces durable antitumor responses when used in situ, i.e., intratumoral injection. As a new direction, it is showed that CPMV demonstrates widespread effectiveness as an immunoprophylactic agent - potent efficacy is demonstrated in four metastatic models of colon, ovarian, melanoma, and breast cancer. Systemic administration of CPMV stimulates the innate immune system, enabling attack of cancer cells; processing of the cancer cells and associated antigens leads to systemic, durable, and adaptive antitumor immunity. Overall, CPMV demonstrated broad efficacy as an immunoprophylactic agent in the rejection of metastatic cancer