ウガンダ共和国ブウィンディ原生国立公園におけるマウンテンゴリラの観光と保全

京都大学新制・課程博士博士(地域研究)甲第23302号地博第283号新制||地||108(附属図書館)京都大学大学院アジア・アフリカ地域研究研究科アフリカ地域研究専攻(主査)教授 山越 言, 教授 大山 修一, 准教授 佐藤 宏樹, 助教 木下 こづえ学位規則第4条第1項該当Doctor of Area StudiesKyoto UniversityDGA

Haphazard Sharing of Plant Food among the Baka Hunter-Gatherers in Southeast Cameroon

Most studies of food sharing among hunter-gatherers have focused on meat. However, sharing of meat is not the only food sharing practice among hunter-gatherers. Today, the Baka hunter-gatherers live a semi-sedentary lifestyle in southeast Cameroon, spending more than half of the year in semi-permanent settlements near roads. As their lifestyle has changed, their diets have become dependent on agricultural crops. Plant foods, including agricultural crops, show less variation in yield among harvesters than wild meat, and therefore they are not expected to be shared as frequently as meat. However, we observed that the Baka frequently practiced food sharing even in their settlements. Among the Baka, the women who cook decide to whom to give the food. They gave dishes preferentially to close kin, which contributed to increasing their inclusive fitness, and therefore kin selection at least partly explained their food sharing practices. However, they also gave dishes preferentially to their husbands' kin, which did not necessarily increase the women's inclusive fitness. In addition, sharing with distant kin formed a considerable part of the sharing network. Furthermore, visits made to the cooks influenced the subsequent sharing. In summary, the Baka practice food sharing according to plural and complex principles, and because of this hybrid nature, their food sharing practices appear to be haphazard. The results also have implications for the distinction between sharing and reciprocal gift-exchange. Food sharing among the Baka is characterized by imbalances in mutual giving and returning. Although it is much easier to balance mutual giving and returning for agricultural crops than meats, they do not pay attention to this. Unlike reciprocal gift-exchange, which involve a timeline of alternating mutual giving and returning, sharing is practiced of the basis on contingent face-to-face interactions in everyday life

Recent Trends in Sensor-based Activity Recognition

This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.Maekawa T., Xia Q., Otsuka R., et al. Recent Trends in Sensor-based Activity Recognition. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 36 (2023); https://doi.org/10.1109/MDM58254.2023.00018

Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

Otsuka R., Yoshimura N., Tanigaki K., et al. Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers. Methods in Ecology and Evolution 15, 716 (2024); https://doi.org/10.1111/2041-210X.14294.Machine learning-based behaviour classification using acceleration data is a powerful tool in bio-logging research. Deep learning architectures such as convolutional neural networks (CNN), long short-term memory (LSTM) and self-attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration-based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal-specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre-training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state-of-the-art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time-warping and rotation) was randomly applied to each data during mini-batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre-training with unlabelled data did not improve model performance. The state-of-the-art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi-head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration-based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self-supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time-series sensor data

Predicting bushmeat biomass from species composition captured by camera traps: Implications for locally based wildlife monitoring

アフリカ熱帯雨林における野生動物資源量推定の有効な指標を発見 --地域住民主体の野生動物モニタリング法の基礎を確立--. 京都大学プレスリリース. 2022-08-26.Cameras candidly capture bushmeat mammals to avert crisis. 京都大学プレスリリース. 2022-08-30.1. Facing the bushmeat crisis, tropical forests require effective monitoring for sustainable wildlife management. To gain credibility with local people and conservation officials, the monitoring needs indicators that comply with local knowledge and predict the available faunal resources. 2. This study explores predictive indicators for bushmeat biomass --the total biomass of five main hunted mammals-- in a Cameroonian rainforest. We employed camera trapping and the Random Encounter and Staying Time (REST) model to estimate the spatial variation in each species' population density and bushmeat biomass at three sites. We then calculated six indicators from camera-trap capture rate estimates and assessed their predictive performance for the total wild meat amount. 3. Duikers generally increased with distance from the public road, but two red duiker species were more markedly affected by the distance than blue duikers. Spatial density patterns of brush-tailed porcupines and Emin's pouched rats differed between sites. Consequently, bushmeat biomass displayed exponential growth away from the road with varying degrees among the sites. 4. Of the six indicators, the R/B ratio (red-to-blue duiker ratio) and the D/R ratio (duiker-to-rodent ratio) exhibited positive linear-like correlations to bushmeat biomass at all sites. The correlation lines were moderately similar across sites in the R/B ratio but largely different in the D/R ratio, suggesting that the latter is unsuitable for sharing information between neighbouring communities. 5. Synthesis and applications. The two indicators based on captured animal composition may effectively predict the total biomass of the main target species for bushmeat hunting, given a reasonably large sample size. The R/B ratio (red duikers/blue duikers) is recommended as a first choice; the D/R ratio (duikers/rodents) can be a good alternative when information sharing is not essential. Because local hunters are aware of depletion-related changes in species composition of caught animals, these indices may be effectively incorporated into community-based wildlife monitoring.1. Face à la crise de la viande de brousse, les forêts tropicales nécessitent un système de suivi efficace pour une gestion durable de la faune. Pour gagner en crédibilité auprès des populations locales et des responsables de la conservation, le suivi a besoin d'indicateurs qui respectent les connaissances locales et prédisent les ressources fauniques disponibles. 2. Cette étude explore des indicateurs prédictifs de la biomasse de la viande de brousse --la biomasse totale de cinq principaux mammifères chassés-- dans une forêt tropicale camerounaise. Nous avons utilisé des caméra-piège et le modèle REST (Random Encounter and Staying Time) pour estimer la variation spatiale de la densité de chaque espèce et de la biomasse de viande de brousse sur trois sites. Nous avons ensuite calculé six indicateurs à partir des estimations du taux de capture par caméra-piège et évalué leur performance prédictive pour la quantité totale de viande sauvage. 3. Le nombre de céphalophes augmentaient généralement avec la distance de la route publique, mais deux espèces de céphalophes roux étaient plus fortement affectés par la distance que les céphalophes bleus. Les modèles de densité spatiale des porcs-épics et des rats géants d'Emin différaient selon les sites. Par conséquent, la biomasse de viande de brousse a affiché une croissance exponentielle en s'éloignant de la route, avec des degrés variables selon les sites. 4. Parmi les six indicateurs, le ratio R/B (le ratio des céphalophes rouges par rapport aux céphalophes bleus) et le ratio D/R (le ratio des céphalophes par rapport aux rongeurs) présentaient des corrélations linéaires avec la biomasse de viande de brousse sur tous les sites. Les lignes de corrélation étaient modérément similaires d'un site à l'autre pour le ratio R/B mais largement différentes pour le ratio D/R, ce qui suggère que ce dernier n'est pas approprié pour le partage d'informations entre communautés adjacentes. 5. Synthèse et applications. Les deux indicateurs basés sur la composition des animaux capturés peuvent prédire efficacement la biomasse totale des principales espèces cibles de la chasse à la viande de brousse, à condition de disposer d'une taille d'échantillon raisonnablement importante. Le ratio R/B (les céphalophes rouges/bleus) est recommandé comme premier choix; le ratio D/R (les céphalophes/les rongeurs) peut être une bonne alternative lorsque le partage des informations n'est pas essentiel. Comme les chasseurs locaux sont conscients des changements liés à l'épuisement dans la composition des espèces des animaux capturés, ces indicateurs peuvent être efficacement intégrés dans le suivi communautaire de la faune

Analyzing the popularity of YouTube videos that violate mountain gorilla tourism regulations

人とゴリラが接近する「危険」な映像が視聴者の興味を引くことを解明 --YouTube動画の内容と視聴者の反応の分析--. 京都大学プレスリリース. 2020-06-03.Although ecotourism is expected to be compatible with conservation, it often imposes negative effects on wildlife. The ecotourism of endangered mountain gorillas has attracted many tourists and functioned as a key component of their conservation. There might be expectations on the part of tourists to observe or interact with gorillas in close proximity and such expectations may have been engendered by the contents of social media in this Information Age. However, the risk of disease transmission between humans and gorillas is a large concern and it is important to maintain a certain distance while observing gorillas to minimize risk. We conducted a content analysis and described the general characteristics of 282 YouTube videos related to mountain gorilla tourism. Humans and gorillas were observed simultaneously in 70% of the videos, and physical contact or close proximity within arm’s reach were identified in 40%. To explore the factors affecting the number of views and likes that these videos received, we ran generalized linear mixed models and performed AIC model selection with 206 videos in which humans and gorillas were observed simultaneously. Videos obtained more views and likes when the thumbnail photos included humans and gorillas together, while videos with thumbnail photos of only gorillas did not obtain more views and likes compared with those that included no gorillas. Moreover, videos obtained more views and likes in cases where physical contact or close proximity within arm’s reach with gorillas were clearly observed, compared with those that did not clearly include close human-gorilla interaction. These results suggest that human-gorilla interaction and proximity with gorillas attract more public attention than gorillas shown by themselves. Our study highlights the importance of further investigation on the direct link between such contents that violate tourism regulations and the conflicting situation

Moderate Molecular Recognitions on ZnO m-Plane and Their Selective Capture/Release of Bio-related Phosphoric Acids

Herein, we explore the hidden molecular recognition abilities of ZnO nanowires uniformly grown on the inner surface of an open tubular fused silica capillary via liquid chromatography. Chromatographic evaluation revealed that ZnO nanowires showed a stronger intermolecular interaction with phenylphosphoric acid than any other monosubstituted benzene. Furthermore, ZnO nanowires specifically recognized the phosphate groups present in nucleotides even in the aqueous mobile phase, and the intermolecular interaction increased with the number of phosphate groups. This discrimination of phosphate groups in nucleotides was unique to the rich (10[1 with combining macron]0) m-plane of ZnO nanowires with a moderate hydrophilicity and negative charge. The discrimination could be evidenced by the changes in the infrared bands of the phosphate groups on nucleotides on ZnO nanowires. Finally, as an application of the molecular recognition, nucleotides were separated by the number of phosphate groups, utilizing optimized gradient elution on ZnO nanowire column. Thus, the present results elucidate the unique and versatile molecular selectivity of well-known ZnO nanostructures for the capture and separation of biomolecules

Recent Trends in Sensor-based Activity Recognition

Maekawa T., Xia Q., Otsuka R., et al. Recent Trends in Sensor-based Activity Recognition. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 36 (2023); https://doi.org/10.1109/MDM58254.2023.00018.This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding

Characterization of Amorphous Solid Dispersion of Pharmaceutical Compound with pH-Dependent Solubility Prepared by Continuous-Spray Granulator

A continuous-spray granulator (CTS-SGR) is a one-step granulation technology capable of using solutions or suspensions. The present research objectives were, (1) to reduce the manufacturing operations for solid dosage formulations, (2) to make amorphous solid dispersion (ASD) granules without pre-preparation of amorphous solids of active pharmaceutical ingredients (API), and (3) to characterize the obtained SGR granules by comprehensive pharmaceutical analysis. Rebamipide (RBM), a biopharmaceutical classification system class IV drug, that has low solubility or permeability in the stomach, was selected as a model compound. Five kind of granules with different concentrations of polyvinylpyrrolidone/vinyl acetate copolymer (PVP-VA) were prepared using a one-step SGR process. All of the SGR granules could be produced in amorphous or ASD form and their thermodynamic stability was very high because of high glass transition temperatures (>178 °C). They were unstable in 20 °C/75%RH; however, their stability was improved according to the proportion of polymer. The carboxy group of RBM was ionized in the granules and interactions appeared between RBM and PVP-VA, with the formation of an ASD confirmed and the solubility was enhanced compared with bulk RBM crystals. The SGR methodology has the possibility of contributing to process development in the pharmaceutical industry