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Reinforced Concrete (RC) has been extensively used in the construction of buildings and infrastructure facilities. Particularly, RC bridge piers have been widely utilized in the construction of highways, mountainous, and river elevated bridges due to their cost-effectiveness, ease of construction, durability, seismic resistance, and corrosion resistance. In the design and construction of bridge piers, the bond performance between reinforcement and concrete is crucial. Ensuring sufficient bond strength between the materials is essential for reliable stress transmission. In most RC structures, deterioration of bond strength between reinforcement and concrete in column boundaries and within footings leads to slippage phenomena, reducing the column’s load-bearing capacity and rigidity, resulting in a decrease in the seismic performance of RC structures. Previous studies have shown that the diameter and arrangement of axial bars significantly affect the bond performance at the joint. Therefore, in bridge piers with densely arranged small-diameter axial bars, the bond between axial bars and footing concrete may be lost due to decreased anchorage performance, possibly changing the failure mode from flexural failure, as assumed in current designs, to a failure mode caused by rocking deformation. In this study, considering the above background, cyclic loading tests and finite element analysis based on reduced-scale RC column models, consisting of different diameters and numbers of axial bars with similar reinforcement ratios and strengths, were conducted. Through these, the influence of bond-slip phenomena in RC bridge piers with densely arranged small-diameter axial bars on the seismic reinforcement performance of RC columns was investigated. The structure of this paper is described below. In Chapter 2, cyclic loading tests using RC column specimens with densely arranged small-diameter axial bars, having similar reinforcement ratios and strengths compared to the standard reduced-scale RC bridge pier models commonly used in previous studies, were conducted. The influence of small-diameter axial bars on the deformation and load-bearing performance and failure mechanisms of RC columns was compared with standard specimens. Specifically, analyses and considerations were made regarding the strain history of axial bars at loading stages, load-strain relationship history, damage conditions of reinforcements inside the specimens, and rotational deformation behaviors calculated from vertical displacements on both sides of the column base. In Chapter 3, reproduction analysis of cyclic loading tests based on nonlinear finite element methods was conducted. It was clarified that it is necessary to consider the bond between axial bars and concrete. A new modeling method to reproduce the bond-slip phenomena between axial bars and concrete in RC columns was proposed. In these numerical analysis methods, focusing on the bond-slip behavior of reinforcements at the joint and differences in bond failure characteristics caused by different reinforcement arrangements, detailed analyses were conducted on how they affect the overall deformation and load-bearing performance of RC columns. From these analyses and considerations, the performance and failure mechanisms of RC columns with densely arranged small-diameter axial bars were summarized. In Chapter 4, the possibility of seismic reinforcement for RC columns with small-diameter axial bars was verified. Even now, various reinforcement works are being conducted for existing transportation infrastructure facilities for reasons such as improving the seismic performance of RC bridge piers, extending the life of aging structures, and taking measures against imminent heavy rain disasters. In the case of existing RC bridge piers designed and constructed based on old seismic standards, many of them use smaller diameter axial bars compared to current standards and do not have sufficient flexural strength. Also, in reinforcement, it is necessary to select a construction method that comprehensively considers seismic resistance, durability, workability, and economy. Especially when applying to river piers, it is necessary to smoothly construct within a limited construction period, and in some cases, a reinforcement method with a thin wrapping thickness is chosen to reduce the riverbed occupancy rate and maintain its performance for a long time. Since it is unclear whether the reinforcement effect can be sufficiently expected even if reinforcement is performed, cyclic loading tests were conducted on specimens reinforced with PCM materials for RC columns with insufficient deformation performance due to such reinforcements and anchorage conditions, and the load-bearing deformation performance was evaluated. Detailed verification was conducted focusing on the suppression effect of anchorage failure of axial bars and rotational deformation in the plastic hinge section caused by bond failure. It was clarified that the high-strength PCM material pouring reinforcement method can suppress the anchorage failure of existing part reinforcements and the rocking deformation of the existing part. In Chapter 5, verification based on nonlinear finite element methods was conducted on the specimens reinforced in the previous chapter, focusing on the suppression effect of anchorage failure of axial bars in the existing part and rocking deformation due to the wrapping reinforcement of PCM materials targeted in cyclic loading tests. By appropriately modeling the PCM reinforced part and the reinforced part reinforcements, it was possible to reproduce the pinching phenomena observed in the unloading and reloading history of cyclic loading tests, and it was clarified that the rocking deformation of the plastic hinge part caused by bond failure at the base of the specimen could also be suppressed. Finally, the conclusions of each chapter were summarized, and a comprehensive summary of the research results on the seismic reinforcement performance of RC bridge piers with densely arranged small-diameter axial bars focusing on bond-slip behavior was conducted. Also, unresolved issues in this study were raised, and descriptions were made regarding future research issues.

製品にはプロダクトライフサイクルがあり，その段階ごとに要求される開発内容も変化するため，市場における製品のイノベーションの状態を把握することが重要である. また，ドミナントデザイン発現前に製品を市場に投入することは，製品が広く受け入れられるための有効な手段の一つであるといわれている. しかし，ドミナントデザイン発現時期は，事後にしかわからないという問題があり，その対策として，特許情報を使った手法が多くの研究者によって検討されているが，製品に関する技術の専門家が必要であることが課題である. 例えば，分析に使う特許分類コードや技術の専門用語が特定できないことである. 特許情報を使った先行研究では，特許分類コードを使った方法，テキストマイニングを使った方法，機械学習・ディープラーニングを使った方法があるが，製品に関する技術の専門家の知見が必要とされている．そこで，製品に関する技術の専門家の判断によらず，イノベーションの状態変化，ドミナントデザインの発現時期を得る方法に対する社会的要求がある． 本研究では，製品に関する技術の専門家の判断によらず，日本の特許情報と分類コードのFタームを使い，ドミナントデザインの発現時期を得る新たな手法を提案した.また，分析手法の有用性の検証として，製品の開発事例を元に，ドミナントデザインの発現時期を示すことができることを確認した．ここで，検証には，組み立てて完成する製品で，精密機器・装置分野の製品であり，かつＦタームが付与されているものを対象とした． 本論文は，以下の4章から成る. 1章では，研究の背景を述べ，先行研究の調査を行った. 先行研究の課題を認識し，取り組む課題を考え，本研究の目的を定めた. さらに，本論文の構成のアウトラインを示した. 2章では，製品に関する技術の専門家の判断によらず，Fタームを使い，イノベーションの状態変化，ドミナントデザイン発現時期を捉える新たな手法を提案した. 提案した新たな手法では，まず，分析対象の製品に関する特許を選定するために，特許分類コードのFIを特定する必要がある．FIを特定する手法の検討では，カメラを対象とし，製品を表す一般的な単語からFIを求めることができることを示した．また同時に，コア技術を表す特許分類コードを顧客の声から特定するため，日本の農業用草刈り機メーカーを分析し，コア技術を表すテーマコードが特定できることを示した．つぎに，イノベーションの状態変化とドミナントデザインの発現時期を得る手法を確認するために，先のそれぞれの結果及びFタームを用いて，製品に関するFIから，Fタームを特定し，Fタームから，イノベーションの状態変化を求めた．求めたイノベーションの状態変化から，AbernathyとUtterbackが提唱したA-Uモデルの条件を適用し，ドミナントデザインの発現時期を特定できることを示した．また，インクジェットプリンタ，NC加工機，プロジェクタについて分析し，ドミナントデザイン発現時期を求め，製品のドミナントデザイン時期と比較した結果も示している． 3章では，2章で示した，新たな手法の有用性を検証した．有用性の検証は，製品開発に成功した日本企業を事例として分析し，2章で得た結果と比較した．この分析企業は，日本の業務用可食インクジェットプリンタ市場において，最も早く製品を投入した企業の一つであり，市場においてトップシェアを獲得している．製品開発の歴史と，ドミナントデザイン発現時期の分析結果を比較し，製品開発に着手したタイミング，製品を市場へ投入するタイミングは，ドミナントデザインの発現時期の前であることを示した．このことから，本研究で提案する手法は，製品開発において，製品を市場へ投入するタイミングを判断する際に有用であることを確認した．さらに，製品開発においては，ドミナントデザイン発現時期を鑑みた製品を市場へ投入するタイミングだけではなく，ターゲット選定，独自性や品質を実現する技術開発も重要であるため，事例でとりあげた企業の事業戦略，実行計画についても調査した．事例企業は，製品を市場へ投入するタイミングを定め，目標時期までにターゲット選定，製品の独自性を実現するための課題認識，課題解決のための技術開発を，戦略的，計画的に実施しており，このことからも，本研究で提案した新たな手法は，事業戦略において，製品を市場へ投入するタイミングを決定することに活用できることを示唆している． 4章では，2，3章を総括した. 2章，3章の成果をまとめ，本研究で新たに提案した特許情報と日本の特許分類コードのFタームを使った特許分析手法は，組み立てて完成する製品で，精密機器・装置分野の製品であり，かつFタームが付与されている製品において，製品に関する技術の専門家の判断によらずドミナントデザインの発現時期を特定できることを示し，企業の製品開発事例の検証から，提案した新たな手法の有用性を示唆している.また，本研究の限界と今後の展望，さらに，その実現のために取り組むべき課題を述べた．

In the dynamic global business landscape, finance and innovation stand as the twin pillars of corporate success. Both finance and innovation are vital for a company's long-term viability, demanding a harmonious interplay between prudent financial management and a culture that fosters innovation. Research in the field examining the relationship between a firm's finance and innovation is rapidly growing, offering profound insights into the dynamics shaping organizational success. While many empirical studies traditionally presumed that financial support drives innovative efforts, alternative perspectives support the reverse causation hypothesis, suggesting that innovation can stimulate financial performance. The current corporate management research often takes a segmented approach, focusing on either the signaling effect of innovation on financial performance or the influence of finance on innovation performance. While insightful, this segmented approach resembles examining separate puzzle pieces without considering the whole picture. We contend that finance and innovation are mutually interdependent, influencing each other. Our study uniquely explores both dimensions, investigating how financial resources stimulate innovation, and how innovation, primarily represented by patents, attracts investors and secures financial support. Focusing on Japanese corporations, our research provides a distinctive perspective due to Japan's diverse business landscape, strong patent system, and commitment to innovation. Japan's risk-averse market and global competition highlight the importance of innovation and the role of patents as signals for economic growth. The first study scrutinizes the intricate relationship between financial resources and firms' innovation outputs, exploring the influence of various financing sources, internal and external, inspired by the Pecking Order Theory. It involves a sample of 113 Japanese manufacturing firms listed on the JASDAQ market, using patent-based metrics to gauge technological innovation. The study highlights the crucial roles played by both internal and external financial resources in driving innovation outputs. Firms demonstrate a strong preference for self-generated financing, particularly internal funding. Additionally, the research unveils the complementary impact of debt financing, especially when internal resources are depleted, aligning with the Pecking Order Theory's risk principles. In our second study, we explore the reverse causation between innovation and finance, particularly during initial public offerings (IPOs). IPOs are pivotal, as they provide capital for growth and enhance a firm's reputation. However, information asymmetry poses a challenge, leading investors to rely on quality signals. We hypothesize that patents, as a proxy for innovation, mitigate information asymmetry because their information is verifiable, observable, and entails maintenance costs. Thus, a company with numerous patents before an IPO is likely to gain investor trust, leading to a more successful IPO. We analyze 338 newly listed Japanese firms across various industries, finding robust positive correlations between pre-IPO patent applications and IPO financial performance. This contribution enriches the literature on the impact of patents on IPO performance and illuminates the broader influence of innovation on finance. The third study delves into the dynamics of patent signaling within IPO firms, distinguishing between high-tech and low-tech sectors. High-tech firms often face more information asymmetry, with less transparency in R&D and patent disclosures, making them riskier for investors. Low- tech firms, with valuable patents and balanced resource allocation, are more accessible to investors. This raises the question of whether high-tech firms are less successful in using patent signals to raise total capital during the IPO process, as previous research has mainly focused on high-tech firms in technology-intensive markets. While prior studies often grouped all IPOs together or concentrated on specific industries, our study adds fresh insights to the entrepreneurship and innovation landscape by asserting that patents exert a more substantial influence on IPO success for low-tech companies in comparison to their high-tech counterparts. This observation underscores the necessity for an in-depth exploration of the patent signaling mechanism in IPOs, especially for low-tech firms characterized by simpler innovation portfolios and tangible assets appealing to risk-averse investors. Overall, our dissertation offers a comprehensive exploration of the interplay between finance and innovation in Japanese corporations, providing nuanced insights into the implications of this symbiotic relationship for businesses, policymakers, and scholars worldwide.

In China, there are about 800,000 congenital diseases among 20 million newborns, of which nearly 200,000 fetuses have serious defects or diseases. The birth of these sick fetuses brings serious economic burden and social problems to the family and even the society. It is therefore important to carry out early fetal monitoring in order to detect fetal defects and diseases as early as possible. Umbilical artery blood signals contain important information about fetal growth and development, reflecting various problems during pregnancy, such as intrauterine g rowth r etardationetardation（IUGR）, hypoxia and maternal hypertension, which can be determined by umbilical artery blood signals. Therefore, the analysis of umbilical artery blood signals is important for prenatal monitoring and fetal health status diagnosis. The acoustics pectral parameter method is a conventional technique for analyzing the umbilical artery blood signals and consists of three parameters that serve as clinical diagnostic criteria: resistance index (RI), pulsesatility index (PI) and maximum systolic/end diastolic umbilical flow velocity (S/D). However, these parameters ignore phase properties of the signal, such as phase delay, phase frequency and phase mode, and focus only on the fundamental statistical parameters of blood velocity, s uch as maximum, minimum and mean values. This may lead to clinical misdiagnosis. Umbilical artery blood signals have complicated structures and nonlinear characteristics in addition to changes in signal amplitude. This paper presents a comprehensive new approach for characteristics parameter extraction and classification of umbilical artery blood signals using fractal theory and Chaos theory in order to handle these complex structures and nonlinear properties of the signal. First, by focusing on the fract al characteristics of umbilical artery blood signals, the fractal dimension (BD) and the correlation dimension (CD) are obtained to verified that BD is positively correlated with the gestational week and CD is effective in discriminating normal from abnormal. Next, we obtain the maximum Lyapunov exponent (MLE) of the chaotic characteristics of umbilical artery blood time series, and verified its effectiveness in distinguishing normal signals from abnormal signals. Finally, a diagnostic model is proposed b y applying particle swarm optimization support vector machine (PSO SVM) to the conventional feature parameters (RI, PI, S/D) and newly obtained parameters (BD, CD, MLE) to classify and diagnose the umbilical blood signals in the four statuses (normal, oligohydramnios, umbilical cord around neck, fetal malposition). This doctoral dissertation consists of 6 chapters. Chapter 1 introduces the background and means of umbilical artery blood study as well as reviewing the current re search situation. The outline o f this dissertation is also given. In chapter 2, the fundamentals of fetal hemodynamics are described. The clinical significance and normal reference values of umbilical artery blood signal parameters are outlined. Details of the umbilical artery signal acquisition equipment, data classification and acquisition process are explained. In chapter 3，the fractal dimension box counting method (BD) and the correlation dimension (CD) are used to investigate the nonlinear characteristics of the umbilical artery blood signals based on fractal theory. First, the BD of the umbilical artery blood signals is calculated and the fractal characteristics of the signals are analyzed. Results show a positive relationship between the fractal dimension of umbilical artery blood signals and gestational weeks. A bnormal and normal umbilical artery signals are then classified into abnormal group and n ormal group. T h e Grassberg P rocaccia algorithm (GP algorithm) is used to calculate and analyze the CD of the two groups. T he overall CD of normal umbilical artery blood signals is greater than that of abnormal signal s. CD is significantly better at discriminating the normality of the umbilical artery blood signal compared to conventional parameters. Furthermore, t he Hurst exponent of umbilical artery blood signal is calculated and analyzed by Lo method. The results show that umbilical artery blood signal belong s to non sta tionary signal and show obvious “1/f fluctuation” characteristics. In chapter 4，c haotic phase space diagram method and m aximum L yapunov e xponent (MLE) are used to determine the chaotic characteristics of umbilical artery blood signals from qualitative and quantitative perspectives. The attractor reconstruction of umbilica l artery blood signals is performed in t hree d imension (3D) and t wo d imension (2D) phase space. The results show that the chaotic phase diagram of the time series for abnormal umbilical artery signals show a jumbled “ball of wool” state and the chaotic “shape” appears to converge. Application of the r eceiver o perating c haracteristic (ROC) curve to the obtained maximum Lyapunov exponent (MLE) shows that the rate of discrimination of normality of the umbilical artery blood signal is significantly better than the conventional feature parameters. In chapter 5，an artificial intelligent classifier is proposed to classify the four states of umbilical artery blood signals (normal, oligohydramnios, umbilical cord around neck and fetal malposition). The support vector machine (SVM) classifying method is constructed based on the conventional parameters, S/D, PI and RI. The particle swarm optimisation support vector machine (PSO SVM) classifier is also constructed using the fractal dimension (BD), correlation dimension ( CD) and maximum L yapunov exponent (MLE) derived in Chapters 3 and 4 as feature parameters. The results of the classification tests show that the PSO SVM classifier is more accurate , confirming the usefulness and effectiveness of the proposed classification method. In Chapter 6, summary of this dissertation and future work are described.

The COVID-19 pandemic has significantly transformed higher education, shifting it from traditional classrooms to online platforms. This change requires reassessment and adaptation of educational methods, particularly student assessment. Online formative assessments have become essential for improving teaching and learning outcomes because they provide immediate feedback, enable interactive support, and encourage selfassessment, thereby playing a key role in the learning process. The multiple-choice test is widely used to assess students. However, the inherent nature of multiple-choice questions poses the risk of obtaining correct answers, even without a genuine understanding of the content. To mitigate this issue, typical measures involve increasing the number of questions. To address this concern, this study implemented a new constraint aimed at enhancing the inherent characteristics of the multiple-choice format. This research objective focuses on investigating innovative scoring methods for formative assessments in online courses that can improve learning in higher education within the context of Yamaguchi University. This study evaluated the effectiveness of this learning assessment method by employing multiple-choice questions, presenting a practical and efficient approach for online formative learning assessment designed to assess a large student cohort. The new scoring method in this study extends Ikebururo's concepts that introduce partial scoring systems in MCQ design, driving the creation of a new scoring system centered on the "degree of matching.” This approach involved comparing the alignment between student responses and the instructor's design, resulting in a detailed five-level scoring system for four-choice questions. This scoring method hinges on evaluating how closely students’ answers align with the instructor's intended choices. Each question, with its four choices, is akin to a binary process, represented by a 4-digit binary number. Each digit in this comparison corresponds to a specific choice, allowing for a granular assessment of the match between student selection and the ideal answer. This innovative approach steps away from the conventional pass-fail binary system, offering a spectrum of evaluation outcomes. It provides a better understanding of students’ comprehension by gauging the extent of the alignment between their choices and the instructor's design. This method can enhance assessment accuracy by capturing the subtleties of student responses beyond mere correctness, earning partial points for partial knowledge or progress via multistep reasoning, promoting critical thinking, recognizing the importance of incremental progress, and capturing the depth of a respondent's knowledge. Initially, an extensive literature review established a theoretical framework, identifying gaps in the current understanding of online formative assessments. Subsequently, the study examined data collected from graduate students in the 'Advanced Research and Development Strategies' course at Yamaguchi University. The data span two academic years, 2019 and 2020, and provide a comparative view of face-to-face and online Lecturer Formats. Furthermore, the k-means clustering algorithm was used to analyze student performance using formative assessment scores. This method categorizes student performance into distinct clusters, revealing insights into individual learning behaviors. The k-means method, a popular technique in data mining and pattern recognition, efficiently groups data into 'k' clusters. It is effective for large datasets and versatile across various data types. The technique involves steps such as initialization, assignment, centroid updating, and convergence checking, and is instrumental in identifying performance patterns, enabling the development of more focused educational strategies. The results demonstrate the potential of the four-choice multiple-choice scoring method to revitalize online formative assessments. The key contributions of this study are as follows: ・Innovative Scoring Method: This study shows how the four-choice method can lead to more dynamic and engaging online assessments. This approach captures student performance more accurately and encourages deeper engagement with the material. ・Enhanced Student Engagement and Understanding: The new four-multiple-choice scoring method significantly affected student engagement and understanding. This fosters an environment in which students are more actively involved in their learning processes, contributing to better comprehension and retention of material. ・Practical Implications for Educators and Institutions: The need to adapt assessment strategies for digital learning, focusing on continuous feedback and personalized learning. ・Educational Technology Contribution: Key insights into adapting assessment strategies for digital learning, emphasizing continuous feedback, and personalized learning. This dissertation presents a comprehensive examination of new assessment techniques in the context of online learning. This provides a critical roadmap for educators and institutions to adapt to the digital educational environment for more effective and engaging assessment practices in online higher education.

As the population ages, the demand for elderly care services will continue to increase, which includes providing specialized care, daily life support, and medical health services. As a result, informal caregiving provided by non-professionals such as family, friends, neighbors, and volunteers is becoming more prevalent. Injuries that occur during caregiving can affect the caregiving’s life, especially their mental and physical health. Therefore, the correct positioning and posture during caregiving are crucial to prevent musculoskeletal disorders among caregivers. Although training programs are useful to reduce the risk of musculoskeletal disorders for informal caregivers, many of them express that it is still difficult for them to grasp the correct caregiving postures. Moreover, they struggle to obtain professional advice to correct their posture through long-term practice. Therefore, finding a targeted ergonomic posture risk assessment and guidance method is crucial to improve caregivers' posture-related risks, enhance work efficiency, and safeguard their physical health. Rapid Entire Body Assessment (REBA) is a postural risk assessment method based on ergonomics that has been attracting attention recently, and it basically evaluates the risk from the angle of each joint of the body. However, in caregiving movements, the way of load placed on the caregiver and the time to maintain the movements vary greatly depending on the weight and posture of the cared person, so the current risk assessment using REBA is insufficient for caregiving movements. Additionally, posture recognition algorithms such as OpenPose are often used to extract skeletons. With these techniques, problems such as missing skeletons or misrecognition often occur due to image conditions or the overlapping of multiple people, and skeleton extraction may sometimes fail. In this research, the Spatial Temporal Graph Convolution Network (ST-GCN) is applied to develop a technique for complementing missing skeletons based on behavioral features and a technique for correcting skeletons that are misrecognized due to overlapping people, and to improve the accuracy of calculating skeletal joint angles. In order to evaluate caregiving posture risk more appropriately, some parameters such as center of gravity trajectory, load duration, asymmetric load during caregiving movements are investigated and a new REBA method is proposed. This paper consists of six chapters. In Chapter 2, to solve the problems of skeleton misidentification and missing information by OpenPose an improved skeleton reconstruction method based on ST-GCN is propose. The method compensates for missing skeletons in terms of behavioral features and corrects incorrectly identified skeletons based on skeleton weight features. This approach improves the accuracy and robustness of pose recognition and allows more accurate estimation of skeletal joint angles and its REBA score. In Chapter 3, to address the issue of REBA evaluation scores being too high for caregiving scenarios, a postural risk assessment method (C-REBA) is proposed by considering the characteristics of caregiving task. Customize the traditional REBA method and add parameters such as center of gravity trajectory, load duration, and asymmetric loading to the evaluation score. the caregiving movements to assist in transferring from a bed to a wheelchair on a group of experienced nurses and a group of inexperienced caregivers are analyzed and the effectiveness of the C-REBA method is verified. In Chapter 4, a method that combines the ST-GCN framework and C-REBA for postural risk assessment is proposed. The deep neural network algorism is applied to learn motion features and additional features such as load duration, motion frequency, center of gravity variation, and asymmetric load. So that all evaluation parameters for C-REBA rules can be obtained automatically. With this method, postural risk assessment processes in caregiving operations can be performed automatically. In Chapter 5, "Behavior Analysis and Posture Assessment System" (BAPAS) is developed. BAPAS is a system aimed at assessing the risk of musculoskeletal disorders related to working postures in medical support work. This chapter introduces the functions and usefulness of this system and demonstrates how this system can be extended to other medical fields easily by setting parameter is settings. Chapter 6 provides a summary of the paper as a whole and future prospect.