Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigations of the acoustics of the vocal tract and vocal folds in vivo, ex vivo and in vitro

Speech and singing are of enormous importance to human culture, yet the physics that underlies the production and control of the voice is incompletely understood, and its parameters not well known, mainly due to the difficulty of accessing them in vivo. In the simplified but well-accepted source-filter model, non-linear vocal fold oscillation produces a sound source at a fundamental frequency and its multiples, the resonances of the vocal tract filter the spectral envelope of the sound to produce voice formants. In this thesis, both source and tract properties are studied experimentally and an in vitro experiment investigates how the filter can affect the source. The control of fundamental frequency by either air supply or mechanical control parameters is investigated ex vivo using excised human larynges. All else equal, and excluding the four types of discontinuity or hysteresis observed, the fundamental frequency was found to be proportional to the square root of subglottal pressure, which has implications for singing and speech production, particularly in tonal languages. Additionally, airflow through the glottis causes a narrowing of the aryepiglottic tube and can initiate ventricular and/or aryepiglottic fold oscillation without muscular control. The acoustic impedance of the vocal tract was measured in vivo over a range of 9 octaves and 80 dB dynamic range with the glottis closed and during phonation. The frequencies, magnitudes and bandwidths were measured for the acoustic and for the mechanical resonances of the surrounding tissues. The bandwidths and the energy losses in the vocal tract that cause them were found to be five-fold higher than the viscothermal losses of a dry, smooth rigid cylinder, and to increase during phonation. Using a simple vocal tract model and measurements during inhalation, the subglottal system resonances were also estimated. The possible effects of the filter on the source are demonstrated in an experiment on a water-filled latex vocal fold replica: changing the aero-acoustic load of the model tract by inserting a straw at the model lips changes the fundamental frequency. This result is discussed in the context of straw phonation used in speech therapy.
A cogging-torque-assisted motor drive for internal combustion engine valves

Internal combustion engine valve trains form the interface between the intake and exhaust systems and largely contribute to the overall engine performance, emissions and efficiency. Most modern engines use a camshaft to operate the valve train, but suffer from suboptimal performance since valve events cannot be dynamically altered during transient engine operation. Electromechanical valve actuation is a type of variable valve actuation that uses electromechanical actuators to replace the camshaft in an internal combustion engine. Electromechanical valve actuation promises to improve engine performance, reduce fuel consumption and lower harmful emissions by allowing for fully-independent control of the intake and exhaust valves. A major goal of electromechanical valve actuation is to achieve fully-independent valve control while minimizing the impact on concomitant systems, such as the charging and cooling systems. A new type of electromagnetic actuator is presented in this thesis, which uses cogging torque to recover kinetic energy in the form of a magnetic field. Cogging torque allows the presented actuator to be much more efficient and compact compared to other electromechanical valve actuators that use external mechanical spring systems. To utilize cogging torque effectively, design motivations are initially established and used to conceptualize a practical and efficient design. The proposed design is first simulated to predict its performance and later is experimentally validated using a fabricated prototype. The results of the experiments reveal a highly efficient and fast actuator design compared to other electromechanical actuators found in literature. The energy loss is further reduced by generating an optimal kinematic trajectory using the Nelder-Mead algorithm. The optimal kinematic trajectory enabled the proposed actuator design to be the most efficient electromechanical valve actuator found in literature. The results presented show the novel actuator design reduced losses by over 40% when compared with the most efficient electromechanical valve actuator published in literature and by over 70% when compared with a conventional camshaft. The conclusions of this thesis suggest a cogging-torqueassisted actuator could be feasibly retrofitted into an existing engine with only minor modifications due to its compact and highly efficient nature.
Alzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time

Objective: Despite the potential of machine learning techniques to improve dementia diagnostic processes, research outcomes are often not readily translated to or adopted in clinical practice. Importantly, the time taken to administer diagnostic assessment has yet to be taken into account in feature-selection based optimisation for dementia diagnosis. We address these issues by considering the impact of assessment time as a practical constraint for feature selection of cognitive and functional assessments in Alzheimer’s disease diagnosis. Methods: We use three different feature selection algorithms to select informative subsets of dementia assessment items from a large open-source dementia dataset. We use cost-sensitive feature selection to optimise our feature selection results for assessment time as well as diagnostic accuracy. To encourage clinical adoption and further evaluation of our proposed accuracy-vs-cost optimisation algorithms, we also implement a sandbox-like toolbox with graphical user interface to evaluate user-chosen subsets of assessment items. Results: We find that there are subsets of accuracy-cost optimised assessment items that can perform better in terms of diagnostic accuracy and/or total assessment time than most other standard assessments. Discussion: Overall, our analysis and accompanying sandbox tool can facilitate clinical users and other stakeholders to apply their own domain knowledge to analyse and decide which dementia diagnostic assessment items are useful, and aid the redesigning of dementia diagnostic assessments. Clinical Impact (Clinical Research): By optimising diagnostic accuracy and assessment time, we redesign predictive and efficient dementia diagnostic assessments and develop a sandbox interface to facilitate evaluation and testing by clinicians and non-specialists.
Do the time-varying effects of oil prices affect the trade balances of ASEAN-5 countries?

ABSTRACT This study investigates the effects of oil price shocks on five Association of Southeast Asian Nations (ASEAN-5) trade balances by employing a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR). The time-varying responses derived from this model indicate that the impacts of oil price shocks vary significantly across economies due to their distinct characteristics in the oil market. The results also demonstrate the significant time-varying impact of economic activity and the real exchange rate on the trade balances of the five countries. The time-varying responses show that the effects of oil prices on trade balances are more pronounced during global and local economic events. Our findings have important policy implications for each country to avoid the risks associated with fluctuations in oil prices.
Design of a 400 kW Gas Turbine Prototype

Decentralized power and heat generation is a growing trend throughout the world. In smaller applications, electrical power output less than few megawatts, reciprocating engines have dominated the market. In recent years, small sized gas turbines have emerged as challengers for the reciprocating engines. The small gas turbines have a growing share of the decentralized energy market, which itself is rapidly growing. Hence, improvements in small gas turbine efficiency have a significant impact from the economic and environmental perspective.In this paper, the design of a high efficiency 400 kW gas turbine prototype is described and discussed. The prototype is a two-spool, recuperated and intercooled gas turbine where both spools comprise of a radial compressor and turbine, a permanent magnet electric generator, an axial and two radial active magnetic bearings and two safety bearings.The prototype design was divided into five categories and each of the categories are discussed. The categories were: the process design, the turbomachinery design, the generator and electrical design, bearing design and rotor dynamic analysis, and mechanical design. The design of recuperator, intercooler, and combustion chamber were outsourced. Hence, they are not discussed in this paper.The prototype design process showed the readiness of the chosen technological selections, as well it showed that the type of machine under discussion can be designed and manufactured. © 2016 ASME
Active control analysis of passenger vehicle interior noise produced from tyre/road interaction

Active noise control (ANC) system is a possible solution for reducing the overall vehicle interior noise and minimising the noise annoyance experienced by the driver and passengers. ANC systems can be installed in vehicles in order to adapt the road noise to customer preferences and expectations. They bring human perception criteria other than loudness into the control design. However, the analysis in this paper deals with applying ANC technology to the tyre/road noise in a vehicle enclosure. In the automotive industry, both cost/weight requirements and increasing comfort demands explain the interest shown at the moment for ANC. In consideration of requirements of quietness, improvement and reduction of weight, an ANC system is based on vibration measurement (reference) at the driver's legs region and sound pressure level at the driver's head position. The neuro-fuzzy technique identifies a specified desired output from two input parameters. The results obtained indicate the feasibility of applying the methodology in this instance. Moreover, it has been verified that a good noise reduction performance can be obtained at the driver's head position over frequencies up to 400 Hz.
A Dynamic Programming Approach for Emergency Vehicle Dispatching Problems

In this research, emergency vehicle dispatching problems faced with in the wake of massive natural disasters are considered. Here, the emergency vehicle dispatching problems can be regarded as a single machine stochastic scheduling problems, where the processing times are independently and identically distributed random variables, are considered. The objective of minimizing the expected number of tardy jobs, with distinct job due dates that are independently and arbitrarily distributed random variables, is dealt with. For these problems, optimal static-list policies can be found by solving corresponding assignment problems. However, for the special cases where due dates are exponentially distributed random variables, using a proposed dynamic programming approach is found to be relatively faster than solving the corresponding assignment problems. This so-called Pivot Dynamic Programming approach exploits necessary optimality conditions derived for ordering the jobs partially.
Smart Underwater Pollution Detection Based on Graph-Based Multi-Agent Reinforcement Learning Towards AUV-Based Network ITS

The exploitation/utilization of marine resources and the rapid development of urbanization along coastal cities result in serious marine pollution, especially underwater diffusion pollution. It is a non-trivial task to detect the source of diffusion pollution, such that the disadvantageous effect of the pollution can be reduced. With the vision of 6G framework, we employ Autonomous Underwater Vehicle (AUV) flock and introduce the concept of AUV-based network. In particular, we utilize the Software-Defined Networking (SDN) technique to update the controllability of the AUV-based network, leading to the paradigm of SDN-enabled multi-AUVs network Intelligent Transportation Systems (SDNA-ITS). For SDNA-ITS, we utilize artificial potential field theories to model the control model. To optimize the system output, we introduce the graph-based Soft Actor-Critic (SAC) algorithm, i.e., a category of Multi-Agent Reinforcement Learning (MARL) mechanism where each AUV can be regarded as a node in a graph. In particular, we improve the optimization model based on Centralized Training Decentralized Execution (CTDE) architecture with the assistance of the SDN controller, by which each AUV can efficiently adjust its speed towards the diffusion source. Further, to achieve exact path planning for detecting the diffusion source, a dynamic detection scheme is proposed to output the united control policy to schedule the SDNA-ITS dynamically. Simulation results demonstrate that our approaches are available to detect the underwater diffusion source when the actual scenario is taken into account and perform better than some recent research products.
Molecular Signatures of Mitochondrial Complexes Involved in Alzheimer's Disease via Oxidative Phosphorylation and Retrograde Endocannabinoid Signaling Pathways

Objective The inability to intervene in Alzheimer's disease (AD) forces the search for promising gene-targeted therapies. This study was aimed at exploring molecular signatures and mechanistic pathways to improve the diagnosis and treatment of AD. Methods Microarray datasets were collected to filter differentially expressed genes (DEGs) between AD and nondementia controls. Weight gene correlation network analysis (WGCNA) was employed to analyze the correlation of coexpression modules with AD phenotype. A global regulatory network was established and then visualized using Cytoscape software to determine hub genes and their mechanistic pathways. Receiver operating characteristic (ROC) analysis was conducted to estimate the diagnostic performance of hub genes in AD prediction. Results A total of 2,163 DEGs from 13,049 background genes were screened in AD relative to nondementia controls. Among the six coexpression modules constructed by WGCNA, DEGs of the key modules with the strongest correlation with AD were extracted to build a global regulatory network. According to the Maximal Clique Centrality (MCC) method, five hub genes associated with mitochondrial complexes were chosen. Further pathway enrichment analysis of hub genes, such as oxidative phosphorylation and retrograde endocannabinoid signaling, was identified. According to the area under the curve (AUC) of about 70%, each hub gene exhibited a good diagnostic performance in predicting AD. Conclusions Our findings highlight the perturbation of mitochondrial complexes underlying AD onset, which is mediated by molecular signatures involved in oxidative phosphorylation (COX5A, NDUFAB1, SDHB, UQCRC2, and UQCRFS1) and retrograde endocannabinoid signaling (NDUFAB1) pathways.
Knowledge of Osteoporosis and Its Associated Factors among Public Health Professionals in a Municipal Office in Japan

Lifelong efforts to maintain bone health are beneficial for preventing osteoporosis, and public health professionals play an important role in that. The current study aimed to assess the knowledge of osteoporosis among public health professionals in a Japanese municipal office and explored associated factors. A questionnaire was distributed to 124 eligible public health professionals in a municipal office in S City, Japan; in total, 89 individuals who returned it were analyzed. Their knowledge of osteoporosis was assessed using the revised Osteoporosis Knowledge Test, its two subscales, and the Facts on Osteoporosis Quiz, translated into Japanese. To compare the level of knowledge between categories of selected factors, the Wilcoxon rank-sum test or the Kruskal–Wallis test was applied. The mean of correct answers ranged from 70 to 79%, depending on the scale. The knowledge level was significantly higher among registered dietitians than among public health nurses. Higher scores were significantly associated with past learning experience in off-the-job training, with having a family history of osteoporosis, and with having had a past osteoporosis test. Japanese public health professionals were likely to have moderate knowledge of osteoporosis. Several factors were implied to be associated with the knowledge level of osteoporosis.
Personalized finite element modelling of the proximal femur for fracture risk prediction

Because of the population ageing, osteoporosis and hip fractures are largely considered as a major health-care problem. The surgery, combined with the patients rehabilitation and their dependency management, involve a worrying socio-economical burden which must be reduced, but the high cost of any preventive strategy must also be taken into account. Bone mineral density measured by DXA is considered as the gold standard for fracture risk prediction, but this diagnostic tool is limited by the overlap that still exists between bone density values of fractured and non-fractured subjects. The purpose of this study was to develop Finite Element models in order to improve the hip fracture risk prediction. An anatomical subject-specific model that take into account the complex geometric and mechanical specificities of each specimen reached a good compromise between an accurate individual fracture load prediction (Fexp = 1,006 FMEF avec r = 0,87, IC95% = +- 2600 N) and a suitable numerical cost for further clinical use (numerical simulation 30mn). To improve the model personalisation, an experimental study on 92 bone samples (46 traction and 46 compression) led to a better prediction of the cortical mechanical properties from medical imaging (r = 0,42 a 0,72). A total of 40 pairs of human proximal femurs were also tested to failure in order to provide a robust database for the model validation. A parameterized model was simultaneously used in an experimental design study in order to understand the role of the geometrical parameters in the femoral strength. This work indicated the ability of the Finite Element modelling to become a complementary tool for fracture risk prediction, and underlined the major influence of the cortical bone and its mechanical strength.
Ecological aspects of meranti kunyit (Shorea macroptera Dyer) in Rantau Bertuah Forest, Siak Regency, Riau Province and the implication for forest management and conservation

Sumatra’s lowland tropical rainforest is in the third place after Kalimantan and Papua in terms of Indonesia’s plant species diversity. Shorea macroptera is one of the species from the Dipterocarpaceae family, known as an important commercial timber tree species. In September 2019, the study was conducted in Rantau Bertuah Forest, Siak Regency, Riau Province, Sumatra, with a total sample plot area of 1 ha. The data were analyzed to show the vegetation quantitative structure and composition on the study site using the Important Value Index, and the association of S. macroptera using the Ochiai index. The results recorded 55 plant species belong to 33 families on this study site. Dipterocarpaceae, Euphorbiaceae, and Fabaceae have the most abundant species, encompassed 51 species ≥10 cm in diameter with a total of 624 individuals. The result showed that the top three dominant species in this area, namely Callophyllum macrocarpum Hook.f. (IVI = 27.88%), Shorea parvifolia Dyer (IVI = 21.98%) and Gironniera nervosa Planch. (IVI = 19.99%). C. macrocarpum dominated all the regeneration levels: tree, sapling and seedling with IVI of 27.88%, 49.57% and 86.31%, respectively. S. macroptera has the strongest association with C. macrocarpum (Ochiai index of 0.73), followed by the S. parvifolia (Ochiai index of 0.61) and G. nervosa species (Ochiai index of 0.51). S. macroptera is widely used for industrial timber purposes and has limited distribution. Therefore, it is necessary to increase the efforts to manage it sustainably, especially in production forests.
A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context‐aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye‐tracking, user logs, and click‐stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.
Deep Diving with the Use of a Crabe Rebreather

Abstract The article provides the results of 31 experimental dives carried out within the depth range of H ∈ [60; 80] mH2O. A combined mathematical model for ventilation and decompression was proposed with the possibility of an emergency omission of the last station at 3 mH2O and decompression completion at 6 mH2O in the event of a deterioration in weather conditions.
Gambling Versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial-and-learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.