Visual Analysis of Brain Activity from fMRI Data

Classically, analysis of the time-varying data acquired during fMRI experiments is done using static activation maps obtained by testing voxels for the presence of significant activity using statistical methods. The models used in these analysis methods have a number of parameters, which profoundly impact the detection of active brain areas. Also, it is hard to study the temporal dependencies and cascading effects of brain activation from these static maps. In this paper, we propose a methodology to visually analyze the time dimension of brain function with a minimum amount of processing, allowing neurologists to verify the correctness of the analysis results, and develop a better understanding of temporal characteristics of the functional behaviour. The system allows studying time-series data through specific volumes-of-interest in the brain-cortex, the selection of which is guided by a hierarchical clustering algorithm performed in the wavelet domain. We also demonstrate the utility of this tool by presenting results on a real data-set.     

A unifying framework for robot control with redundant DOFs

Recently, Udwadia (Proc. R. Soc. Lond. A 2003:1783–1800, 2003) suggested to derive tracking controllers for mechanical systems with redundant degrees-of-freedom (DOFs) using a generalization of Gauss’ principle of least constraint. This method allows reformulating control problems as a special class of optimal controllers. In this paper, we take this line of reasoning one step further and demonstrate that several well-known and also novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the derived controllers. The suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

Structural Identification and Damage Detection from Noisy Modal Data

In this paper we present a simple, yet powerful, method for the identification of stiffness matrices of structural and mechanical systems from information about some of their measured natural frequencies and corresponding mode shapes of vibration. The method is computationally efficient and is shown to perform remarkably well in the presence of measurement errors in the mode shapes of vibration. It is applied to the identification of the stiffness distribution along the height of a simple vibrating structure. An example illustrating the method’s ability to detect structural damage that could be highly localized in a building structure is also given. The efficiency and accuracy with which the method yields estimates of the system’s stiffness from noisy modal measurement data makes it useful for rapid, on-line damage detection of structures.     

Nonideal Constraints and Lagrangian Dynamics

This paper deals with mechanical systems subjected to a general class of non-ideal equality constraints. It provides the explicit equations of motion for such systems when subjected to such nonideal, holonomic and∕or nonholonomic, constraints. It bases Lagrangian dynamics on a new and more general principle, of which D'Alembert's principle then becomes a special case applicable only when the constraints become ideal. By expanding its perview, it allows Lagrangian dynamics to be directly applicable to many situations of practical importance where non-ideal constraints arise, such as when there is sliding Coulomb friction.     

Customizing ultraviolet curable polymer-ink properties via reactive diluent modification and viscosity control

In inkjet 3D printing, material selection plays a crucial role in shaping both printing performance and material characteristics. Achieving the desired properties relies on precisely formulated compositions. In this context, ultraviolet (UV) curable polymers require specific viscosities, typically ranging from 3700 to 5700 mPa·s, to function effectively in 3D printing's material jetting technique, preventing complications during material dispensing. This study formulates UV-curable polymers by blending four distinct monomers: Genomer 4247 Aliphatic Urethane Dimethacrylate, 1,10-Decanediol Dimethacrylate, Isobornyl Acrylate, and Methyl Acrylate, within a controlled dark room environment at room temperature to prevent undesired crosslinking reactions during mixing. The selection of these monomers and their compositions is meticulous, considering their capacity to achieve specified viscosities and enhance overall material performance. This formulation process yields a wide range of UV-curable polymer viscosities. The study employs comprehensive methodologies, including rheometry, Fourier transform infrared analysis, Soxhlet extraction, thermal analysis, and tensile testing, for rigorous evaluation of viscosity, curing efficiency, thermal characteristics, and mechanical performance. Notably, mechanical and chemical performance exhibits marginal differences within the viscosity range, attributed to UV-curable polymer crosslinking, consistently exceeding 99% for all samples. However, the polymer composed of 98% v/v oligomer and methyl acrylate (MA) demonstrates notably better thermal and mechanical properties due to its 99.91% gel content crosslinking. Remarkable polymer fabrication thus occurs within the desired viscosity range.     

Indoor air pollution and self-reported diseases - a case study of NCT of Delhi

People in modern societies often spend 80-90% of their time in indoor environments. It is, therefore, imperative to analyze indoor air quality (IAQ) and its determinants and to consider the contribution of IAQ to possible health outcomes at the household level. Based on empirical data collected from 5949 households from 35 wards of Delhi, it can be summarized that higher proportions of residents live in degraded indoor environmental conditions. The highest risks to health were attached to use of traditional fuels (64%), lack of a kitchen (59%), exposure to environmental tobacco smoke (ETS) (55%), and poor ventilation (55%). Acute respiratory infections (43%) were identified as one of the most prevalent health problems confronted by residents and are strongly associated with use of traditional fuels (adjusted OR 2.7, 95% CI 2.3-3.1). Asthma shows a significant relationship with the use of traditional fuels (adjusted OR 3.8, 95% CI 3.4-4.3), exposure to ETS (adjusted OR 2.5, 95% CI 2.2-2.7), and poor ventilation (adjusted OR 1.26, 95% CI 1.13-1.41). Lung cancer (adjusted OR 1.54, 95% CI 1.38-1.71) and cardiovascular diseases (adjusted OR 2.25, 95% CI 2.01-2.53) also show a strong relationship with ETS exposure. More research is needed. PRACTICAL IMPLICATIONS: The present study can help to create new insights in understanding the gravity of indoor air quality problems in Delhi and can therefore provide interesting material to social scientists, public health officers, planners, and decision makers. The information can be utilized to help formulate comprehensive policies and planning with a humanistic approach for proper urban indoor environments that will be applicable at all administrative levels, viz. local, national, and international, and will also provide an important background for additional research in this area.     

Equations of motion for general constrained systems in Lagrangian mechanics

This paper develops a new, simple, explicit equation of motion for general constrained mechanical systems that may have positive semi-definite mass matrices. This is done through the creation of an auxiliary mechanical system (derived from the actual system) that has a positive definite mass matrix and is subjected to the same set of constraints as the actual system. The acceleration of the actual system and the constraint force acting on it are then directly provided in closed form by the acceleration and the constraint force acting on the auxiliary system, which thus gives the equation of motion of the actual system. The results provide deeper insights into the fundamental character of constrained motion in general mechanical systems. The use of this new equation is illustrated through its application to the important and practical problem of finding the equation of motion for the rotational dynamics of a rigid body in terms of quaternions. This leads to a form for the equation describing rotational dynamics that has hereto been unavailable.     

Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System

It is remarkable that several hydrological parameters have a significant effect on the reservoir operation. Therefore, operating the reservoir system is complex issue due to existing the nonlinearity hydrological variables. Hence, determining modern model has high ability in handling reservoir operation is crucial. The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. The major objective for the proposed model is minimizing the deficit volume between water releases and the irrigation water demand. The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed models have been utilized of finding the optimal policies to operate Timah Tasoh Dam, which is located in Malaysia. The study utilized considerable statistical indicators to explore the efficiency of the models. The simulation period showed that SMLA approach outperforms both of conventional algorithms. The SMLA attained high Reliability and Resilience (Rel. = 0.98%, Res. = 50%) and minimum Vulnerability (Vul. = 21.9 of demand). It is demonstrated that shark machine learning algorithm would be a promising tool in handling the long-term optimization problem in operation a reservoir system.