The assessment of material handling strategies in dealing with sudden loading: The effects of load handling position on trunk biomechanics

Back injury caused by sudden loading is a significant risk among workers that perform manual handling tasks. The present study investigated the effects of load handling position on trunk biomechanics (flexion angle, L5/S1 joint moment and compression force) during sudden loading. Eleven subjects were exposed to a 6.8 kg sudden loading while standing upright, facing forward and holding load at three different vertical heights in the sagittal plane or 45° left to the sagittal plane (created by arm rotation). Results showed that the increase of load holding height significantly elevated the peak L5/S1 joint compression force and reduced the magnitude of trunk flexion. Further, experiencing sudden loading from an asymmetric direction resulted in significantly smaller peak L5/S1 joint compression force, trunk flexion angle and L5/S1 joint moment than a symmetric posture. These findings suggest that handling loads in a lower position could work as a protective strategy during sudden loading.     

Stair Climber Smart Mobile Robot (MSRox)

Most current mobile robots are designed to determine their actions according to their positions. Before making a decision, they need to localize themselves. Thus, their observation strategies are mainly for self-localization. However, observation strategies should not only be for self-localization but also for decision making. We propose an observation strategy that enables a mobile robot to make a decision. It enables a robot equipped with a limited viewing angle camera to make decisions without self-localization. A robot can make a decision based on a decision tree and on prediction trees of observations constructed from its experiences. The trees are constructed based on an information criterion for the action decision, not for self-localization or state estimation. The experimental results with a four legged robot are shown and discussed.     

Synthesis of interdigital capacitors based on particle swarm optimization and artificial neural networks

This article reports on the use of the particle swarm optimization (PSO) algorithm in the synthesis of the planar interdigital capacitor (IDC). The PSO algorithm is used to optimize the geometry parameters of the IDC in order to obtain a certain capacitance value. The capacitance value of the IDC is evaluated using an artificial neural network (ANN) model with the geometry parameters of the IDC as its inputs. Several design examples are presented that illustrate the use of the PSO algorithm, and the design goal in each example is easily achieved. Full‐wave electromagnetic simulations are also performed for some of the studied IDC structures implemented using coplanar waveguide (CPW) technology. The simulation results are in good agreement with those obtained using the ANN/PSO algorithm. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Reaction Pathways of Nanocomposite Synthesized in-situ from Mechanical Activated Al–C–TiO<Subscript>2</Subscript> Powder Mixture

In-situ Al matrix composite was synthesized from Al–TiO₂–C powder mixtures using mechanical alloying and heat treatment, subsequently. The effect of ball milling on reaction processes of the resulting nanocomposite was investigated. The evaluation of powder mixture without mechanical activation showed that at 900°C aluminum reduced TiO₂, forming Al₃Ti and Al₂O₃. After 20 h mechanical activation of powder mixture, Al₃Ti and Al₂O₃ were fabricated. After that, by increasing milling time up to 30 h, no new phases formed. The DTA analysis of 30 h milled powder indicated two peaks after aluminum melting at 730 and 900°C. The XRD results confirmed that at 730°C, molten Al reacted with TiO₂ and C, forming Al₃Ti, Al₂O₃ and Al₄C₃. After that, at 900°C, Al₃Ti reacted with Al₄C₃, causing TiC formation. This results proposed that the TiC formation is associated by a series of reactions between intermediate products, Al₃Ti and Al₄C₃ and the resultant nanocomposite was successfully synthesized after 30 h milling and heated by DTA analysis up to 1200°C.     

Transformation and Deformation Texture Study in Friction Stir Processed API X80 Pipeline Steel

The nature of deformation in friction stir welding/processing (FSW/P) is complex which is further complicated when allotropic phase transformations are present. Electron backscattered diffraction (EBSD) is used as a means to reconstruct prior austenite texture and grain structure to study deformation and recrystallization in austenite and ferrite in FSW/P of high strength low alloy (HSLA) steels. Analyses show evidence of shear deformation textures such as A1* (111)[?1?12], B (1?12)[110], and ?B (?11?2)[?1?10], as well as rotated-cube recrystallization texture in the reconstructed prior austenite. Existence of rotated-cube texture as well as polygonal grain structure of the prior austenite implies that recrystallization is partially occurring in elevated temperatures. Room temperature ferrite exhibits well-defined shear deformation texture components. The observed shear deformation texture in the room temperature microstructure implies that FSW/P imposes deformation during the phase transformation. The evolution of both elevated and room temperature textures in friction stir processed API X80 steel are presented.     

A Comparative Analysis of Single and Two-Phase Models of Turbulent Convective Heat Transfer in a Tube for TiO2 Nanofluid with CFD

In this article, turbulent forced convection flow of dilute water/TiO₂ nanofluid with a particle diameter equal to 30 nm in a horizontal circular tube exposed to convection with saturated steam at the wall, is numerically analyzed. Two different approaches are taken into consideration: single and two-phase mixture models. It is comprehended that the convective heat transfer coefficient increases with the particle volume concentration and Reynolds number. On the other hand, the mixture model was in better agreement with experimental data than the single phase approach.     

Optimization of the Thermal Efficiency of a Two-Phase Closed Thermosyphon using Active Learning on the Human Algorithm Interaction

Here, pristine and functionalized multi-walled carbon nanotubes with silver/water nanofluids were first synthesized. To investigate thermal performance of two-phase closed thermosyphon, thermal efficiency was experimentally obtained as a key parameter. To obtain optimal points in operational condition, the active learning method was employed in concentration ranges of 0–1 wt% as well as input power of 30–150 W which cannot be accessed. The active learning method is based on the fuzzy logic rules; here query synthesis and the measure human algorithm interaction (HAI) were used for learning. As the primary data obtained from experiments is small, this method was used to suggest the most optimal conditions. First, primary data obtained from experiments are given to the algorithm and then algorithm proposed some suggestions based on the maximum uncertainty. Subsequently, thermal efficiency is estimated based on fuzzy inference. Here, two mechanisms are employed as combined ones. The mentioned suggestions will be tested in the offered operational conditions. If the accuracy of the suggestions was confirmed by the obtained data, these data would be added to the primary ones. In fact, the used method can be considered in the area of HAI performing with the aid of experienced human simultaneously with intelligent algorithms. Meanwhile, the best working concentration to obtain the most optimum thermal efficiency obtained in the range 0.90 to 0.95 for both nanofluids.     

Dryland river regime shifts in Iran: Drivers and feedbacks

In the Anthropocene, human activities have created unprecedented changes and nonlinear relationships between humans and nature. These changes can be much faster and more intense in arid and semiarid areas that have been affected by intense human activities. Iran has climates from very humid to very dry, but arid and semiarid climates cover the country's largest area. Many of these arid areas have undergone severe changes in their surface and groundwater ecosystems in recent years, which have caused severe damage to humans and the environment in the area and surrounding areas. Therefore, in this study, using the theory of regime shifts, the time series of the Zayandeh-Rud River Basin in the center of Iran were analyzed. First, the data of the desired time series in the period of 1986–2018 was arranged seasonally. Then, using the sequential t-test method, regime shifts in these time series were identified, and then, causal loop diagrams of these shifts and their drivers and feedbacks were interpreted. The results showed that in the time series of quantity and quality of surface water and groundwater level in the studied stations and aquifers, regime shifts can be identified. Regime shifts were also identified in the time series of agricultural land area. These shifts have occurred with the increase in human activities since the early 1950s in the metropolis of Isfahan, the increase in agricultural and industrial exploitation, and consequently, the increase in population. When this reinforcing feedback loop becomes dominant, the Zayandeh-Rud River system has shifted from a regime of rich water resources to a regime of poor water resources. However, by recognizing and systematically analyzing these shifts, the Zayandeh-Rud River system can be directed toward a sustainable system through structural reform, negotiation, and redefining goals.     

Observability Under Sampling for Nonlinear Systems

A previous article has investigated the problem of the preservation of the observability under sampling in the framework of compact manifolds. A natural generalization of the cited result would be to extend it to the systems globally Lipschitzian defined on non compact manifolds. A simple representative of this class of systems is constituted by the set of bilinear systems, for this particular class of systems the preservation of the property of observability has been shown in a previous work. In this paper, we deal with the preservation of the observability under sampling for globally Lipschitzian systems defined on R ⁿ.     

Black-, gray-, and white-box modeling of biogas production rate from a real-scale anaerobic sludge digestion system in a biological and advanced biological treatment plant

Neural Computing and Applications - Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling...