A Hierarchical Model of Supply-Chain Integration: Information Sharing and Operational Interdependence in the US Grocery Channel

This paper examines costs of and motivations for interconnectivity within the grocery supply chain, employing evidence from multiple case studies and survey data to develop a seven-level model of technology-enabled supply-chain connectivity and channel interdependence. This theoretical model, built around a modified transactions-costs framework, is illustrated using examples of processes that span multiple levels of interconnectivity and interdependence within the grocery channel between different groups of customers and suppliers. Our analysis suggests that while a discernible hierarchy of levels of IT-enabled interorganizational connectivity exists, not all relationships necessarily evolve to the highest level of virtual integration. Indeed, limits on executive attention preclude this level from being achieved by more than a small fraction of trading partners. The model generates eight testable hypotheses for further study.     

Constant-life diagram modified for notch plasticity

A modified constant-life Haigh diagram has been formulated to account for plasticity occurring at stress concentrations under cyclic loading at sufficiently high stress ratios. The notch plasticity is assumed to occur within a range of elastic stress concentration factors, k_t, and cycles to failure, N_f, such that plastic straining occurs only during the first cycle of constant-amplitude cycling and straining remains elastic thereafter. This condition is expected to occur in high cycle fatigue at stress concentrations of moderate k_t loaded at high stress ratio. The validity of the model is assessed by means of fatigue data from Ti–6Al–4V notched specimens having a range of k_t. The model, purposely kept simple for ease of use as a design tool, is found to capture trends in the fatigue data not predicted using traditional straight-line models on a mean stress versus alternating stress constant-life plot.     

Variations of Time of Concentration Estimates Using NRCS Velocity Method

Time of concentration (Tc) is the time required for runoff to travel from the hydraulically most distant point to the outlet of a watershed. The Natural Resources Conservation Service (NRCS) velocity method commonly is used to estimate Tc for hydrologic analysis and design. The NRCS velocity method applies the physical concept that travel time is a function of runoff flow length and flow velocity. Time of concentration for 96 Texas watersheds is independently estimated by three research teams using the NRCS velocity method. Drainage areas of the 96 watersheds considered in the study are approximately 0.8–440.3?km2 (0.3–170?mi2). Digital elevation models having a grid size of 30?m were used to derive watershed physical characteristics using ArcGIS or HEC-GeoHMS. Average channel width was estimated from 1?m or 1?ft digital orthoimagery quarter quadrangle or aerial photography. Each team made independent decisions to estimate parameters needed for different flow segments for the NRCS velocity method. Estimates of time of concentration made by three research teams are compared, and both graphic comparison and statistical summary demonstrate that time of concentration estimated using the NRCS velocity method is subject to large variation, dependent on the analyst-derived parameters used to estimate flow velocity. Because of the propensity for different analysts to arrive at different results, caution is required in application of the NRCS velocity method to estimate Tc.     

Learning networks for tornado detection

In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and minimax probability machines (MPMs) are applied for tornado detection. The last two approaches utilize kernel methods to address non-linearity of the data in the input space. All methods are applied to detect when tornadoes occur, using variables based on radar derived velocity data and month number. Computational results indicate that BNNs are more accurate for tornado detection over a suite of forecast evaluation indices.     

Support vector machines for spatiotemporal tornado prediction

The use of support vector machines (SVMs) for predicting the location and time of tornadoes is presented. In this paper, we extend the work by Lakshmanan et al. (Proceedings of 2005 IEEE international joint conference on neural networks (Montreal, Canada), 3, 2005a, 1642–1647) to use a set of 33 storm days and introduce some variations that improve the results. The goal is to estimate the probability of a tornado event at a particular spatial location within a given time window. We utilize a least-squares methodology to estimate shear, quality control of radar reflectivity, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and SVM classification to generate the final spatiotemporal probability field. On the independent test set, this method achieves a Heidke's skill score of 0.60 and a critical success index of 0.45.     

N‐Arylsulfonyl Indolines as Retinoic Acid Receptor‐Related Orphan Receptor γ (RORγ) Agonists

The nuclear retinoic acid receptor‐related orphan receptor γ (RORγ; NR1F3) is a key regulator of inflammatory gene programs involved in T helper 17 (T_H17) cell proliferation. As such, synthetic small‐molecule repressors (inverse agonists) targeting RORγ have been extensively studied for their potential as therapeutic agents for various autoimmune diseases. Alternatively, enhancing T_H17 cell proliferation through activation (agonism) of RORγ may boost an immune response, thereby offering a potentially new approach in cancer immunotherapy. Herein we describe the development of N‐arylsulfonyl indolines as RORγ agonists. Structure–activity studies reveal a critical linker region in these molecules as the major determinant for agonism. Hydrogen/deuterium exchange coupled to mass spectrometry (HDX‐MS) analysis of RORγ–ligand complexes help rationalize the observed results.     

<Emphasis Type="Italic">HumanPT</Emphasis>: An Open-source,HumanPT Architecture-based,Robotic Application for Low Cost Robotic Tasks

In this paper, first HumanPT architecture for low cost robotic applications is presented. HumanPT architecture differs than other architectures because it is implemented on existing robotic systems (robot  robotic controller) and exploits the minimum communication facilities for real-time control that these systems provide. It is based on well-known communication methods like serial communication (USB, RS232, IEEE-1394) and windows sockets (server–client model) and permits an important number of different type of components like actuators, sensors and particularly vision systems to be connected in a robotic system. The operating system (OS) used is Microsoft Windows, the most widely spread OS. The proposed architecture exploits features of this OS that is not a real-time one, to ensure – in case that the robotic system provide such a facility – control and real time communication with the robotic system controller and to integrate by means of sensors and actuators an important number of robotic tasks and procedures. As implementation of this architecture, HumanPT robotic application and experimental results concerning its performance and its implementation in real tasks are provided. HumanPT robotic application, developed in Visual C++, is an integrated, but simultaneously an open-source software that can be adapted in different types of robotic systems. An important number of robotic tasks or procedures including sensors and particularly vision systems can be generated and executed. Small enterprises by means of the proposed architecture and the open source software can be automated at low cost enhancing in this way their production.     

Conceptual design of a two-step solar hydrogen thermochemical cycle with thermal storage in a reaction intermediate

This paper presents the conceptual design for a two-step thermochemical cycle producing hydrogen continuously, even off-sun, with the concentrated solar energy as the heat source. For a case study, the two-step iron oxide cycle (Fe₃O₄/FeO) is selected to illustrate the design concept. Two reactors, one storage tank and the solar collector comprise the system. Molten wustite (FeO) is accumulated in the storage tank on-sun. The FeO is not only involved in the reactions but also acts as the heat transfer medium, obtaining the energy from the solar insolation and delivering energy to support the thermal decomposition of magnetite (Fe₃O₄). In this way, the temperature limitation (<800 K) of molten salt is solved, and the intermittency problem of variable insolation is circumvented. A simple feedback scheme is used to control the flow rate between the storage tank and the reactors in order to minimize the temperature fluctuations. For the wustite hydrolysis reaction, the volumetric flow rate of water is regulated to control the temperature in the reactor. We derived the kinetics of the two-step iron oxide cycle from previous experimental reports. We simulated the dynamics of the system over 50 days with mass and energy balances. The simulation results show that the storage tank temperature will be stationary at 2250 K. After five days, the decomposition temperature at 2100 K, and the hydrogen production stabilized at 7 kg/min. Admitting the difficulty of high temperature operation, this design is still promising due to the high efficiency of two-step cycle itself, the process intensification of the FeO acting as the reactant/product/heat transfer medium (no need of heat exchangers), and the continuous operation/production of hydrogen.     

Parallel-access memory management using fast-fits

The two most common approaches to managing shared-access memory-free lists and buddy systems-have significant drawbacks. Free list algorithms have poor memory access characteristics, and buddy systems utilize their space inefficiently. In this paper, we present an alternative approach to parallel-access memory management based on the fast-fits algorithm. A fast-fits memory manager stores free blocks in a tree structure, providing fast access and efficient space use. Since the fast-fits algorithm accesses fewer blocks than a free list algorithm, it reduces the amount of cache invalidation overhead due to the memory manager. Our performance experiments show that the parallel-access fast-fits memory manager allows significantly greater access rates than a serial-access fast-fits memory manager does. We note that shared-memory multiprocessor systems need efficient dynamic storage allocators, both for system purposes and to support parallel programs.