The effectiveness of a goal-setting model on behavioural change

To evaluate the effectiveness of a goal-setting model on behavioural change, thirty nine adults between the ages of23 and 73 years who were in a weight loss program were assigned to one oftwo groups. One group was taught to change eating behaviour using a weight-reducing diet. The other group was taught to use a goal-setting model to change behaviour. Pretest and posttest surveys were completed by all participants, and a callback survey by theexperimentals. The PET Type Check and Kolb's Learning Style Inventory were administered to all participants. As well, five ofthe experimentals were interviewed. Results of qualitative analyses showed no significant difference between the two groups, but qualitative research suggested that experimentals were more likely to use the goal-setting model to make behavioural changes, and that being successful increased their self-efficacy.

Analysis of Some Single and Two Commodity Inventory Problems

This thesis is devoted to the study of some stochastic models in inventories. An inventory system is a facility at which items of materials are stocked. In order to promote smooth and efficient running of business, and to provide adequate service to the customers, an inventory materials is essential for any enterprise. When uncertainty is present, inventories are used as a protection against risk of stock out. It is advantageous to procure the item before it is needed at a lower marginal cost. Again, by bulk purchasing, the advantage of price discounts can be availed. All these contribute to the formation of inventory. Maintaining inventories is a major expenditure for any organization. For each inventory, the fundamental question is how much new stock should be ordered and when should the orders are replaced. In the present study, considered several models for single and two commodity stochastic inventory problems. The thesis discusses two models. In the first model, examined the case in which the time elapsed between two consecutive demand points are independent and identically distributed with common distribution function F(.) with mean  (assumed finite) and in which demand magnitude depends only on the time elapsed since the previous demand epoch. The time between disasters has an exponential distribution with parameter . In Model II, the inter arrival time of disasters have general distribution (F.) with mean  ( ) and the quantity destructed depends on the time elapsed between disasters. Demands form compound poison processes with inter arrival times of demands having mean 1/. It deals with linearly correlated bulk demand two Commodity inventory problem, where each arrival demands a random number of items of each commodity C1 and C2, the maximum quantity demanded being a (< S1) and b(

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: the Infinite Horizon Case

We analyze an infinite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average profit over the infinite planning horizon. We show that a stationary (s,S,p) policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period.

Implementation of global make to inventory strategy at BASF SE

La optimización y armonización son factores clave para tener un buen desempeño en la industria química. BASF ha desarrollado un proyecto llamada acelerador. El objetivo de este proyecto ha sido la armonización y la integración de los procesos de la cadena de suministro a nivel mundial. El proceso básico de manejo de inventarios se quedó fuera del proyecto y debía ser analizado. El departamento de manejo de inventarios en BASF SE ha estado desarrollando su propia estrategia para la definición de procesos globales de manufactura. En este trabajo se presentará un informe de las fases de la formulación de la estrategia y establecer algunas pautas para la fase de implementación que está teniendo lugar en 2012 y 2013.

Psychometric Properties of the Inventário Dimensional Clínico da Personalidade (IDCP) using the Rating Scale Model

The aim of this study was to evaluate the performance of the Dimensional Clinical Personality Inventory (DCPI) using Rasch-based person and item analysis. 1281 participants were recruited, between 18 and 90 years of age (M=26.64; SD=8.94), 431 men (33.6%) and 127 (9.9%) patients diagnosed with axis I disorders and/or axis II according to DSM-IV-TR. Results indicated the IDCP scales performed reasonably well, and the usefulness of the analyses presented, demonstrate the Rasch model’s applicability for clinical applications. Among the important tools offered by the Rasch model, we explore the use of the person-item map, which visually presents the intuitively understandable psychological construct along the dimensional scale of the instrument.

Glacier shrinkage and climatic change in the Russian Altai from the mid-20th century: An assessment using remote sensing and PRECIS regional climate model

This paper examines changes in the surface area of glaciers in the North and South Chuya Ridges, Altai Mountains in 1952-2004 and their links with regional climatic variations. The glacier surface areas for 2004 were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Data from the World Glacier Inventory (WGI)dating to 1952 and aerial photographs from 1952 were used to estimate the changes. 256 glaciers with a combined area of 253±5.1 km2 have been identified in the region in 2004. Estimation of changes in extent of 126 glaciers with the individual areas not less than 0.5 km2 in 1952 revealed a 19.7±5.8% reduction. The observed glacier retreat is primarily driven by an increase in summer temperatures since the 1980s when air temperatures were increasing at a rate of 0.10 - 0.13oC a-1 at the glacier tongue elevation. The regional climate projections for A2 and B2 CO2 emission scenarios developed using PRECIS regional climate model indicate that summer temperatures will increase in the Altai in 2071-2100 by 6-7oC and 3-5oC respectively in comparison with 1961-1990 while annual precipitation will increase by 15% and 5%. The length of the ablation season will extend from June-August to the late April – early October. The projected increases in precipitation will not compensate for the projected warming and glaciers will continue to retreat in the 21st century under both B2 and A2 scenarios.

Constraining a North Atlantic Ocean general circulation model with chlorofluorocarbon observations

Measurements of anthropogenic tracers such as chlorofluorocarbons and tritium must be quantitatively combined with ocean general circulation models as a component of systematic model development. The authors have developed and tested an inverse method, using a Green's function, to constrain general circulation models with transient tracer data. Using this method chlorofluorocarbon-11 and -12 (CFC-11 and -12) observations are combined with a North Atlantic configuration of the Miami Isopycnic Coordinate Ocean Model with 4/3 degrees resolution. Systematic differences can be seen between the observed CFC concentrations and prior CFC fields simulated by the model. These differences are reduced by the inversion, which determines the optimal gas transfer across the air-sea interface, accounting for uncertainties in the tracer observations. After including the effects of unresolved variability in the CFC fields, the model is found to be inconsistent with the observations because the model/data misfit slightly exceeds the error estimates. By excluding observations in waters ventilated north of the Greenland-Scotland ridge (sigma (0) < 27.82 kg m(-3); shallower than about 2000 m), the fit is improved, indicating that the Nordic overflows are poorly represented in the model. Some systematic differences in the model/data residuals remain and are related, in part, to excessively deep model ventilation near Rockall and deficient ventilation in the main thermocline of the eastern subtropical gyre. Nevertheless, there do not appear to be gross errors in the basin-scale model circulation. Analysis of the CFC inventory using the constrained model suggests that the North Atlantic Ocean shallower than about 2000 m was near 20% saturated in the mid-1990s. Overall, this basin is a sink to 22% of the total atmosphere-to-ocean CFC-11 flux-twice the global average value. The average water mass formation rates over the CFC transient are 7.0 and 6.0 Sv (Sv = 10(6) m(3) s(-1)) for subtropical mode water and subpolar mode water, respectively.

Results of an aqueous source term model for a radiological risk assessment of the Drigg LLW Site, U.K.

A radionuclide source term model has been developed which simulates the biogeochemical evolution of the Drigg low level waste (LLW) disposal site. The DRINK (DRIgg Near field Kinetic) model provides data regarding radionuclide concentrations in groundwater over a period of 100,000 years, which are used as input to assessment calculations for a groundwater pathway. The DRINK model also provides input to human intrusion and gaseous assessment calculations through simulation of the solid radionuclide inventory. These calculations are being used to support the Drigg post closure safety case. The DRINK model considers the coupled interaction of the effects of fluid flow, microbiology, corrosion, chemical reaction, sorption and radioactive decay. It represents the first direct use of a mechanistic reaction-transport model in risk assessment calculations.

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

INVENTORY CONTROL SYSTEM : Optimization of production system and reliability

The main idea of this research to solve the problem of inventory management for the paper industry SPM PVT limited. The aim of this research was to find a methodology by which the inventory of raw material could be kept at minimum level by means of buffer stock level.The main objective then lies in finding the minimum level of buffer stock according to daily consumption of raw material, finding the Economic Order Quantity (EOQ) reorders point and how much order will be placed in a year to control the shortage of raw material.In this project, we discuss continuous review model (Deterministic EOQ models) that includes the probabilistic demand directly in the formulation. According to the formula, we see the reorder point and the order up to model. The problem was tackled mathematically as well as simulation modeling was used where mathematically tractable solution was not possible.The simulation modeling was done by Awesim software for developing the simulation network. This simulation network has the ability to predict the buffer stock level based on variable consumption of raw material and lead-time. The data collection for this simulation network is taken from the industrial engineering personnel and the departmental studies of the concerned factory. At the end, we find the optimum level of order quantity, reorder point and order days.

A METHODOLOGY TO EVALUATE OBSOLETE INVENTORY IN HEALTH CARE

Many organizations are currently facing inventory management problems such as distributing inventory on-time and maintain the correct inventory levels to satisfy the customer or end users. Organizations understand the need for maintaining the accurate inventory levels but sometimes fall short leading a wide performance gap in maintaining inventory accurately. The inventory inaccuracy can consume much of the investment on purchasing inventory and many times leads to excessive inventory. The research objective of thesis is to provide a decision making criteria to the management for closing or maintaining the warehouse based on basic purchasing and holding cost information. The specific objectives provide information regarding the impact of inventory carrying cost, obsolete inventory, inventory turns. The methodology section explains about the carrying cost ratio that would help inventory managers to adopt best practices to avoid obsolete inventory and also reduce excessive inventory levels. The research model was helpful in providing a decision making criteria based on the performance metric developed. This research model and performance metric had been validated by analysis of warehouse data and results indicated a shift from two-echelon inventory supply chain to a one-echelon or Just In Time (JIT) based inventory supply chain. The recommendations from the case study were used by a health care organization to reorganize the supply chain resulting in the reduction of excessive inventory.

Working Alliance Inventory-Short Revised (WAI-SR): psychometric properties in outpatients and inpatients

The Working Alliance Inventory-Short Revised (WAI-SR) is a recently refined measure of the therapeutic alliance that assesses three key aspects of the therapeutic alliance: (a) agreement on the tasks of therapy, (b) agreement on the goals of therapy and (c) development of an affective bond. The WAI-SR demonstrated good psychometric properties in an initial validation in psychotherapy outpatients in the USA. The generalizability of these findings is limited because in some countries a substantial portion of individual psychotherapy is delivered in inpatient settings. This study investigated and compared the psychometric properties of the WAI-SR in German outpatients (N = 88) and inpatients (N = 243). In both samples reliability (alpha > 0.80) and convergent validity with the Helping Alliance Questionnaire were good (r > 0.64). Confirmatory factor analysis showed acceptable to good model fit for the proposed Bond-Task-Goal model in both samples. Multi-group analysis demonstrated that the same constructs were measured across settings. Alliance ratings of outpatients and inpatients differed regarding the overlap of alliance components and the magnitude of the alliance ratings: The differentiation of the alliance components was poorer in inpatients and they reported lower alliances. Unique aspects of the alliance in inpatient treatment are discussed and a need for further research on the alliance in inpatient settings is pointed out. Overall, the WAI-SR can be recommended for alliance assessment in both settings.

Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.