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This paper proposes an energy-aware production schedule model for flexible manufacturing systems (FMSs) that aims to minimise energy costs and carbon tax while considering rising energy costs and environmental policies motivated by climate change concerns. The model is based on a sequence-dependent five-machine FMS scheduling problem, which is flexible in levels of parts, tools, machines, and routings. The proposed model is solved as a multi-objective problem, with tardiness and energy consumption as primary goals and carbon footprint reduction policies incorporated into the framework as control strategies. The machine ON/OFF strategy is also introduced to reduce idle energy. The study analyses the trade-off between minimising tardiness and carbon emissions to achieve both service level and environmental sustainability of the FMS. The proposed model reduced total energy consumption and carbon emissions by 20% and 21%, respectively, without compromising manufacturing deadlines. Future research may explore the integration of renewable energy sources and storage systems, considering more complex manufacturing scenarios, such as stochastic demands, machine failures, and workforce constraints. [ABSTRACT FROM AUTHOR]

The global manufacturing sector is currently subject to Time-of-Use energy pricing, compelling them to avoid peak demand. Overuse of energy by industries can lead to the utilisation of thermal power plants, which can have a severe influence on the environment. Energy-efficient production scheduling can help manufacturers to reduce energy costs. This paper focuses on the energy efficiency and production output of a flexible manufacturing system (FMS) consisting of five machines and operates based on sequence-dependent scheduling. A centralised energy storage system is considered in the model to improve the voltage profile during peak load hours and lower peak load demand. A simple and effective strategy is proposed for peak load shaving via real-time scheduling of the FMS. The scheduling problem is treated as a multi-objective issue and tackled using a whale optimisation algorithm. The optimisation results demonstrate the effectiveness of the proposed model in reducing peak demand from the energy supplier side to zero by utilising the energy storage system while also maintaining an acceptable makespan. The model can help energy-intensive industries reduce peak demand by moving load to an energy storage system. By adopting these measures, industrial facilities can reduce their carbon footprint and contribute to the global effort to mitigate climate change. Highlights: The study focuses on a flexible manufacturing system's energy efficiency and production output. The study uses a centralised energy storage system to improve the voltage profile. The whale optimisation algorithm is used to solve the multi-objective problem. [ABSTRACT FROM AUTHOR]

Metal Additive Manufacturing (MAM) produces high-strength and complex shape components through a layer-by-layer metal deposition approach for aerospace, automobile, and structural applications. The heat is accumulated and subjected to reheating in the metal layers by depositing material from the feedstock with the help of a heat source on the existing metal layer. Generally, the reheating in the MAM process consists of multiple annealing, multilevel normalizing, and long tempering processes. The material morphology and mechanical properties of MAM products are affected due to the stored heat in the layers while the component is manufactured with the heat source of laser energy, electron energy, or conventional arc welding techniques. The grain structure is refined by reheating the layers by the heat source and without post-heat treatment techniques. The mechanical properties and morphological characteristics of the as-built MAM components due to the effect of in-situ intrinsic heat treatment (reheating) are measured through various materials testing methods such as Electron Back Scatter Diffraction (EBSD), Scanning electron microscopy (SEM), X-ray diffraction (XRD), tensile test, hardness test, compressive test and etc. The review aims to study the mechanical and morphological characteristics due to the effect of in-situ intrinsic heat treatments on low carbon steel, maraging steel, and Al alloys, which were fabricated by direct energy deposition (DED) and laser powder bed fusion (LPBF) techniques. [ABSTRACT FROM AUTHOR]

Background: In spite of the significant progress in modern medicine, viruses continue to be a formidable challenge to human health. The use of plants for the remediation of viral-borne diseases stretches back to the very dawn of mankind. Among bulbous plants, the Amaryllidaceae is one of the most popular families exploited in the traditional remediation of infectious diseases. Methods: This account details the findings from a literature search carried out on the antiviral properties of the Amaryllidaceae. The keyword engaged in the search was "antiviral" in combination with the words "Amaryllidaceae," "Amaryllidaceae specie," and "Amaryllidaceae alkaloid." Results: Thirty-six taxa from 13 countries, notably in Africa and parts of Asia, have been cited as traditional remedies for viral diseases. Alcoholic bulb extracts of 18 species were evaluated against 23 different pathogens from 13 viral families. A wide range of activities was observed, with the whole-plant methanol extract of Zephyranthes candida seen to be the most striking (IC50 0.0019 µg/mL against poliovirus). The active principles in the main were isoquinoline alkaloids, of which lycorine impressed against the Avian influenza virus (strain H5N1). The mechanisms underlying the antiviral effects were seen to be related to the inhibition of DNA, RNA, and protein synthesis as well as inhibitory effects toward reverse transcriptase and protease enzymes. Conclusion: Amaryllidaceae provides a richly diverse platform for antiviral drug research. Such endeavors have been fortified by the significant amounts of information emerging from indigenous knowledge systems. Ongoing studies will continue to target the active entities, particularly from taxa with verifiable ethnomedicinal backgrounds. [ABSTRACT FROM AUTHOR]

Understory plants are often inadequately represented or neglected within analyses of forest ecosystem productivity. Further, the potential impacts of the biological factors of age class and growth form on carbon cycling physiology, and how it may vary across the growing season and amongst species of different native/non-native status, have not been thoroughly considered. Our study examines photosynthesis and associated physical leaf traits in two understory woody species, Rhamnus cathartica , introduced and invasive in North America, and Prunus serotina , a common subcanopy species native to North America. We estimated leaf-level photosynthesis as measured through light and carbon dioxide response curves, dark-adapted chlorophyll fluorescence and leaf traits (leaf mass per area and stomatal density) for each combination of species and age class at plots in the understory of a temperate deciduous research forest in the US Upper Midwest at two time points during the growing season, late spring (late May) and mid-summer (mid-July). Carbon assimilation rates from light response curves (A sat, A 400) and fluorescence capacity estimate F v/ F m all increased between the two measurement points in both species and age class. Estimates of carbon reaction capacity (V cmax and J max) exhibited a different directional response to seasonal development, declining in seedlings of both species and P. serotina trees (~8–37 % reduction in V cmax, ~9–34 % reduction in J max), though increased in trees of R. cathartica (+24 % in V cmax, +9 % in J max). Divergent responses in photosynthetic parameters amongst these factors may be explained by species differences in leaf mass per area and stomatal density, which together are likely influenced by both growth form, canopy position and ontogeny. Overall, we believe our findings suggest complex, varied influences on photosynthesis that indicate environmental and biological plasticity which may contribute to the historic and continued expansion of R. cathartica in the US Upper Midwest region. [ABSTRACT FROM AUTHOR]

An attempt has been made to develop a generalised system for the generation of process plan for the manufacture of micro parts in this work. It has two components; first one deals with automatic part feature extraction from feature-based model and second one performs the execution of process planning activities in accordance with feature using knowledge-based system approach. The proposed system maps extensible markup language (XML) data for the feature based-model and produces the corresponding manufacturing activities needed for the manufacture of micro parts. The process plan modules considered in this work includes process sequence, tools and fixtures, process parameters selection and set-up plan generation. An attempt has been made to develop process parameters selection module based on experimental investigation and optimisation apart from manufacturing catalogues and user manuals. Feature extraction through XML files avoids complex feature extraction process. The application of the developed system has been verified with a case study. The present system is limited to micro turn-mill features. Incorporation of more micro features and consideration of other activities of process plan ensures a complete process planning system for micro-machining processes. [ABSTRACT FROM PUBLISHER]

The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience. [ABSTRACT FROM AUTHOR]

Accurate prediction of the deviations/deformations is highly necessary for the new product development process. It is a tedious multi-tasking activity, in which various aspects need to be considered in the early phase of design. This paper proposes a new constitutive and collaborative framework to model the functional assembly geometry. For complex industrial applications in the multi-plant scenario, several departments work together for a common goal. Often the department's goal is different and cannot achieve the feat of right for the first time. Hence this paper aimed to integrate the vital departments of the manufacturing industry. Initially, the functional assembly was predicted through Finite Element Analysis (FEA). A multi-layer perceptron type (MLP) artificial neural network was employed to learn the FEA behavior of the assembly. Further, the assembly prototype was practically tested to validate the FEA results, and the obtained data were used to verify the MLP network model. The best trained and tested network model was simulated to predict the near-net geometry considering the functional behavior of the assembly with external loads. The proposed method provides affirmative knowledge while integrating the finite element analysis, testing methods, and neural networks. [ABSTRACT FROM AUTHOR]

Increasingly, undergraduate institutions are incorporating original research into the curriculum as a matter of best practice. However, while the practice of science has grown more collaborative, undergraduate research has remained largely confined to single-institution studies. Incorporating long-term, distributed research projects into the undergraduate research experience can better prepare students to interpret and engage in science of the future. The Decomposition in Aquatic and Terrestrial Invaded Systems (DATIS) project within the Ecological Research as Education Network (EREN) offers a good model for examining how to minimize challenges and maximize opportunities associated with classroom use of long-term, collaborative research projects. Eleven key challenges are identified, and practical solutions are provided for each. By modeling this scientific approach in primarily undergraduate institutions, we are preparing graduates who will have the tools and knowledge to work collaboratively and create their own distributed research networks. Our goal is that the decomposition project we describe here can inform and inspire others seeking to engage in research at the undergraduate level, either as potential research coordinators or as collaborators in an existing network. [ABSTRACT FROM AUTHOR]

Purpose: Due to the economic benefits and environmental awareness, most of the battery manufacturing industries in India are interested to redesign their existing supply chain network or to incorporate the effective closed loop supply chain network (CLSCN). The purpose of this paper is to develop CLSCN model with eco-friendly distribution network and also enhance recycling to utilize recycled lead for new battery production. The existing CLSCN model of a battery manufacturing industry considered for case study is customized for attaining economic benefit and environmental safety. Hence, single objective, multi-echelon, multi-period and multi-product CLSCN model with centralized depots (CD) is developed in this work to maximize the profit and reduce the emission of CO2 in transportation. Design/methodology/approach: The proposed CD has the facility to store new batteries (NB), scrap batteries (SB) and lead ingot. The objective of the proposed research work is to identify potential location of CD using K-means clustering algorithm, to allocate facilities with CD using multi-facility allocation (MFA) algorithm and to minimize overall travel distance by allowing bidirectional flow of materials and products between facilities. The proposed eco-friendly CLSCN-CD model is solved using GAMS 23.5 for optimal solutions. Findings: The performance of the proposed model is validated by comparing with existing model. The evaluation reveals that the proposed model is better than the existing model. The sensitivity analysis is demonstrated with different rate of return of SB, different proportion of recycled lead and different type of vehicles, which will help the management to take appropriate decision in the context of cost savings. Originality/value: This research work has proposed single objective, multi echelon, multi period and multi product CLSCN-CD model in the battery manufacturing industry to maximize the profit and reduce the CO2 emission in transportation, by enhancing the bidirectional flow of materials/products between facilities of entire model. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of simultaneous scheduling of machines and two identical automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). For solving this problem, a new meta-heuristic differential evolution (DE) algorithm is proposed. The problem consists of two interrelated problems, scheduling of machines and scheduling of AGVs. A simultaneous scheduling of these, in order to minimise the makespan will result in a FMS being able to complete all the jobs assigned to it at the earliest time possible, thus saving resources. An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs as an integral part of the overall scheduling activity. The algorithm is tested by using problems generated by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers and analysed. [ABSTRACT FROM AUTHOR]

Traditional Geometric tolerance allocation method assumes the parts of the assembly are purely rigid. Upon assembly and function, it is realised that the rigidity of the parts is non-ideal which leads to severe variations. Hence the performance of an assembly declines and is associated with the parts performance in the assembly. An assembly always experiences deformations due to internal and external forces/loads and drop in efficiency is critically observed. This paper proposes a methodology to predict such variations in assembly and integrate geometric tolerance design to it. First, the Ontological model of the assembly is predicted through Finite Element Analysis, and the near net shape of the assembly is obtained. Second, a set of features of an assembly which plays a vital role is selected, to determine the optimal geometric tolerances through the hybridised neural network and Genetic algorithm. Finally, a gear pump assembly is chosen, the proposed method is demonstrated. This method will be useful for design and new product development engineers in reducing the assembly variations and associated manufacturing cost. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We thank Lambert and Steen ([12]) for the attention they have given to our paper (Bowne et al. [2]) and welcome productive discussions of how human activities affect turtle populations. We agree with Lambert and Steen that roads and urbanization can be and in many cases are threats to freshwater turtles, but we argue that male-biased sex ratio is not a universal result of anthropogenic activity. We also note that relative sex ratio is the standard most used in the literature on turtle sex ratio (e.g., Marchand & Litvaitis [14]; Steen & Gibbs [17]). We support any effort to improve our collective understanding of the relationship between turtle populations and urbanization by generating better population estimates, but that is different from treating relative population ratios as absolute population ratios. [Extracted from the article]

Globalization, the modern lifestyle, immuno-suppressive agents, invasive surgical procedures, the loss of efficacies of existing drugs, and multidrug resistance are some of the factors used to explain the rise in fungal infections in recent years. Significant advances have been made in attempts to replace existing antifungal schedules, especially with synthetic targets. The identification of other platforms for drug discovery is now entrenched in research programs across the globe. Plants offer significant benefits owing to their numerical superiority, exceedingly broad chemical basis and appealing sustainability characteristics. Furthermore, plants have a long and rich historical association with traditional approaches towards fungal diseases. These have in numerous instances served as markers in the bioassay-guided identification of the active constituents. Although the plant family Amaryllidaceae is conventionally associated with cancer and motor-neuron disease chemotherapies, around 30 of its species have been examined for antifungal activities with microgram per millilitre inhibitory activities detected in several instances. This review focuses on the nearly 40 constituents from the family, mainly isoquinoline alkaloids, which have been screened against around 50 fungal pathogens. Encouragingly, microgram per millilitre growth inhibitory activities were applicable for several of the compounds with a minimum inhibitory concentration of 4 μg/ml seen to be the lowest. [ABSTRACT FROM AUTHOR]

The main objective of this study is to investigate the effects of various electrode materials and their influences on electrical discharge micro-machining performance attributes. The performance attributes are determined in terms of tool wear rate (TWR), material removal rate (MRR), taper angle, overcut, and surface roughness by drilling micro-holes on SS 316 L material. It is noticed that, for high discharge energy the MRR, TWR, taper angle, and overcut are more and also when the thermal conductivity, boiling point, and melting point of the electrode material are high, the TWR is low. The surface finish is good at low discharge energy and low spindle speed. If the tool electrode is rotating at minimum speed during machining, a centrifugal effect is created on the dielectric so that debris at the inter-electrode gap is evacuated efficiently. If the tool is stationary, then the machining conditions are unstable due to improper flushing of debris. [ABSTRACT FROM PUBLISHER]

The plant family Amaryllidaceae is well-known for its unique alkaloid constituents, which exhibit a wide range of biological activities. Its representative, Amaryllis belladonna, has a geographical distribution covering mainly southern Africa, where it has significant usage in the traditional medicine of the native people. In this study, A. belladonna samples collected in Brazil were examined for alkaloid content. Alkaloid profiles of A. belladonna bulbs were generated by a combination of chromatographic, spectroscopic and spectrometric methods, including GC--MS and 2D NMR. In vitro screening against four different parasitic protozoa (Trypanosoma cruzi, T. brucei rhodesiense, Leishmania donovani and Plasmodium falciparum) was carried out using the A. belladonna crude methanol extract, as well as three of its alkaloid isolates. Twenty-six different Amaryllidaceae alkaloids were identified in the A. belladonna bulb samples, and three of them were isolated. Evidence for their respective biosynthetic pathways was afforded via their mass-spectral fragmentation data. Improved data for 1-O-acetylcaranine was provided by 2D NMR experiments, together with new 1H-NMR data for buphanamine. The crude extract and 3-O-acetylhamayne exhibited good antiprotozoal activity in vitro, although both with a high cytotoxic index. [ABSTRACT FROM AUTHOR]

This study investigates the impact of machining factors on the performance of additive mixed micro-electric discharge drilling of 316L stainless steel. The effects of three kinds of powder, powder concentration, voltage, capacitance, feed rate, and speed on tool wear rate, material removal rate, taper angle, and overcut (OC) were also investigated. The experimental results show that adding additives to the dielectric enhanced rate of material removal and reduced rate of tool wear significantly. Mixing powder with dielectric increased the length of the sparking, resulting in significant OC, and process performance also improved with the increase in amount of powder added. Further, scanning electron microscopy analysis was carried out to examine the surface characteristics and material migration properties, which confirmed that the properties of the machined surface are indeed significantly improved. [ABSTRACT FROM AUTHOR]

This paper focuses on the issues of Resistor-Capacitor-based Electrical Discharge Micro-Machining process and investigates the effects of tool speed and polarity on the performance measures such as Tool Wear Rate, Material Removal Rate, Overcut and Taper Angle by drilling on 316L Stainless Steel. Taguchi’s L54 mixed orthogonal array design is employed to conduct experiments by varying tool polarity at two levels and voltage, capacitance, spindle speed at three levels. The cause and effect relationship between the experimental factors and responses are analysed and discussed using Factorial Analysis of Variance technique. Optimum combinations of machining parameters are also evaluated using Taguchi-based Grey Relational Analysis, by considering grey relational grade matrix and influence of process parameters on the responses. Further, microscopic analysis is done to identify the micro-voids, globular formation, and cracks present on the surface of the hole produced under various machining conditions. [ABSTRACT FROM PUBLISHER]

We discuss the benefits that a collaborative research network, a group of faculty from different institutions who jointly conduct a research project, can have on undergraduate research (UR) by enhancing the diversity and significance of projects and by improving student motivation and breadth of learning. The main example used is the Ecological Research as Education Network (EREN), founded in 2010 to enhance undergraduate research in ecology at primarily undergraduate institutions (PUIs) by (1) providing networking and collaborative research opportunities for both faculty and students and (2) developing free educational resources. EREN comprises about 280 ecology faculty and staff nationally and has facilitated development of nine continental-scale, collaborative research projects. Project leaders design a research project that can be conducted by faculty and students at just about any college. Faculty and their students carry out the data collection at their home institution and submit it to the publicly available project database. Then participants (and even non-participants) can avail themselves of the large, multi-year, continent-wide data set. Substantial benefits have been reported for programs, faculty, and students. Undergraduate research programs are broadened and faculty benefit because they gain insights and laboratory techniques from colleagues in other institutions and fields, thereby expanding the diversity of potential undergraduate research projects and resulting in more broadly trained undergraduates. The research projects themselves have become educational resources incorporated into courses at all levels, as well as independent research projects. Because data collection happens nationwide, college faculty and students at small colleges are now able to investigate large-scale ecological questions. Preliminary assessments have shown significant improvements for some student-learning outcomes, including thinking across scales, use of best practices in data management, and describing scientific collaboration techniques. Students demonstrate increased motivation and retention through participation in a nationwide, authentic research project with publication-quality data, becoming part of a community of scholars and gaining a sense of belonging and responsibility. Despite challenges with coordination and communication, students are exposed to a wider range of techniques and subfields of ecology than they would be without this network. [ABSTRACT FROM AUTHOR]

A new phytochemical study of the indigenous Brazilian species Hippeastrum papilio is reported herein. Three novel Amaryllidaceae alkaloids were isolated, including hippapiline ( 1), papiline ( 2), and 3- O-demethyl-3- O-(3-hydroxybutanoyl)haemanthamine ( 3). Their structures were determined by physical and spectroscopic methods. In addition, the known alkaloids, haemanthamine ( 4), galanthamine ( 5), narwedine ( 6), 11 β-hydroxygalanthamine ( 7), apogalanthamine ( 8), and 9- O-demethyllycosinine B ( 9) were identified. The unusual cis-B/ C-ring fusion for the new homolycorine representative hippapiline was ratified by NMR and CD spectroscopy. [ABSTRACT FROM AUTHOR]

The present work deals with feature-based modelling (FBM) and process parameters selection in a computer-aided process planning (CAPP) system for prismatic micro parts. The proposed system maps the Extensible Markup Language (XML) data from the feature-based model and produces the corresponding process parameters required for micro part manufacturing. It has two components: (1) invention of FBM and automatic extraction of manufacturing feature information and (2) selection of process parameters for the given micro features using knowledge-based system (KBS) approach. An attempt has been made to develop process parameters based on experimental investigation and optimisation using genetic algorithm (GA) apart from the information from literatures and user manuals used for database development. FBM and data extraction through XML files avoid complex feature extraction and recognition processes. The application of the proposed system was verified with the case study. The present system is intended for miniature part with micro drill and mill features. Incorporation of more micro features and consideration of various other process planning activities ensures a complete CAPP system for prismatic micro parts. [ABSTRACT FROM AUTHOR]

Question To what extent are species, including native and non-native species, influencing one another's distribution and abundance in the herb layer of a Minnesota oak woodland? Location Oak woodland succeeding into a more mesic forest, on bluffland of the Mississippi River, east-central Minnesota. Methods We collected plant composition and species cover data in 182 1.0 × 0.5 m quadrats regularly spaced on a 6-ha study grid in the oak woodland. We also recorded slope, slope position, aspect, elevation and photosynthetically active radiation (PAR) at each quadrat. Results Presence and abundance of other plant species, topographic variables and light availability explained only a small portion of the variation (5-19%) in the distribution and abundance of individual species. The most common strongest predictor of cover for the ten most common species was species richness, with the association being positive. The non-native species, garlic mustard ( Alliaria petiolata) exhibited the strongest positive association with species richness. Only one of the 45 pair-wise comparisons of the ten species resulted in a negative relationship between the species. Abundance and distribution of two species were associated with topographic features, but this accounted for much less of the variation in abundance than did species richness. Conclusion We found little evidence that competition or any other interactions among common herb layer species, including the non-native Alliaria petiolata, play an important role in determining the abundance and the distribution of herb layer species in this oak woodland. Topographic factors may explain a small amount of the distribution and abundance patterns of a few species. But, for the most part, species are more likely to be present when other species are present, suggesting that they are simply establishing in microsites favourable to plants in general. [ABSTRACT FROM AUTHOR]

In Computer Aided Process Planning (CAPP), process parameters selection for the given manufacturing feature is the final activity and it is the key area for research and development. In this work, an attempt has been made to optimize parameters for micro end-milling operation as a part of CAPP system development for micromachining processes using Artificial Intelligence (AI) approach. Genetic Algorithm (GA) has been found to be the robust and efficient tool to solve nonlinear optimization problems involved in process planning. Microfeatures of size 0.7 and 1 mm are considered and polymethyl methacrylate is chosen as the work material due to its potential application in microparts fabrication. Initially, experimental investigation has been carried out to analyze the impact of process conditions such as spindle speed and feed rate on surface roughness and machining time. Further multiobjective optimization for minimization of responses is carried out using GA. Finally, confirmation experiments are carried out to validate the accuracy of GA results. The optimized process parameters are stored in the database and it ensures foolproof parameters for micro end-milling operation for CAPP applications apart from manuals and catalogues. The proposed approach can be repeated for various other end mill features and for different work and tool material combination to ensure a complete parameters selection module for CAPP system applications. [ABSTRACT FROM AUTHOR]

Technology development has led to the need of micro and miniaturized products in the field of automobile, aerospace, electronics, medical implants, biomedicine, robotics, and so on. Micromachining is the key technology to satisfy the need of the industry in terms of functionality and miniaturization in size. In this article, state-of-the-art review on tool-based micromachining processes such as microturning, microdrilling, micromilling, electric discharge micromachining (Micro-EDM), and electrochemical micromachining (Micro-ECM) has been carried out. The review begins with an overview of micromachining, classifications, and discussions about different aspects of tool-based micromachining processes. The research works carried out for the past 10 years are analyzed in terms of materials perspective, process parameters, size effects, performance characteristics, condition monitoring, product development, micro features generation, and fabrication of micro tools. A statistical analysis has been performed with respect to the previous review articles, numbers of publications in the related area, and their research gap. Finally, the possible future research direction is presented. [ABSTRACT FROM AUTHOR]

Generating employment and alleviating poverty are the biggest challenges for regional economic growth in the rural areas of the Northeastern United States. Despite the revival of the economy in much of the nation's heartland, rural areas are still suffering from high poverty and unemployment rates. Self-employment, a measure of entrepreneurship, indicates an opportunity for rural communities to improve the quality of life and accelerate regional economic development. Taking into consideration the problem of unemployment in rural communities, there is a need to focus on generating self-employment opportunities at micro level to enhance economic growth and reduce the per capita income 'gap' between rural and urban areas. The overall objective of the study is to identify and estimate the impacts of self-employment in the economic development of the Northeastern United States. The empirical model of this study is derived from the three-equation simultaneous model of Deller et al. (2001). The study estimated the relationship of employment, population and per capita income to self-employment. Research findings show that employment and population have a positive relationship to self-employment, indicating positive contribution of self-employment to regional economic development. [ABSTRACT FROM AUTHOR]

Fiber reinforced sandwich structures typically respond very poorly to transverse impact events. This paper is an attempt to investigate the impact response of sandwich panels subjected to low-velocity impact. Experimental investigations were carried out on the influence of three design factors: height of fall, core thickness, and impactor mass, which are the most relevant parameters to be considered for deflection. The study of behavior of the mentioned response was done by using Design of Experiments tool. Response surface methodology (RSM), a sequential experimentation strategy for model building, is used to model the response in order to determine the most significant factor among the influential factors. In this case, full factorial face-centered central composite design was chosen due to the number of factors and their levels in the study. The specimen consisted of face sheets made up of bi-woven glass fiber cloth with polyurethane foam as core material. The parametric analysis reveals that deflection increases steadily with an increase in the height of fall when compared with the impactor mass and the core thickness. The reason for this study is imperative for the next generation aircraft, marine, road, and rail vehicles with improved lightweight stiff materials that can absorb higher impact energy with higher resistance to deflection. [ABSTRACT FROM AUTHOR]

The suitability of applying the NRCS curve number (CN) to continuous runoff prediction is examined using a new technique of back-calculating CN values from the output of a complex nonlinear hydrologic model. The continuous watershed model Hydrologic Simulation Program-FORTRAN (HSPF) is employed as a simple theoretical watershed in two numerical procedures designed to link CNs with model parameters. The influence of soil type, soil depth, storm depth, storm distribution, and initial abstraction ratio value (λ) on the calculated CN value is investigated. For the first procedure, a soil physics model was developed to calculate the soil moisture content equivalent to the NRCS antecedent runoff condition (ARC) II based on soil type and soil depth. Additionally, the model provides estimates for HSPF soil moisture and infiltration parameters with the remaining parameters adopted from previous calibrations on real watersheds. Curve numbers are back-calculated from HSPF parameter sets using the direct runoff produced from a cyclic synthetic storm event time series input to HSPF. The second numerical method is based on an established asymptotic curve fitting approach to determining CNs from data using real time series rainfall records and simulated runoff events. It was determined that the calculated CN values demonstrated a nonlinear dependence on the computational variables listed above. While significant error was found to be associated with the CNs resulting from the first numerical procedure, the second method suggests some promise for the use of CN values as surrogates for HSPF parameter sets for long term hydrologic analysis. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of the study is to evaluate the impact of China's current coal-to-liquids (CTL) activities on its coal and oil markets from 2005 to 2025. Design/methodology/approach – A partial equilibrium multi-equation model of China's oil and coal markets is developed based on data obtained from the existing literature. The impact of CTL technologies on China's oil and coal markets is evaluated using computer simulations by solving the model under scenarios with and without CTL production. Findings – The simulation results show that on average, the planned CTL activities will decrease crude oil prices by 5.73 percent and China's oil imports by 6.09 percent and increase China's domestic oil supply by 9.26 percent over the 20 year period. Also, China's demand for oil will increase by 0.35 percent on average, suggesting that CTL production will slightly stimulate China's demand for oil because of the drop in oil prices. China's demand for coal will also increase by 1.02 percent because of the additional demand for coal created by CTL production. Surprisingly, both coal prices and China's coal supply will decline by 0.51 percent while the demand for coal and coal supply of the rest of the world will be reduced by 1.63 percent and 0.28 percent, respectively. Originality/value – The paper is the first study on the implication of CTL conversion from an economist's point of view. It applies an economic model to quantify the impacts of such technology on overall energy prices and supplies. [ABSTRACT FROM AUTHOR]

The article reports on the significance of the study of birds towards the identification of environmental pollutants in which the birds where found. The author argues that preserve birds which could be found among museums reflects significant information about the environment in which they were found. Information including feeding habits, feathers, as well as environmental toxins can be traced by simply studying the bird's history. According to the author, humans have used information they could derive form birds to explain local environmental as well as natural phenomenon.

Understanding tropical succession requires insight into propagule availability, which constrains possible woody plant recruitment, yet seed rain composition in multiple post-agricultural sites has seldom been examined. We monitored seed rain for 60 wk in five abandoned pastures in southern Costa Rica, collecting a total of 1 140 688 seeds of 165 morphospecies. Most seeds (80.1%) arrived during the wet season. Species richness was highest in the wet season and greater in forest than in pasture. Seed rain density was greatest at the forest/pasture edge and decreased drastically just a few metres into pastures. In and near the forest, animal-dispersed seeds were more abundant than seeds dispersed by other means, while wind-dispersed taxa increased in relative importance at greater distances from the forest. Total seed input to pastures did not reflect size of adjacent forest fragments, although seed rain density varied more than threefold among sites. Among-site variation in density of regenerating woody seedlings was roughly proportional to among-site variation in seed rain. Morphospecies composition differed significantly among sites. Also, seed rain and woody plant colonists were rather dissimilar in composition, suggesting that while propagule availability is necessary for early woody plant establishment, it is a poor predictor of successional trajectory. [ABSTRACT FROM PUBLISHER]

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm. [ABSTRACT FROM AUTHOR]

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented . [ABSTRACT FROM AUTHOR]

Differential Galois theory, the theory of strongly normal extensions, has unfortunately languished. This may be due to its reliance on Kolchin's elegant, but not widely adopted, axiomatization of the theory of algebraic groups. This paper attempts to revive the theory using a differential scheme in place of those axioms. We also avoid using a universal differential field, instead relying on a certain tensor product. We identify automorphisms of a strongly normal extension with maximal differential ideals of this tensor product, thus identifying the Galois group with the closed points of an affine differential scheme. Moreover, the tensor product has a natural coring structure which translates into the Galois group operation: composition of automorphisms. This affine differential scheme splits, i.e. is obtained by base extension from a (not differential, not necessarily affine) group scheme. As a consequence, the Galois group is canonically isomorphic to the closed, or rational, points of a group scheme defined over constants. We obtain the fundamental theorem of differential Galois theory, giving a bijective correspondence between subgroup schemes and intermediate differential fields. On the way to this result we study certain aspects of differential algebraic geometry, e.g. closed immersions, products, local ringed space of constants, and split differential schemes. [ABSTRACT FROM AUTHOR]

Background: Expression of platelet-derived growth factor (PDGF) and activation (by autophosphorylation) of its receptor (PDGF-R), a tyrosine kinase, are associated with the growth of metastatic prostate tumor cells in the bone parenchyma. The tyrosine kinase inhibitor STI571 blocks the PDGF signaling pathway by inhibiting PDGF-R autophosphorylation. We examined the effects of STI571, given alone or with paclitaxel (Taxol), on tumor growth in a mouse model of prostate cancer metastasis.Methods: Human prostate cancer PC-3MM2 cells were injected into the tibias of male nude mice. Three days later the mice (20 per group) were randomly assigned to 5 weeks of treatment with oral and injected water (control), daily oral STI571, weekly injected paclitaxel, or STI571 plus paclitaxel. Lesions in bone and the surrounding muscles were then harvested and analyzed by histology, western blotting (for PDGF-R phosphorylation), immunohistochemistry (for expression of proangiogenic molecules), and double immunofluorescence (to identify endothelial cells and apoptotic tumor cells). Growth of bone lesions was monitored by digital radiography. Bone lesions from control mice were used to establish short-term cell cultures for analysis of PDGF-R phosphorylation. All statistical tests were two-sided.Results: PC-3MM2 cells cultured from bone lesions and treated in vitro with STI571 had less phosphorylated PDGF-R than untreated cells. In control mice, bone lesions expressed high levels of PDGF and activated (i.e., phosphorylated) PDGF-R, whereas lesions in the adjacent musculature did not. Activated PDGF-R was present on the surface of endothelial cells within the bone lesions but not in endothelial cells of uninjected bone. Mice treated with STI571 or STI571 plus paclitaxel had a lower tumor incidence, smaller tumors, and less bone lysis and lymph node metastasis than mice treated with water or paclitaxel alone (P<.001 for all). Mice treated with STI571 or STI571 plus paclitaxel had less phosphorylated PDGF-R on tumor cells and tumor-associated endothelial cells, less tumor cell proliferation, statistically significantly more apoptotic tumor cells (all P<.001), and fewer tumor-associated endothelial cells (P<.001) than control mice.Conclusions: Endothelial cells appear to express phosphorylated PDGF-R when they are exposed to tumor cells that express PDGF. Using STI571 to inhibit PDGF-R phosphorylation may, especially in combination with paclitaxel, produce substantial therapeutic effects against prostate cancer bone metastasis. [ABSTRACT FROM AUTHOR]

Background: Overexpression of epidermal growth factor receptor (EGF-R) is associated with increased malignant potential and correlates with poor clinical outcome in head and neck cancer. Therefore, inhibition of the EGF-R pathway provides an ideal target for molecular therapy. We examined in vitro and in vivo effects of PKI166, an orally administered EGF-R inhibitor, on 2 human squamous cell carcinoma of the oral cavity cell lines, Tu159 and MDA1986. Study Design: Basic science, laboratory investigation. Results: For Western blotting, Tu159 and MDA1986 cells were pretreated for 1 hour and then stimulated with EGF. The EGF-R–specific tyrosine kinase autophosphorylation was inhibited completely by PKI166 at all doses tested (1-10 µg/mL). By means of a tetrazolium-based viable cell assay, PKI166 was shown to arrest the growth of Tu159 and MDA1986 cells. The inhibitory concentration (50%), calculated from regression lines on the linear portion of the growth inhibition graphs, was 0.18µM (R = 0.98) for Tu159 cells and 0.23µM (R = 0.97) for MDA1986 cells. Nude mice were inoculated subcutaneously with 1 × 10[sup 6] Tu159 tumor cells and observed for 7 days. Next, daily doses of PKI166 (0, 10, or 50 mg/kg) were delivered by orogastric lavage for 28 days and the animals were observed for tumor growth. PKI166 significantly reduced tumor growth in mice treated for 1 month with oral PKI166 in a dose-dependent fashion. Conclusions: Targeted molecular therapy with EGF-R blockade arrests the growth of oral cancer in vitro and reduces its proliferation in an experimental xenograft animal model. [ABSTRACT FROM AUTHOR]