Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

Assessment of Environmental Performance of Rapid Prototyping and Rapid Tooling Processes

A method for assessing the environmental performance of solid freeform fabrication (SFF) based rapid prototyping and rapid tooling processes is presented in this paper. In this method of assessment, each process is divided into a number of life stages. The environmental effect of each process stage is analyzed and evaluated based on an environmental index utilizing the Eco-indicators that were compiled by PreConsultants of the Netherlands. The effects of various life stages are then combined to obtain the environmental performance of a process. In the assessment of SFF processes, we consider the material use in the fabrication of a part, energy consumption, process wastes, and disposal of a part after its normal life. An example is given to illustrate this assessment method applied to the stereolithography (SLA) process and two SLA based rapid tooling processes

Environmental Performance Analysis of Solid Freedom Fabrication Processes

This paper presents a method for analyzing the environmental performance of solid freeform fabrication (SFF) processes. In this method, each process is divided into life phases. Environmental effects of every process phase are then analyzed and evaluated based on the environmental and resource management data. These effects are combined to obtain the environmental performance of the process. The analysis of the environmental performance of SFF processes considers the characteristics of SFF technology, includes material, energy consumption, processes wastes, and disposal. Case studies for three typical SFF processes: stereolithography (SL); selective laser sintering (SLS); and fused deposition modeling (FDM) are presented to illustrate this method

Lifecycle Analysis for Environmentally Conscious Solid Freeform Manufacturing

A lifecycle based process model for analyzing the environmental performance of SFM processes and SFM based rapid tooling processes is presented in this paper. The process environmental performance assessment model considers material, energy and disposal scenarios. The material use, process parameters (e.g. scanning speed) and power use can affect the environmental consequence of a process when material resource, energy, human health and environmental damage are taken into account. The presented method is applied to the SLA process and two SLA based rapid tooling processes. The method can be used to compare different rapid prototyping (RP) and RT processes in terms of their environmental friendliness and for further multi-objective decision makin

Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision

Effect of Ultrasonic Treatment on Physicochemical and Functional Properties of Goat Milk Casein

In this experiment, goat milk casein was used as raw material. Goat milk casein was treated with different ultrasonic intensities to explore the effects of ultrasonic treatment on the pH, conductivity, Zeta potential, turbidity and other physical and chemical indexes, as well as the functional characteristics of emulsification and foaming. The results showed that compared with untreated group, after ultrasonic treatment, the pH decreased and the conductivity had no significant change (P>0.05). The absolute value of Zeta potential reached the maximum value of 27.85±1.55 mV when treated at 400 W for 2 min. Turbidity was significantly decreased compared with untreated group (P<0.05). Ultrasonic treatment reduced the particle size of casein which reached its lowest value at 700 W treatment for 6 min, from 383.20±13.07 nm to 176.17±4.28 nm. Scanning electron microscopy results showed that ultrasound changed the original apparent morphology of casein, resulting in the formation of irregular fragments. After ultrasonic treatment, the emulsification at 100 W for 10 min was increased by 41.44%, and the foaming capacity and foaming stability of goat milk casein at 400 W for 10 min were significantly increased (P<0.05), reaching the maximum value of 32.88%±0.07% and 91.93%±0.19%, respectively. In conclusion, the cavitation effect and mechanical action produced by ultrasonic treatment can provide better physicochemical and functional properties for goat milk casein

Metabolomic profiles of bovine mammary epithelial cells stimulated by lipopolysaccharide

Bovine mammary epithelial cells (bMECs) are the main cells of the dairy cow mammary gland. In addition to their role in milk production, they are effector cells of mammary immunity. However, there is little information about changes in metabolites of bMECs when stimulated by lipopolysaccharide (LPS). This study describes a metabolomics analysis of the LPS-stimulated bMECs to provide a basis for the identification of potential diagnostic screening biomarkers and possible treatments for bovine mammary gland inflammation. In the present study, bMECs were challenged with 500 ng/mL LPS and samples were taken at 0 h, 12 h and 24 h post stimulation. Metabolic changes were investigated using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF MS) with univariate and multivariate statistical analyses. Clustering and metabolic pathway changes were established by MetaboAnalyst. Sixty-three differential metabolites were identified, including glycerophosphocholine, glycerol-3-phosphate, L-carnitine, L-aspartate, glutathione, prostaglandin G2, α-linolenic acid and linoleic acid. They were mainly involved in eight pathways, including D-glutamine and D-glutamic acid metabolism; linoleic acid metabolism; α-linolenic metabolism; and phospholipid metabolism. The results suggest that bMECs are able to regulate pro-inflammatory, anti-inflammatory, antioxidation and energy-producing related metabolites through lipid, antioxidation and energy metabolism in response to inflammatory stimuli

Pressure-induced coevolution of transport properties and lattice stability in CaK(Fe1-xNix)4As4 (x= 0.04 and 0) superconductors with and without spin-vortex crystal state

Here we report the first investigation on correlation between the transport properties and the corresponding stability of the lattice structure for CaK(Fe1-xNix)4As4 (x=0.04 and 0), a new type of putative topological superconductors, with and without a spin-vortex crystal (SVC) state in a wide pressure range involving superconducting to non-superconducting transition and the half- to full-collapse of tetragonal (h-cT and f-cT) phases, by the complementary measurements of high-pressure resistance, Hall coefficient and synchrotron X-ray diffraction. We identify the three critical pressures, P1 that is the turn-on critical pressure of the h-cT phase transition and it coincides with the critical pressure for the sign change of Hall coefficient from positive to negative, a manifestation of the Fermi surface reconstruction, P2 that is the turn-off pressures of the h-cT phase transition, and P3 that is the critical pressure of the f-cT phase transition. By comparing the high-pressure results measured from the two kinds of samples, we find a distinct left-shift of the P1 for the doped sample, at the pressure of which its SVC state is fully suppressed, however the P2 and the P3 remain the same as that of the undoped one. Our results not only provide a consistent understanding on the results reported before, but also demonstrate the importance of the Fe-As bonding in stabilizing the superconductivity of the iron pnictide superconductors through the pressure window