Manufacturing Engineering

Manufacturing Industry is one of the industrial activities in Indonesia, manufacturing industry is an industry with main activities is to change raw materials, components, or other parts into goods which is according to company specifications standards. In the production floor, activity in the manufacturing industry, the workers have different job specifications with each other. Some works consist of human-machine interaction is found by the activity between workers and lathe machine, welding maching, milling machine, frais machine, and others. The manufacturing industry will increase its ability to serve a variety of better quality products caused by the desire or encouragement of the customers. In general, according to the increase of corporate targets, its also need improvement from the aspect of work performance. To obtain a description of the proposed improvement based on human performance, it is necessary to identify the eight aspects of ergonomics include the consumption of nutritionalfor workers (energy), muscle power, body posture, environment, time conditions, social conditions, information conditions, and human machine interaction. This research use cross sectional method approach that is research done at one time, no follw up, to find the correlation between independent variable (risk factor) with dependent variable (effect). The conclusion of this research is needed nutrition intake or nutrition to recover the workers, it is necessary to design facilities such as manufacturing aids to reduce the use of excess muscle or appropriate technology (TTG). After the application of TTG (Appropriate Technology) to reduce the excessive use of muscle to the workers, the company should provide the nutritional intake accordance with workload of employees in the manufacturing industry

The carbon footprint (CFP) assessment of smallholders offers key information on the capacities and challenges for greenhouse gas (GHG) mitigation at farm scale. This allows prioritizing the practices that ensure both the food security of farmers and the low carbon impact associated to climate change. To tackle food security challenges and to maintain sustainable environment production, agroecological practices were planned for farmers in the Itasy region, Central Highlands of Madagascar. The project consisted of agroforestry and forestry systems, composting of organic matters, and system of rice intensification. The goals of this study were (i) to assess the CFP of farms in the Itasy region Central Madagascar, (ii) to assess the impact of agroecological practices adopted by farmers on farms CFP, and (iii) to compare the impact of Tier 1 and Tier 3 factors for carbon removal in woody biomass and in cropland soils on farms CFP. For these purposes, a survey of 192 representative farms was realized during the years 2012e2013. Agroecological practices integrated at farm scale reduced significantly farms CFP up to 364% in terms of land surface and up to 578% in terms of food production, suggesting an important GHG sequestration at farm scale. Main sources of GHG at farms scale were: nitrous oxide from soil management (25%), methane from rice cultivation (24%), livestock manure management (24%), and enteric fermentation (23%). Trees planted in agroforestry and forestry systems offered the highest GHG mitigation benefits. Tier 1 factors overestimated up to 7 times higher the farms CFP compared to Tier 3 factors. This study highlights that the integration of agro-ecological practices at farm scale offers significant GHG mitigation and carbon sequestration in Malagasy context, thus giving an alternative for climate change mitigation.

A numerical investigation of nanofluid flow and heat transmission through a circular pipe has been carried out. The nanofluid considered is a CuO-water mixture with varying volume fractions. The inlet flow has been assumed to be uniform at a constant temperature. In addition, the wall temperature is constant. The numerical methodology employed is S. V. Patankar's Control Volume Formulation, which employs the SIMPLER algorithm and a power law scheme. The results reveal a significant increase in overall heat transfer rate as the nanoparticle volume fraction
increases, at the expense of pressure decrease.

A four-week mini-project for teaching is described for an experimental physical chemistry or organic chemistry course. The activities included synthesis of a Pd catalyst, PdCl 2 supported on calcined coconut shell fiber, and its use in the preparation of 4-methylbiphenyl via Suzuki-Miyaura cross-coupling reaction. The technical skills and concepts that are typically presented in practical chemistry courses were covered, including notions of surface chemistry e development of new catalysts, modern organic synthesis, gas chromatography techniques, separation of mixtures, and stoichiometric calculations.

This paper proposes a novel finite dimensional discrete-time Nonlinear Model Predictive Control. This technique is based on discrete-time state-space models, Taylor series expansion for prediction and performance index optimization. Furthermore, the technique extends the concept of the Lie derivative for the discrete time case using Euler backwards method. The performance validation for the discrete-time Nonlinear Model Predictive Control uses the simulation of a single-link flexible joint robot and the inverted pendulum. Comparison of the proposed finite dimensional discrete-time Nonlinear Model Predictive Control technique with Feedback Linearization Control is also discussed. Analytical and numerical results show excellent performances for both, the single-link flexible joint and inverted pendulum controllers using the proposed discrete-time Nonlinear Model Predictive Control technique.

Background: Cloud robotics is an integration of cloud computing with robotics, which will bring efficiency to task management in both healthcare and manufacturing sectors. Optimization of performance needs effective scheduling and resource allocation. Objectives: This research is aimed at the optimization of dynamic task scheduling with fuzzy logic and metaheuristics in healthcare and manufacturing settings for enhanced task allocation and execution.s Methods: We adopted a hybrid methodology to integrate decision-making using fuzzy logic with optimization techniques of metaheuristic algorithms in order to optimally schedule the tasks. Parameters include resource usage, task priorities, and the time taken by each task during execution. Results: The result showed that it is possible to have 92% accuracy on the allocation of tasks with the proposed model where 35% execution time improvement compared to conventional approaches.

The increasing price of energy, the security of supply, the reduction of green house gases, and the scarcity of oil and gas urge the use of more and more renewable energy. An important renewable energy source is the biomass which can be applied for heat, electricity, and transportation fuel production. The heat and electricity production are the so called ''direct utilisation'' alternatives and the transportation fuel production alternatives are the ''indirect utilisation'' alternatives of biomass energy. If efficient land use is considered, the alternatives can be compared on the basis of the utilisable energy produced from the biomass per hectare. It is shown that the bioethanol production from corn has about 89-99% less energy production capability than that of the direct utilisation alternatives. The cellulosic type bioethanol production technologies, since these partially directly utilise the biomass energy, have better energy utilisation potential, that is about 40-50% of direct alternatives.

Grinding is one of the final types of processing, which provides high quality of the treated surface (accuracy and roughness) at high productivity of the process. Ensuring the properties of the surface depends largely on the correct choice of the working tool –the grinding wheel, and the parameters of the grinding mode.The main characteristics of the grinding wheel are its geometric parameters (diameter, width) and the characteristics of the material of the wheel (grain size, binder type, diamond concentration and thickness of the diamond layer). The parameters of the grinding mode include the speed of rotation of the wheel and the depth of grinding and feed.Premium  and  CBN  grinder  wheels  on  the  organic  connection  of  the  production  of  PJSC  “Poltava Diamond Tool” are used in the study.The parameters for the grinding round as well as the grinding modes have  been  chosen  based  on  DSTU  (ISO),  which  regulates  the  nomenclature  of  grinding  round  and requirements of howthey should operate: the roughness of a finished surface –Ra and the coefficient of grinding.We considered and analysed constant parameters, like the brand of grinding circle, the type of grinding  operation  (flat)  and  the  use  of  lubricating  and  cooling technical  means,  as  well  as  variable parameters of grinding mode. The parameters of the grinding mode are the main control factors of the grinding process and are set for the grinding machine by the operator directly.PJSC “Poltava Diamond Tool” is the company that specialises in the production of a wide range of diamond and CBN tools for grinding and polishing hard alloys, as well as heat-resistant, alloyed and stainless types of steel and other materials.The specialists of the factory together with scientists have carried out multiple works on optimisingthe method of choosing thegrinding parameters and the grinding mode depending on the processing material, the specified accuracy and roughness of the surface and working capacity.The recommended range of grinding wheel grain and  relative  concentration  of  diamond  powder  in  the  diamond  layer  has  been  developed  to  achieve  the specified roughness of the treated surface at different types of grinding. The recommended grinding modes are presented for differenttypes of grinding with circles from cubic boron nitride CBN.

Latin America and Caribbean (LAC) is a developing region characterized for its importance for global food security, producing 23 and 11% of the global beef and milk production, respectively. The region's ruminant livestock sector however, is under scrutiny on environmental grounds due to its large contribution to enteric methane (CH 4 ) emissions and influence on global climate change. Thus, the identification of effective CH 4 mitigation strategies which do not compromise animal performance is urgently needed, especially in context of the Sustainable Development Goals (SDG) defined in the Paris Agreement of the United Nations. Therefore, the objectives of the current study were to: 1) collate a database of individual sheep, beef and dairy cattle records from enteric CH 4 emission studies conducted in the LAC region, and 2) perform a meta-analysis to identify feasible enteric CH 4 mitigation strategies, which do not compromise animal performance. After outlier's removal, 2745 animal records (65% of the original data) from 103 studies were retained (from 2011 to 2021) in the LAC database. Potential mitigation strategies were classified into three main categories (i.e., animal breeding, dietary, and rumen manipulation) and up to three subcategories, totaling 34 evaluated strategies. A random effects model weighted by inverse variance was used (Comprehensive Meta-Analysis V3.3.070). Six strategies decreased at least one enteric CH 4 metric and simultaneously increased milk yield (MY; dairy cattle) or average daily gain (ADG; beef cattle and sheep). The breed composition F1 Holstein × Gyr decreased CH 4 emission per MY (CH 4 I Milk ) while increasing MY by 99%. Adequate strategies of grazing management under continuous and rotational stocking decreased CH 4 emission per ADG (CH 4 I Gain ) by 22 and 35%, while increasing ADG by 22 and 71%, respectively. Increased dietary protein concentration, and increased concentrate level through cottonseed meal inclusion, decreased CH 4 I Milk and CH 4 I Gain by 10 and 20% and increased MY and ADG by 12 and 31%, respectively. Lastly, increased feeding level decreased CH 4 I Gain by 37%, while increasing ADG by 171%. The identified effective mitigation strategies can be adopted by livestock producers according to their specific needs and aid LAC countries in achieving SDG as defined in the Paris Agreement.

Industry 4.0 implies the digitization of the shopfloor combining technologies such as sensors, augmented reality, and wearable robots that transform the manufacturing environment into a workplace where human-machine interactive symbiosis. As manufacturing companies develop strategies to innovate and engage with the digital transformation, the reality of the enabling technologies demonstrate serious challenges to the wider organizational adoption beyond the pilot phase albeit the promising evaluation results. This paper presents two cases based on two European research projects encompassing representatives of different industrial sectors and distils the challenges encountered that raise barriers to wider adoption.

In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory reprogramming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolás Correa S.A. VERSA milling machine.

Process Analytical Technology (PAT) is a system for designing, analyzing and controlling manufacturing process through timely measurements of critical quality and performance attributes of raw materials, in-process materials and processes with the goal of ensuring final product quality as well as reducing manufacturing costs, thereby significantly benefiting the Pharmaceutical Industry in manufacturing area. PAT involves the use of different technologies and tools to build quality into the products. Different techniques are described for typical steps in the production of drug substance and drug product. These methods are mainly based on spectroscopy and other optical sensors, in combination with chemometric multivariate data evaluation tools. Pharmaceutical companies face many challenges while implementing PAT into their new and pre-existing manufacturing processes.

The role of maintenance in industry is important to ensure optimal performance, reliability and longevity of equipment, machinery and infrastructure. This maintenance ensure the continuity of the production with minimal losses. The present paper present the types of maintenance and based on this it focused on developing a generalized software which can be applied mainly in mechanical production industry. The main positive aspects of the software are: it represent an open source through the option of adding equipment, it can deliver sheets with costs of preventive maintenance and lists of personnel. Maintenance activities play a significant role in supporting the production process, minimizing downtime, reducing costs and increasing overall operational efficiency.

Transformation in the manufacturing industry followed by the development of globalization, requiring performance improvements along the supply chain. Supply chain management is one of competitive strategy to enhance the company's productivity and profitability. Supply chain management maturity models is present to propose the best practices in supply chain management. A basic idea from these models is that increased maturity will improve supply chain performance. This study investigates the relationship between supply chain management maturity and supply chain performance based on the five key supply chain management maturity dimensions consisting of strategy; asset and facility; human resource; information and technology system; and process areas provided in SCOR model derived from previous literature. They were collected by web-survey from relatively manufacturing industry in various company size. The study results may be limited by the size of collected sample. The research framework and hypothesis in this study were tested using Partial Least Square approach. The results specifically highlight the contribution implementation of supply chain management maturity which can improve supply chain operational and supply chain organizational performance. This study provides useful planning information in the manufacturing industry.

The elasto-plastic theory of a shallow two bar truss is developed. The behavior of this structural system under tension loading or compression loading is formulated, as well as for reversal loading, where the structure undergoes loading, unloading and reloading. The simple straightforward expressions enable one to correlate the external work to the central displacement and follow the elasto-plastic stresses and strains in the bars along the loading history.

Manufacturing system has been rapidly developing during these decades. Within this advancement, maintenance becomes an important supporting factor. Maintenance aims to develop better production processes in order to perform as expected. Preventive maintenance (PM) is one of maintenance strategies to prevent incipient failures. Many scholars have been studying PM in numerous occasions as well as many different perspectives. This paper attempts to review a number of references, classify them from different view points, as well as integrate both theoretical and practical benefit obtained. This paper also highlights many approaches in determining optimal maintenance policy. The proposed classification is based on the practical cases presented in references. Discussion about comprehensive thinking in PM strategy is also elaborated. Based on authors’ knowledge, there papers work on our concern beforehand. This feature, makes this paper different from others. This paper offers usefull new ...

Requirements Applying of Six Sigma Methodology to Reduce Productive Wastage at Yemeni Pharmaceutical Manufacturing Companies: A Field Study
The Study aimed to identify the impact of Requirements Applying of Six Sigma Methodology with its dimensions (Top Management commitment and support, Continuous Improvement, Human Resources, technology and information systems) on Reducing Productive Wastage, the level of Requirements applying it and the Reducing Productive Wastage at Yemeni Pharmaceutical Manufacturing Companies. To achieve the study objectives, the descriptive analytical method was used and the study population consisted of the workers in Yemeni Pharmaceutical Manufacturing Companies with a total number of (476) employees. The study questionnaire was distributed on stratified random sample with a total number of (195) employees. By analyzing the data using descriptive analysis methods and multiple regression analysis using the SPSS program, the findings revealed positive impact of Requirements Applying of Six Sigma methodology on the reduction of productive waste in Yemeni Pharmaceutical Manufacturing Companies, and also that there is a positive impact of the three Applying of Six Sigma Methodology dimensions: Continuous Improvement, information and technology systems and Human Resources on Reducing Productive Wastage, while there was no impact of the top Management commitment and support on Reducing Productive Wastage in Yemeni Pharmaceutical Manufacturing Companies, due to the variation in the workers awareness level of Requirements Applying of Six Sigma Methodology. The findings also showed that the level of Requirements applying of the six sigma methodology at Yemeni Pharmaceutical Manufacturing Companies was high, and the level of Reducing Productive Wastage was high as well in Yemeni Pharmaceutical Manufacturing Companies.
Keywords: Six Sigma, the reduction of productive waste, Yemeni Pharmaceutical Manufacturing Companies.

The lubrication approximation is applied to electro-osmotic flow through a thin parallel-plate channel under the combined effect of charge and hydrodynamic slippage modulation on the walls. The walls are periodically patterned for the charge and slip distributions, with a wavelength much longer than the channel height. It is shown that the phase of the wall patterns will play a significant role in determining the section-averaged velocity as well as the local convection pattern, quantitatively and qualitatively. The effect of the phase on the flow will be dramatically different, depending on whether the electric field is applied along or perpendicular to the varying direction of the patterns. The possibility of generating a net flow in a direction perpendicular to the applied field is demonstrated.

Motivated by the importance of customer buying behaviour (such as correlation among product attributes/features of products configured in the past) in planning future configurations, this paper addresses the issue that product evolution (upgrades) usually render information gathered from past buying behaviour at least partially unusable. For instance, relations among features might have been changed, thus making it difficult to configure the same products again. The proposed approach aims to (1) find associations between product attributes based on the analysis of prior customer orders (2) apply configuration rules to prune attribute association rules which are not controlled by customers, and (3) check whether derived attribute association rules from past orders also work for the new upgraded product. Attribute associations consistent with the upgraded product are then used to predict configurations for production planning. We use machine learning algorithms and optimization techniques to address these issues.

A product (e.g. automobile, computer) can be configured using different combinations of its available attributes (features). However, selection of attributes may not be independent of the selection of other attributes. In practice, each attribute implies a selection rule (dependency) for other sets of attributes in order to generate a valid configuration. Due to dynamic changes in the product design, miniaturization, legislation etc., product attributes and their selection rules get changed. This implies that variants produced in the past may not be valid for future product design. Nevertheless, customer history contains important information related to customer buying behaviour which is an essential input for future planning activities. In order to achieve efficient adaption of past customer orders to a changed product design, we propose a fully automated optimization based framework. The methodology is demonstrated using an industry size example.

Motivated by the importance of customer buying behaviour (such as correlation among product attributes/features of products configured in the past) in planning future configurations, this paper addresses the issue that product evolution (upgrades) usually render information gathered from past buying behaviour at least partially unusable. For instance, relations among features might have been changed, thus making it difficult to configure the same products again. The proposed approach aims to (1) find associations between product attributes based on the analysis of prior customer orders (2) apply configuration rules to prune attribute association rules which are not controlled by customers, and (3) check whether derived attribute association rules from past orders also work for the new upgraded product. Attribute associations consistent with the upgraded product are then used to predict configurations for production planning. We use machine learning algorithms and optimization techni...

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process• that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence teclmiques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.

Closed-form mechanisms of relative manipulation robot is an effective structure which helps to improve the accuracy and flexibility in technological processes. Unfortunately, the requirement about knowledge of exact dynamics of closed-form mechanisms of relative manipulation robot is arduous since it consists of numerous joints and links, and the identification of the kinematic relationship of closed-form structure is also complicated. This causes several shortcomings for controlling closed-form mechanisms of relative manipulation robot by using vector control algorithms because these methods require exactly dynamical equations of control systems. In contrast, the fuzzy controllers do not require knowledge of detailed mathematical equations of the control system since the fuzzy sets aim to capture the semantics of natural linguistic terms present in the fuzzy controller knowledge. Moreover, they have capability of handling uncertain and noisy signals, this helps to deal with the external environmental forces. This article proposes a fuzzy-based controller for closed-form mechanisms of relative manipulation robot to overcome mentioned problems by eliminating the identification of exact dynamics and kinematic constraints of closed-form structure. To verify the performance of the proposed method, the fuzzy-based controller is applied to a welding task by using a model of two-component mechanism which includes one closed-form manipulator and one serial manipulator. The welding task is also conducted by using conventional controllers in which the detailed dynamical equation is applied for the proportional-derivative (PD)-type and proportional integral derivative (PID)type computed torque controllers, whereas the fuzzy-based controller just uses several nominal parameters.

The main purpose of composite materials drilling is the need to put together different parts of a structure, in aeronautics for example. Machining generates damages which affect mechanical properties and have to be taken into account during manufacturing process. The objective of this study is to experimentally analyze the influence of drilling on a carbon/epoxy composite, to investigate the relationships among damages, cutting forces, mechanical properties of the drilled specimens and crack propagation. Stitching and a range of spindle speed and feed have been tested when drilling with a classic twist drill. The effect of each parameter has been assessed in terms of thrust force, moment (during machining) and defects, and then linked to mechanical test results. Experimental results have shown significant influences of feed and composite configuration on delamination. Furthermore, cyclic tensile tests have shown that reducing damage and using stitching help increasing tensile strength.

This study deals with the effect of High-Pressure Water Jet Assisted Turning (HPWJAT) of austenitic stainless steels on chip shape and residual stresses. The machining of the austenitic stainless steels represents several difficulties. Recently, research has shown that the introduction of a high-pressure water jet into the gap between the tool and the chip interface is a very satisfactory method for machining applications. In this article, the effect of a high-pressure water jet, directed into the toolchip interface, on chip shapes breakage and surface integrity in face turning operations of AISI 316L steel has been investigated. Tests have been carried out with a standard cutting tool. The cutting speeds used were 80 and 150 m/min, with a constant feed rate of 0.1 mm/rev and a constant cutting depth of 1 mm. Three jet pressures were used: 20, 50 and 80 MPa. Residual stress profiles have been analysed using the X-ray diffraction method in both longitudinal and transversal directions. The results show that jet pressure and cutting parameters influence the residual stresses and the chip shapes. Using a high-pressure jet, it is possible to create a well fragmented chip in contrast to the continuous chip formed using dry turning. It is also possible to control the chip shape and increase tool life. When the jet pressure is increased the residual stress at the surface decreases; however it is increased by an increase in cutting speed. It can be concluded that surface residual stresses can be reduced by the introduction of a high-pressure water jet.

In this paper, a new fault-tolerant control system is proposed for input-affine nonlinear plants based on Model Reference Adaptive System (MRAS) structure. The proposed method has the capability to accommodate both partial and total actuator failures along with bounded external disturbances. In this methodology, the conventional MRAS control law is modified by augmenting two compensating terms. One of these terms is added to eliminate the nonlinear dynamic, while the other is reinforced to compensate the distractive effects of the total actuator faults and external disturbances. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed method. Moreover, the control structure has good robustness capability against the parameter variation. The performance of this scheme is evaluated using a CSTR system and the results were satisfactory.

In this paper, a new fault-tolerant control system is proposed for input-affine nonlinear plants based on Model Reference Adaptive System (MRAS) structure. The proposed method has the capability to accommodate both partial and total actuator failures along with bounded external disturbances. In this methodology, the conventional MRAS control law is modified by augmenting two compensating terms. One of these terms is added to eliminate the nonlinear dynamic, while the other is reinforced to compensate the distractive effects of the total actuator faults and external disturbances. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed method. Moreover, the control structure has good robustness capability against the parameter variation. The performance of this scheme is evaluated using a CSTR system and the results were satisfactory.

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing-heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict productionrelated challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing-heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing-heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.

Stem cell research attracts considerable attention in developmental biology, nanobiotechnology, and medicine. Since Maksimow's landmark hypothesis on stem cells in 1909, hundreds of thousands of reports have been published in biology, medicine and biotechnology regarding stem cells, evidence of their considerable worth as a major research subject. Artificial stem cells such as 'induced pluripotent stem cells' (iPS cells), which morphologically and functionally resemble embryonic stem cells have been generated through the reprogramming of somatic cells for research purposes. Typically, optical techniques such as light microscopy in combination with fluorescent markers are applied, sometimes accompanied with fluorescence-activated cell sorting (FACS), to characterize and isolate both tissue-specific stem and iPS cells. To date, modern laserbased techniques such as nonlinear multiphoton microscopy and optical transfection using femtosecond lasers are only used in a few laboratories. The application of innovative femtosecond laser techniques in stem cell research shall be the subject of this chapter. Of particular focus is the application of femtosecond laser microscopy (i) for imaging and characterization of stem cells with high spatial sub-cellular resolution; (ii) as a nanoscalpel for optical cleaning; and (iii) for transfection and optical reprogramming of cells.

Compression properties of textile materials are useful for determining fullness, softness, smoothness, and stiffness. In addition, compression parameters have significant influence on fabric hand value and quality of textile materials. The present paper reports a study of the sublimation printing process influence on the change in compression properties of knitted fabrics (polyester, cotton, and cotton/ polyester) intended for sportswear manufacturing. For that purpose, the KES-FB3-A Compression Tester was used for measuring compression parameters of knitted fabrics. The parameters, which contributed to the smallest reduction percentage in compression properties of polyester knitted fabrics were yarn linear density and weight. For cotton-rich knitted fabrics sublimation coating powder was used for paper modification to bond disperse dyes to cotton fibres. The analysis of the results showed that the printing process had a smaller influence on the change of compression parameters of p...

Original measuring equipment is presented, together with the systems aimed at investigating process parameters of the operation structure and establishing optimal working methods to evaluate properly real working conditions of the man-machine system in garment engineering and technologies. The measuring equipment consists of the system for measuring and storing data on process parameters and bi-plane system of video recording. The system for measuring and data storage is equipped with four sensors, which measure rotation speed of the main shaft, arm movements in the taking and laying-off zones, as well as the movements of pedal regulator, used to manage and control the overall sewing dynamics, all in a simultaneous and contactless manner. Bi-plane system of video recordings is used to record simultaneously the working movement systems (working methods) at the workplace analysed. The operation of seam sewing on the front part of a ladies' costume is analysed on a designed workpla...

In my PhD thesis I investigate the relationship between corporates ́ financial and environmental performances. The concept of quantitative environmental performance measures was introduced to enable to compare and analyse environmental impacts of different socio-economic units e.g. companies, countries, regions. In my dissertation, I use environmental performance measures to examine their effect on the financial performance of different companies. In the first chapter, I apply a meta-analysis to examine the results of the previous studies which investigate the impact of firms ́ environmental performance on their financial performance. The outcomes propose that it is important to account for the omitted variable bias such as unobserved firm heterogeneity. The results suggest that it takes time for the environmental regulation to materialize into the financial performance, too. In the subsequent two chapters I study Czech firms over 2004-2008. First I study the intertemporal effects o...

This paper presents the design of a novel single square millimeter 3-axial accelerometer for head injury detection of racing car drivers. The main requirements of this application are miniaturization and high-G measurement range. We propose a new miniature accelerometer to be incorporated into an earpiece. Nanowires as nanoscale piezoresistive devices have been chosen as sensing element, due to their high sensitivity and miniaturization achievable. By exploiting the electro-mechanical features of nanowires as nanoscale piezoresistors, the nominal sensor sensitivity is overall boosted by more than 30 times. This approach allows significant higher accuracy and resolution with smaller sensing element in comparison with conventional devices without the need of signal amplification. This achievement opens up new developments in the area of implanted devices where the high-level of miniaturization and sensitivity is essential.

Thin walled structures have been applied in engineering field to improve the passive safety of automobiles. This paper studies the crashworthiness of thin-walled tube with elliptical cross section. The influence of aspect ratio f , perimeter of section 0 C and thickness 0 h on energy absorption characteristics were investigated based on the theory of plastic deformation. First, the plastic deformation mode of the elliptical thin-walled tube (ETT) is expounded under axial impact. Second, the theoretical model to predict the mean crushing force of the ETT under axial impact is developed based on the principle of the folding deformation mechanism; finally, the comparison of the theoretical and numerical predictions of ETT are conducted to verify theoretical model. The results also show that the mean crushing force m P is positively correlated with 0 C and 0 h , and negatively related to f .

Thin walled structures have been applied in engineering field to improve the passive safety of automobiles. This paper studies the crashworthiness of thin-walled tube with elliptical cross section. The influence of aspect ratio f , perimeter of section 0 C and thickness 0 h on energy absorption characteristics were investigated based on the theory of plastic deformation. First, the plastic deformation mode of the elliptical thin-walled tube (ETT) is expounded under axial impact. Second, the theoretical model to predict the mean crushing force of the ETT under axial impact is developed based on the principle of the folding deformation mechanism; finally, the comparison of the theoretical and numerical predictions of ETT are conducted to verify theoretical model. The results also show that the mean crushing force m P is positively correlated with 0 C and 0 h , and negatively related to f .

In linear mechanical systems, the vibration of mechanical structures depends upon the boundary conditions, the mode of excitation and the dissipative or damping capacities of the system itself. The latter are the result of passive residual and structural friction. In the cases where passive damping of mechanical structures becomes inadequate, dynamic reactivity of the structure may be influenced by controlled active damping which is particularly suitable for non-linear systems where the structure response and excitation are directly interconnected. The present paper deals with active electromagnetic damping of lateral vibration of a clamped steel cantilever. The electromagnetic system consists of a switching current amplifier and a pair of electromagnets, which are mounted laterally at a right angle to the plane of the steel cantilever. A simulation model of control system has been produced and confirmed by experiments. Control algorithm is simple, efficient and ensures stability in operation.

The quality of a product is the main concern for customers. Hence, the objectives of this research are to determine factor that affect the inline quality inspection and improvement solutions of the quality inspection standard in the production process. Meanwhile, the scopes of the research are studying of all the factors that influence inline quality inspection and techniques to improve quality inspection standard. Hence, interviews had been carried out towards the respondents who work under the Quality Assurance Department and in charge of inline quality inspection. Through analyzing the chosen products which were cot sheet and latex cap, it can be said that worker, material, and machine are the most common factors that cause defects on the products. Therefore, the result of this research shows that the worker's training, maintenance and checking procedure of incoming raw materials are the main influence factors to overcome the problems occurred.

In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Providing amiable solutions to environmental problems as real achievement concerning what is happening presently demands potential long-term actions for development sustainability which will eventually foster good governance in Africa. Within this context, a renewable energy resource seems to negate all other options and remain the most reliable, effective as well as efficient solutions. The more reason why there is an in-depth connection between development sustainability and renewable energy. The methodology used in this paper was to interrogate primary and secondary sources which further centered on anticipated patterns of future energy use and consequent environmental impacts. It should be noted that the greatest challenges to development sustainability are environmental issues. For instance, in a region like Africa, issues like environment-related problems such as land degradation, water security, climate change, conflicts, deforestation, natural disasters, and urbanization are very important. Therefore, the paper discusses relations between renewable energy and development sustainability citing few cases with an illustrative example. Series of issues relating to environment, development sustainability as well as renewable energy, are examined from both current and future perspectives. The conclusions and recommendations of the paper offer potential solutions to present environmental problems as identified along with renewable energy technologies.

In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

N o . 1 lic supports renewable energy and energy effi ciency as priority GHG mitigation options (Curry et al., 2007), their economic potential to cut emissions over the mid-term is likely insuffi cient to prevent the more serious impacts of climate change. Many experts believe that every climate mitigation option, including CCS, must be employed to stabilize atmospheric concentrations at a viable level (Socolow, 2004). Figure 1 offers one illustration SUMMARY Carbon capture and sequestration (CCS) could become an important option to limit carbon dioxide emissions that are now causing global climate change. Interest in CCS has grown in North America, Europe, and Asia over the past 5 years. Selected challenges facing the technology include: developing a policy driver to incentivize deployment; defi ning a fl exible and adaptable regulatory framework; and funding large-scale demonstration projects to resolve technical and integration uncertainties as well as reduce high costs. Addressing these three challenges will help solve a fourth: public acceptability. Debate over the timing of CCS deployment is likely to continue, but it is clear that this climate mitigation option is critical to eventual stabilization of greenhouse gas concentrations in the atmosphere. This policy brief outlines high-level issues related to deployment of carbon dioxide capture and geological sequestration (CCS). It serves as a broad introduction to some of the key challenges that must be addressed for wide-scale deployment of CCS. Forthcoming papers in this WRI series will address selected issues in greater depth. CCS is one option for reducing anthropogenic greenhouse gas emissions that are now altering the global climate system. The CCS process starts with capturing carbon dioxide from power plants or other large industrial point sources, transporting it to suitable locations, and injecting it into underground reservoirs. Interest in CCS has grown in recent years as it would significantly reduce emissions from fossil fuels, which are expected to continue to meet the world's energy needs for decades to come due to their widespread availability and low cost. Challenging economic, technical, social, and institutional hurdles remain, however, before CCS can contribute signifi cantly to a larger climate solution. POTENTIAL FOR REDUCING CO 2 EMISSIONS The global community must reduce greenhouse gas emissions signifi cantly and quickly to prevent catastrophic climate change. According to the latest UN Intergovernmental Panel on Climate Change (IPCC) report, even to hold global mean temperature increases to 2.6°C (4.7°F) or less, global emissions should peak and then begin declining by 2020 (IPCC, 2007). Even then, more frequent heat waves, droughts, severe storms, sea level rise, and other signifi cant climate change impacts are expected. Currently, fossil fuels meet about 80 percent of global energy needs, and demand for energy will increase as populations and incomes rise. While surveys have indicated that the general pub-