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The main purpose of this study is to test the performance of the ink-jet printed microwave resonant circuits on Low temperature co-fired ceramics (LTCC) substrates combined with microfluidic channels for sensor applications. Normally, conductive patterns are deposited on an LTCC substrate by means of the screen-printing technique, but in this paper applicability of ink-jet printing in connection with LTCC materials is demonstrated.

A simple microfluidic LTCC sensor based on the microstrip ring resonator was designed. It was assumed the micro-channel, located under the ring, was filled with a mixture of DI water and ethanol, and the operating frequency of the resonator was tuned to 2.4 GHz. The substrate was fabricated by standard LTCC process, and the pattern of the microstrip ring resonator was deposited over the substrate by means of an ink-jet printer. Performance of the sensor was assessed with the use of various volumetric concentrations of DI water and ethanol. Actual changes in concentration were detected by means of microwave measurements.

It can be concluded that ink-jet printing is a feasible technique for fast fabrication of micro-strip circuits on LTCC substrates, including microfluidic components. Further research needs to be conducted to improve the reliability, accuracy and performance of this technique.

The literature shows the use of ink-jet printing for producing various conductive patterns in different applications. However, the idea to replace the screen-printing with the ink-jet printing on LTCC substrates in connection with microwave-microfluidic applications is not widely studied. Some questions concerning accuracy and reliability of this technique are still open.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

Meals on Wheels (MOW) support older people to live in their own homes and communities. The purpose of this paper is to explore MOW experiences from a multi-stakeholder level to inform and better equip this valuable service.

A qualitative approach was undertaken utilising semi-structured interviews and focus groups with current, former and potential MOW service users and MOW stakeholders.

Qualitative analysis explored MOW perspectives and experiences, highlighting a lack of MOW information and awareness, the importance of a client-centred approach the multiple roles of MOW and service transition.

This research explores MOW from the perspective of different groups directly involved in this community service, offering unique multi-stakeholder insights to understand and guide the future of this service.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

The purpse of this study is to examine sustainable technology development (STD) during the “Valley of Death” phase encountered by university startups undertaking intellectual property rights (IPR) commercialisation.

A comprehensive literature review was conducted after searching for relevant documents across multiple databases. Semi-structured interviews with university startup founders were also conducted as part of a qualitative case study.

This study resulted in two significant findings. First, the Valley of Death has been redefined in the specific context of IPR commercialisation by university startups. Second, the sustainable technology development framework (STDF) has been conceptualised to enhance the success rate of IPR commercialisation by university startups. The authors also identified three essential components of STD in the context of university startups: market development, technical efficiency and business sustainability.

This exploratory research involved a thorough literature analysis. Given that only one qualitative case study was conducted, data saturation was not achieved. Further empirical research is needed to validate the conceptualised STDF.

The validated STDF will be a useful tool for enhancing the success of IPR commercialisation by university startups.

While others have focused on innovating business models, this study focused on an underexplored area: the sustainability of technology development during the commercialisation of IPR by university startups during the Valley of Death phase.

The purpose of the paper is the simulation of nonuniform transmission lines.

The method involves a Magnus expansion and a numerical Laplace transform. The method involves a judicious arrangement of the governing equations so as to enable efficient simulation.

The results confirm an effective and efficient numerical solver for inclusion of nonuniform transmission lines in circuit simulation.

The work combines a Magnus expansion and numerical Laplace transform algorithm in a novel manner and applies the resultant algorithm for the effective and efficient simulation of nonuniform transmission lines.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

The purpose of this paper is to review possibilities of implementing ceramic additive manufacturing (AM) into electronic device production, which can enable great new possibilities.

A short introduction into additive techniques is included, as well as primary characterization of structuring capabilities, dielectric performance and applicability in the electronic manufacturing process.

Ceramic stereolithography (SLA) is suitable for microchannel manufacturing, even using a relatively inexpensive system. This method is suitable for implementation into the electronic manufacturing process; however, a search for better materials is desired, especially for improved dielectric parameters, lowered sintering temperature and decreased porosity.

Relatively inexpensive ceramic SLA, which is now available, could make ceramic electronics, currently restricted to specific applications, more available.

Ceramic AM is in the beginning phase of implementation in electronic technology, and only a few reports are currently available, the most significant of which is mentioned in this paper.

This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.

The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.

The optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.

The proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.

The fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.