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The purpose of this paper is to present a study on miniaturized instruments for analytical chemistry with a microplasma as the excitation source.

The atmospheric pressure glow microdischarge could be ignited inside a ceramic structure between a solid anode and a liquid cathode. As a result of the cathode sputtering of the solution, it was possible to determine its chemical composition by analyzing the emission spectra of the discharge. Cathodes with microfluidic channels and two types of anodes were constructed. Both types were tested through experimentation. Impact of the electrodes geometry on the discharge was established. A cathode aperture of various sizes and anodes made from different materials were used.

The spectroscopic properties of the discharge and its usefulness in the analysis depended on the ceramic structure. The surface area of the cathode aperture and the flow rate of the solution influence on the detection limits (DLs) of Zn and Cd.

Constructed ceramic structures were able to excite elements and their laboratory-size systems. During the experiments, Zn and Cd were detected with DLs 0.024 and 0.053 mg/L, respectively.

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 purpose of this paper is to report on the developments in manufacturing soft magnetic materials using laser powder bed fusion (L-PBF).

Ternary soft magnetic Fe-49Co-2V powder was produced by gas atomization and used in an L-PBF machine to produce samples for material characterization. The L-PBF process parameters were optimized for the material, using a design of experiments approach. The printed samples were exposed to different heat treatment cycles to improve the magnetic properties. The magnetic properties were measured with quasi-static direct current and alternating current measurements at different frequencies and magnetic flux densities. The mechanical properties were characterized with tensile tests. Electrical resistivity of the material was measured.

The optimized L-PBF process parameters resulted in very low porosity. The magnetic properties improved greatly after the heat treatments because of changes in microstructure. Based on the quasi-static DC measurement results, one of the heat treatment cycles led to magnetic saturation, permeability and coercivity values comparable to a commercial Fe-Co-V alloy. The other heat treatments resulted in abnormal grain growth and poor magnetic performance. The AC measurement results showed that the magnetic losses were relatively high in the samples owing to formation of eddy currents.

The influence of L-PBF process parameters on the microstructure was not investigated; hence, understanding the relationship between process parameters, heat treatments and magnetic properties would require more research.

The relationship between microstructure, chemical composition, heat treatments, resistivity and magnetic/mechanical properties of L-PBF processed Fe-Co-V alloy has not been reported previously.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This paper aims to present the basic assumptions for creation of social Fröhlich condensate and attract attention of other researchers (both from physics and socio-political science) to the problem of modeling of stability and order preservation in highly energetic society coupled with social energy bath of high temperature.

The model of social Fröhlich condensation and its analysis are based on the mathematical formalism of quantum thermodynamics and field theory (applied outside of physics).

The presented quantum-like model provides the consistent operational model of such complex socio-political phenomenon as Fröhlich condensation.

The model of social Fröhlich condensation is heavily based on theory of open quantum systems. Its consistent elaboration needs additional efforts.

Evidence of such phenomenon as social Fröhlich condensation is demonstrated by stability of modern informationally open societies.

Approaching the state of Fröhlich condensation is the powerful source of social stability. Understanding its informational structure and origin may help to stabilize the modern society.

Application of the quantum-like model of Fröhlich condensation in social and political sciences is really the novel and original approach to mathematical modeling of social stability in society exposed to powerful information radiation from mass-media and Internet-based sources.

The microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.

In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.

Microseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.

The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

Based on DNase I and reduced graphene oxide (rGO)-magnetic silicon microspheres (MNPS), a highly sensitive and selective fluorescent probe for the detection of PD-L1 was developed.

Here °C we present a feasibility of biosensor to detection of PD-L1 in lung tumors plasma. In the absence of PD-L1°C the PD-L1 aptamer is absorbed on the surface of graphene oxide modified magnetic nanoparticles °8rGO-MNPS°9 and leading to effective fluorescence quenching. Upon adding PD-L1°C the aptamer sequences could be specifically recognized by PD-L1 and the aptamer/PD-L1 complex is formed°C resulting in the recovery of quenched fluorescence.

This sensor can detect PD-L1 with a linear range from 100 pg mL⁻¹ to 100 ng mL⁻¹, and a detection limit of 10 pg•m⁻¹ was achieved.

This method provides an easy and sensitive method for the detection of PD-L1 and will be beneficial to the early diagnosis and prognosis of tumors.

The paper aims to promote the development of intelligent materials and the 4D printing technology by introducing recent advances and applications of additive layered manufacturing (ALM) technology of intelligent materials and the development of the 4D printing technology. Also, an arm-type ALM technology of shape memory polymer (SMP) with thermosetting polyurethane is briefly introduced.

This paper begins with an overview of the development and applications of intelligent materials around the world and the 4D printing technology. Then, the authors provide a brief outline of their research on arm-type ALM technology of SMP with thermosetting polyurethane.

The paper provides the recent developments and applications of intelligent materials and 4D printing technology. Then, it is suggested that intelligent materials mixed with different functional materials will be developed, and these types of materials will be more suitable for 4D printing.

This paper overviews the current developments and applications of intelligent materials and its use in 4D printing technology, and briefly states the authors’ research on arm-type ALM technology of SMP with thermosetting polyurethane.

This paper aims to present an approach based on electrical impedance tomography spectroscopy (EITS) for the determination of water and ice fraction in low-power applications such as autarkic wireless sensors, which require a low computational complexity reconstruction approach and a low number of electrodes. This paper also investigates how the electrode design can affect the reconstruction results in tomography.

EITS is performed by using a non-iterative method called optimal first order approximation. In addition to that, a planar electrode geometry is used instead of the traditional circular electrode geometry. Such a structure allows the system to identify materials placed on the region above the sensor, which do not need to be confined in a pipe. For the optimization, the mean squared error (MSE) between the reference images and the obtained reconstructed images was calculated.

The authors demonstrate that even with a low number of four electrodes and a low complexity reconstruction algorithm, a reasonable reconstruction of water and ice fractions is possible. Furthermore, it is shown that an optimal distribution of the sensor electrodes can help to reduce the MSE without any costs in terms of computational complexity or power consumption.

This paper shows through simulations that the reconstruction of ice and water mixtures is possible and that the electrode design is a topic of great importance, as they can significantly affect the reconstruction results.

The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.

A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.

It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.

There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).

This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.

The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.