In order to effectively utilize red mud and reduce its occupation of land resources as well as its impact on the environment, experiments were conducted to develop stabilized soil materials using nano-SiO2 synergistically modified red mud and to investigate the curing mechanism of stabilized soil. The unconfined compressive strength, microscopic morphology and curing mechanism of the red mud-based stabilized soil materials with different amounts of modified materials were investigated. The test results show that after 7-days curing, the unconfined compressive strength of red mud-based stabilized soil meets the compressive strength requirement of road base material when nano-SiO2, gypsum and cement are synergistically modified. In such case, the soil structure has the lowest fracture rate and the best structural compactness when the amount of nano-SiO2 is 1%; It is found that the needle-like and columnar calcium alumina in the modified red mud-based stabilized soil increases, and the binding energy of hydration product ions in the modified material is improved; The chemical curing mechanism of modified red mud-based stabilized soil includes hydration reaction, volcanic ash reaction, promotion effect of nano-SiO2, and enhancement effect of gypsum. On this base, a model of early start hydration process of red mud-based stabilized soil promoted by nano-SiO2 is established.

In recent years, graphene has received so much attention because of its superlative properties and its potential to revolutionize electronics especially in VLSI. This study analyzes the effect of single event upset (SEU) in SRAM cell which employs metal-oxide semiconductor type graphene nano-ribbon field effect transistor (MOS-GNRFET) and compares the results respectively with another SRAM cell designed in PTM 10nm FinFET node. Our simulation show there is a change in data stored in the SRAM after heavy ion strike. However, it recovers from radiation effects after 0.46 ns for GNRFET and 0.51 ns for FinFET. Since the degradation observed in Q and Qb of GNRFET SRAM are 2.7X and 2.16X as compared to PTM nano-MOSFET, we can conclude that GNRFET is less robust to single effect upset. In addition, the stability of SRAM can be improved by increasing the supply voltage VDD.

Lipid nanoparticles have an interesting part of drug delivery system. In this study, the modification of the convention nano-lipid based soybean lecithin was demonstrated. Ginger oil derived Zingiber officinale was used along with lecithin, cholesterol and span 80 to fabricate nano-lipid (GL nano-lipid) using thin-film method. Through TEM and confocal microscope, GL nano-lipid exposes the liposome- like morphology. The average size of the resultant nanoparticles was 249.1nm with monodistribution (PDI= 0.021). The ζ-potential of GL nano-lipid was negative as similar to as prepared nano-lipid based lecithin. GL nano-lipid express the highly stable over 60 days of storage at room temperature in term of size, ζ-potential. A shift of pH value from alkaline to acid was detected in lecithin nano-lipid, while with the incorporation of ginger oil, pH value of nano-lipid dispersion was around 7.0. Furthermore, due to the rich of shogaol-6 and other active compounds in ginger oil, the GL nano-lipid is endowed with intrinsic antibacterial feature. In addition, the sulforhodamine B (SRB) assay and live/dead imaging revealed the excellent biocompatibility of GL nano-lipid. Notably, GL nano-lipid was capable to carry the hydrophobic agents as curcumin and perform a pH-dependent release profile. A subsequent characterization are a suitable potential for drug delivery system.

A novel drive current model covering the effects of source/drain voltage (VDS) and gate voltage (VGS) and incorporating drift and diffusion current on the surface channel at the nano-node level, especially beyond 28nm node is presented. The effect of the diffusion current added is more satisfactory to describe the behavior of the drive current in nano-node MOSFETs, fabricated with the atomic-layer-deposition (ALD) technology. This breakthrough in model establishment can expose the long and short channel devices together. Introducing the variables of VDS and VGS, the mixed current model more effectively and meaningfully demonstrates the drive current of MOSFETs under the operation of horizontal, vertical, or mixed electrical field. In comparison between the simulation and experimental consequences, the electrical performance is impressive. The error between both is less than 1%, better than the empirical adjustment to issue a set of drive current models.

In this paper, the far field and near field optical responses of a gold nanoparticle are studied and simulated numerically. The electromagnetic field was excited by an electric dipole located near one end of the nanorod, which is used to model the emission of a quantum dot. Another excitation method was also simulated in which an incident plane wave is used. The excitation of dark plasmon modes of the gold nanorod is presented. The Poynting equation was solved numerically to study the influence of the gold nanorod on the dipole radiative power. In addition, the extinction cross section of the gold nanoparticle illuminated by the incident plane wave was calculated to estimate the amount of the scattered and absorbed light.

Poly(vinylidene fluoride) nanocomposites processed with different morphologies, such as porous and non-porous films and fibres, have been prepared with silica nanoparticles (SiNPs) of varying diameter (17, 100, 160 and 300 nm) which in turn have encapsulated perylenediimide (PDI), a fluorescent molecule. Structural, morphological, optical, thermal, and mechanical properties of the nanocomposites, with SiNP filler concentration up to 16 wt% were evaluated. Further, cytotoxicity and cell proliferation studies were performed. All SiNPs are negatively charged independently of the pH and more stable from pH 5 upwards. The SiNPs introduction within the polymer matrix increases the contact angle independently of the nanoparticle diameters and the smallest ones (17 nm) improve the PVDF Young modulus from 0.94 ± 0.04 GPa for the pristine polymer film to 1.05 ± 0.06 GPa. Varying filler diameter, physico-chemical, thermal and mechanical properties of the polymer matrix were not significantly affected. Finally, the SiNPs inclusion does not induce cytotoxicity in murine myoblasts (C2C12) after 72 h of contact and proliferation studies reveal that the prepared composites represent a suitable platform for tissue engineering applications, as they allow to combine the biocompatibility and piezoelectricity of the polymer with the possible functionalization and drug encapsulation and release of the SiNP.

Hybrid inorganic-polymer nanocomposites can be employed in diverse applications due to the potential combination of desired properties from both the organic and inorganic components. The use of novel bottom-up in situ synthesis methods for the fabrication of these nanocomposites is advantageous compared to top-down ex situ mixing methods, as it offers increased control over the structure and properties of the material. In this review, the focus will be on the application of the sol-gel process for the synthesis of inorganic oxide nanoparticles in epoxy and polysiloxane matrices. The effect of the synthesis conditions and the reactants used on the inorganic structures formed, the interactions between the polymer chains and the inorganic nanoparticles, and the resulting properties of the nanocomposites are appraised from several studies over the last two decades. Lastly, alternative in situ techniques and the applications of various polymer-inorganic oxide nanocomposites are briefly discussed.

Recent progress in the fabricating flexible electronics has been developed significantly due to the increased interest of the flexible electronics which can be applied to enormous fields not only to conventional electronic devices but to bio/eco electronic devices. Flexible electronics can be applied to wide range of fields such as flexible display, flexible power storage, flexible solar cells, wearable electronics and healthcare monitoring devices. Recently, flexible electronics are being attached on the skin and even implanted into human body to monitor the bio-signals and for treatment purpose. To improve the electrical characteristic and the mechanical properties of flexible electronics, nanoscale fabrications using novel nano-materials are required. Advanced in nanoscale fabrication methods allow construction of the active materials that can combine with the ultra-thin soft substrate to form flexible electronics with high performances and reliability. In this review, wide range of nanoscale fabrication methods for flexible electronics classified in either top-down or bottom-up approaches such as conventional photolithography, soft lithography, nanoimprint lithography, growth, assembly and chemical vapor deposition(CVD) will be reported with specific fabrication processes and results. Here, our aim is to introduce various fabrication methods that can be used to fabricate the flexible electronics.

One of the important issues being explored in Industry 4.0 is a collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e. it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show centimeter positioning accuracy with a position update frequency of 50 Hz.

Studies at the molecular, systemic, and epidemiological levels have shown that chronic metal exposure is linked to significant health consequences, including cancer, affecting hundreds of millions worldwide. Subtle and convoluted mechanisms underline metals' toxicity and carcinogenicity. The use of sensors for carcinogenic metals' trace detection is on the rise due to their selectivity, simplicity, and affordability. Biotechnology and microelectronics in the development of sensors have grown complementarily in recent years. This study offers a comprehensive overview of current research and advancements in developing sensors for detecting carcinogenic metals. Here, we have focussed on the developed biosensor platforms for group 1 carcinogens, i.e., arsenic, nickel, cadmium, chromium, and beryllium, along with their brief roles in human carcinogenesis. This review also looks at the importance of sensing such metal exposure in humans from a larger perspective, hoping to influence future research toward early prevention and treatment of illnesses like cancer.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a Sensor-Based Telerehabilitation and Telemonitoring System, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for remote monitoring of patients' progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings/

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest to incorporate technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, South of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture, pH, organic matter -OM, phosphorous -P2O5, potassium -K2O, sum of the exchange bases -SEB; and cation exchange capacity -CEC) which were determined from 20 soil samples taken in the different MZs. Additionally, validation was also performed using Rasch measures, which were defined based on the formula-tion of the objective and probabilistic Rasch model, integrating the information from the afore-mentioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. The use of ECa constitutes an efficient technological tool for implementing site-specific man-agement in viticulture, which allows for the improvement of decision-making processes by con-sidering the inherent spatial variability of the soil.

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.

: Microbial fuel cell (MFCs) devices utilise the metabolic processes of bacteria to generate electricity from various organic substrates. Due to the enormous amount of energy that organic waste produces, scientists are highly interested in advanced MFCs. MFCs serve as a multidisciplinary research platform at the engineering and natural sciences intersection. MFCs have various uses besides being used as energy sources, such as sensing capabilities. Despite showing considerable promise, only a few marine sediment MFCs have been deployed in real-world applications to supply current for low-power devices. It is now required to maintain track of the work being done by research groups worldwide and regularly compile significant discoveries due to the rising quantity of research publications. Review papers are a traditional beginning point for a literature review for new scholars. This review is a fast reckoner that directs readers to pertinent reviews and research publications detailing significant advances in microbial fuel cell research during the previous two decades. An overview of key advances in MFC research over the past two decades is provided as a quick reference in this review article. In addition, the report identifies research gaps that, if filled, could bring this technology closer to real-world use.

Over the past decade, Deep Learning (DL) had been applied in a large number of optical sensors’ applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors. Optical sensors are considered as promise technology for the modern intelligent sensing platforms. These sensors are widely used to process monitoring, quality prediction, pollution, defense, security and many other applications. However, they suffer major challenges such as the large generated data and low processing speed for that data and moreover the much cost of these sensor. These challenges can be mitigated by integrating deep learning system with the optical sensor technologies. This paper presents recent studies that integrate DL algorithms with optical sensors applications. This paper also highlights several of DL algorithms directions that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future related research development.

A sensitive electrochemical sensor based on AuPtPd trimetallic nanoparticles functionalized multi-walled carbon nanotubes coupled with chitosan modified glassy carbon electrode (GCE/CS/MWCNTs-AuPtPd) for the rapid detection of bisphenol A (BPA) was proposed. AuPtPd trimetallic nanoparticles were first assembled on MWCNTs to obtain MWCNTs-AuPtPd nanocomposite. Then the MWCNTs-AuPtPd was further dispersed on the surface of chitosan modified electrode surface to fabricate the GCE/CS/MWCNTs-AuPtPd sensor. Due to the superior catalytic properties of MWCNTs-AuPtPd and the good film formation of chitosan, the constructed sensor displayed good performances for BPA detection. The structural morphology of CS/MWCNTs-AuPtPd was characterized by many methods, such as SEM, UV-vis and TEM. Under the optimal conditions, the designed sensor showed a linear range from 0.05 to 100 µM for BPA detecting with a low detection limit of 1.4 nM. The designed sensor was further used to realize the determination of BPA in real samples and the recoveries ranged from 94.4% to 103.6%. The results demonstrated that the electrochemical sensor designed by CS/MWCNTs-AuPtPd nanocomposites was reliable and could be a useful tool for the monitoring of food safety.

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. Companies in the manufacturing sector are heavily integrating Industry 4.0 technologies, including cloud compu-ting, cyber-physical systems (CPS), big data analytics, horizontal and vertical system integration, the internet of things (IoT), and additive manufacturing (3d printing). The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations, allowing critical data collection on products, equipment performance, and maintenance. The sensors connect systems and machines, allowing them to communicate and track operations for enhanced operations. While numerous research has been conducted to show the role of sensors in industry 4.0, limited studies show how these innovations can leverage product design to maximize benefits and opportunities. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. This research paper employs a systematic bibliometric literature review (LRSB) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

The article presents the stages of modeling and simulation of a new design of a wheelchair with the option of moving up and down stairs. These analyzes were aimed at the synthesis of the de-sign parameters and parameters of the sensor and control systems. The simulation results were verified by experimentally testing the prototype.

Currently, large volumes of data are being collected on farms using multimodal sensor technol-ogies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored ef-ficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly ad-dressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future re-search is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

The purpose of this paper is to present advanced techniques in optical biodevices. Moreover different configurations involving the generation of fiber optical biosensors are described. To overcome some limitations of fiber optical biosensors, plasmonic phenomena proposed. In addition novel plasmonic phenomena have broaden researcher’s horizons in new discovering in terms of technology and application. As regards there are many challenges to detect ultra-low concentration samples with high sensitivity in real time. Researchers have always made great efforts to discover more effective methods. Throughout the paper SPR and LSPR as a powerful analysis instrument are introduced. Finally surveys the current practical performances of plasmonic sensors in detection of bio target are provided. As a result these devices demonstrate great potential in identifying target analytic due to their unique optical biosensors.

Since it was proposed, the Robot Operating System (ROS) has fostered solutions for various problems in robotics in the form of ROS packages. One of these problems is Simultaneous Localization and Mapping (SLAM), a problem solved by computing the robot pose and a map of its environment of operation at the same time. The increasingly availability of robot kits ready to be programmed and also of RGB-D sensors often pose the question of which SLAM package should be used given the application requirements. When the SLAM subsystem must deliver estimates for robot navigation, as is the case of applications involving autonomous navigation, this question is even more relevant. This work introduces an experimental analysis of GMapping and RTAB-Map, two ROS compatible SLAM packages, regarding their SLAM accuracy, quality of produced maps and use of produced maps in navigation tasks. Our analysis aims ground robots equipped with RGB-D sensors for indoor environments and is supported by experiments conducted on datasets from simulation, benchmarks and from our own robot.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

In this research paper we describe the development phase of a low-cost LoRa IoT solution applied to a kindergarten school with three years results. A set of sensors solution was developed in a LoRa communication board, battery powered, providing a simplified setup process. These sensors were used in order to measure temperature, humidity, luminosity, air quality and presence. Also, energy monitor solutions were integrated. The acquired data is transmitted and analysed for knowledge extraction, identifying savings and other related KPIs. From data, automatic saving actions were performed towards heating and cooling systems, lighting and a set of if-then actions were developed for automatic cost-saving actions, based on infrared signals to heating/cooling systems using some procedure of external command devices. This approach avoids the usage of proprietary vendor solutions in a flexible approach that can easily be deployed to any building facility. This is an important achievement since most of the building consumption is based on heating and cooling systems. In a three years test of the solution, the total energy consumption savings surpassed 20%

We present an integrable, sensor inlay for monitoring crack initiation and growth inside bondlines of structural carbon fiber reinforced plastic (CFRP) components. The sensing structures are sandwiched between crack stopping polyvinyliden fluoride (PVDF) and a thin reinforcing polyetherimide (PEI) layer. Good adhesion at all interfaces of the sensor system and to the CFRP material is crucial as weak bonds can counteract the desired crack stopping functionality. At the same time, the chosen reinforcing layer must withstand high strains, safely support the metallic measuring grids and possess outstanding fatigue strength. We show that this robust sensor system, which measures the strain at two successive fronts inside the bondline, allows to recognize cracks in the proximity of the inlay regardless of the mechanical loads. Feasibility is demonstrated by static load tests as well as cyclic long-term fatigue testing with up to 1,000,000 cycles. In addition to pure crack detection, crack distance estimation based on sensor signals is illustrated. The inlay integration process is developed with respect to industrial applicability. Thus, implementation of the proposed system will allow the potential of lightweight CFRP constructions to be better exploited by expanding the possibilities of structural adhesive bonding.

The use of New-age (Nano) Modified Emulsions (NME) for the stabilisation of marginal materials for use in the upper-pavement layers of roads have been proven in laboratories, through Accelerated Pavement Tests (APT) and in practice. In addition, material design methods have been developed based on the scientific analysis of granular material mineralogy and the chemical interaction with the binder to design a material compatible NME stabilising agent for naturally available (often marginal) materials. However, the introduction of any new disruptive technology in a traditionally well-established industry, such as the road construction industry, is usually associated with considerable resistance. This is especially relevant when the new technology enables the use of granular materials traditionally considered to be of an unacceptable quality in combination with relatively new concepts such as New-age (Nano) Modified Emulsions (NME). In practice, few road construction projects are without any problems. The introduction of new-technologies obviously makes it an easy target to blame for any non-related problem that may arise during construction. This article aims to assist in pre-empting, recognising, preventing and resolving material or non-material related construction problems through the correct identification of the cause of the problem and recommending the best, most cost-effective way to correct any deficiencies on site.

Preventing the occurrence of secondary caries serves as one of the significant issues in dental clinic, thus make it indispensable to improving the properties of conventional composite resin (CR) by developing a novel CR. In present study, two groups of experimental CRs loaded with different contents of fluoride-doped nano-zirconia fillers (25 wt% and 50 wt%) were fabricated. The surface topography, mechanical performance, fluoride release, antibacterial effect, aging property and cytotoxicity of the experimental CRs were evaluated subsequently. A uniform distribution of the F-zirconia fillers over the whole surface of resin matrix could be observed. The experimental CRs showed continuous fluoride release within 28 days, which was positively correlated with the content of F-zirconia fillers. Moreover, the amount of fluoride release increased in the acidic buffer. Addition of F-zirconia fillers could improve the color stability, wear resistance and microhardness of the experimental CRs, without reducing the flexure strength. Furtherly, the fluoride ions released continuously from the experimental CRs resulted in effective contact and antibacterial properties, while they showed no cytotoxicity. As a consequence, considerations can be made to employ this new kind of composite resin loaded with fluoride-doped nano-zirconia fillers to meet clinical requirements when the antimicrobial benefits are desired.

The application of dry lubrication in rolling contacts is challenging due to wear. In previous studies graphene platelets applied as dry lubricants on the surfaces of angular contact ball bearings demonstrated superior properties. These specific bearings, experiencing both rolling and spinning motion, create more severe conditions for dry lubricants. The applied graphene platelets have a thickness of few graphene layers in the nm range and a diameter in the µm range. Such platelets can be produced by mechanical exfoliation from graphite. To delve deeper into the lubrication effects, investigations were carried out using a milli tribometer. In the tests, a fixed steel ball is sliding against an oscillating counterpart under defined load. During the measurements, the applied load and tangential forces on the ball are recorded to calculate the friction. In our case, a steel ball with a diameter of 6 mm was used against graphene-based films on flat bearing surfaces (axial bearing washer). Comparative investigations included nano graphite particles and fullerene as dry lubricants, in addition to graphene platelets of various staple thicknesses. To increase the adhesion of the films to the surfaces, a pre-rolling process was implemented. Afterwards, the friction on the compressed films was measured. The results indicate that the pre-rolling process effectively reduces the friction of the system. After testing, the surfaces underwent analysis using laser scanning microscopy to assess the formed films, wear, and material transfer. It has been demonstrated that the pre-rolling process leads to the formation of a very thin compacted film with surface protective properties. On the ball as counterpart, the graphene platelets generate a transfer film on the contacting surface.

Soil salinity is seen as a major restriction for crop production, and with water scarcity this problem becomes more complicated. Mulching is crucial to salinity dynamics management by decreasing evaporation with improving the soil’s characteristics. Using chitosan as an eco-friendly biostimulant can enhance plant defense genes during different abiotic stresses. Recently, agricultural research has recognized nanoparticles as a pioneer material due to their distinctive physicochemical features. Therefore, a lysimeter experiment was conducted to investigate the interactive effects of mulching (UNM: un-mulched, WPM: white plastic, RSM: rice straw and SDM: sawdust) and chitosan foliar application (Ch0: control, Ch1: 250 mg chitosan L-1, Ch2: 125 mg nano chitosan L-1, and Ch3: 62.5 mg nano chitosan L-1) on the biochemical soil characteristics and common beans productivity under salt affected soil conditions. Organic mulching (RSM and SDM) treatments significantly improved the soil's organic carbon, available nutrient content, and total count of bacteria. WPM treatment lowered soil EC to 6.63 dS m-1 and increased soil water content to 34.13%. The application of Ch3 caused considerable increases in the plant height, shoot dry weight, root dry weight, grain yield, and nutrient content in the seed. The total fungi count in the soil and Na% in the seed was significantly decreased due to chitosan foliar applications. Moreover, the interactive effect of different mulch materials plus foliar by chitosan applications gave a statistically similar seed yield in both seasons. Overall, this study revealed the potential of the mulching treatments and foliar application of nano chitosan in improved biochemical soil characteristics and common bean productivity under saline soil conditions.

In this paper we introduce the concept of Nano Beta Closure, Nano Beta Interior and study the relationship between them also study some of their properties. Based on these notions we introduced the topological Approach to Decision Making and Its Application in the Medical Field" which describes the use of topological methods in decision-making, specifically through the utilization of nano beta closure and nano beta interior. These mathematical tools are applied in the medical field to improve diagnostic accuracy and aid in treatment planning. The authors demonstrate the effectiveness of this approach through a case study on the diagnosis of Chronic Kidney. The results show that the topological approach using nano beta closure and nano beta interior provides a reliable and accurate method for medical decision-making. Finally, we introduce a medical application for disease groups patients of with chronic kidney. To identify the main causative agent of kidney disease. where using this approach, the medical team can identify the specific areas of the kidney that are affected by the infection, as well as the extent of damage and any potential complications. This information is used to create an individualized treatment strategy that is customized to meet the specific requirements of the patient. and medical history. We write an algorithm that can be a helpful tool for doctors to utilize in diagnosing infection disease Chronic Kidney infections. By utilizing this approach, clinicians can provide accurate diagnoses, develop effective treatment plans, and ultimately improve patient outcomes. finally, we show that fractals hold significant importance in future nano topological generation of the human factor. Their unique properties and self-similar patterns offer insights into the complex organization of biological systems and provide a foundation for designing advanced Nanosystems.

Diabetes mellitus is a cluster of metabolic diseases characterized by hyperglycemia. Since this epidemic growing worldwide, the usage of phototherapeutics to manage diabetes is vital. Coccinia grandis (Aka ivy gourd) belongs to cucumber family is identified as a most effective herb, especially for diabetes and many other disease conditions. Hence, an instant herbal porridge cube incorporating Coccinia grandis leaf powder was produced and sensory acceptability of the porridge was evaluated using trained panelists. The bioactive activity of Coccinia grandis leaf powder showed that 66.77±0.74 mg/g GAE of Total Phenolic Content, 27.65±6.79% of DPPH radical scavenging activity, 5.72% of alpha-amylase inhibition activity and 59.64% of alpha-glucosidase inhibition activity. The effect on postprandial blood sugar levels after consuming the developed porridge was measured using healthy adults and it was observed there was no blood sugar elevation during the 2 hours of time. As a further development, Coccinia grandis leaf extract was nano-encapsulated with food grade Sodium alginate and it was incorporated into the same porridge as a safe delivery method of bioactive compounds. In-vitro digestion assay was conducted and the results show that alpha-amylase inhibition activity for the gastric phase and alpha-glucosidase inhibition activity for the intestinal phase was higher in nano-encapsulated porridge. The study concludes that, consuming nano-encapsulated Coccinia grandis leaf extract with porridge is more beneficial for diabetic patients.

Organophilic nano clay is the composition of mostly clay and organic molecules designed to improve various materials' physical and chemical properties. Permanent deformation in asphalt pavements is primarily the result of inadequate compaction, excessive loading, and high temperatures. Rutting can result in aquaplaning, leading to accidents and costly maintenance and rehabilitation works that drain the economy. Such behavior of asphalt pavements may be attributable to poor selection of aggregate, asphalt binder, or substandard asphalt binders. This study used different percentages of organophilic nano clay ranging from 3.0% - 5.0% with two penetration grade bitumen, i.e., N.R.L 60-70 & N.R.L 80-100. Adding organophilic nano clay to asphalt binder significantly reduced the rutting potential under cyclic loading at high temperatures (55 °C). Ten modified formulations and two virgin bitumen specimens were prepared. The modified binders' viscosity and rutting were tested using the Rotational Viscometer and Wheel Tracker Test (W.T.T.), respectively. The results were analyzed using software for Statistical Analysis. It was recorded that organophilic nano clay-modified bitumen significantly affected rutting to interpret the results. The results indicated that rutting decreased and viscosity increased in all nano clay-modified bitumen samples, with a 4.5% O.N.C modified binder showing the most significant improvement.

The present work is focused on functionalizing Montmorillonite (Mt) with poly dimethyl siloxane (PDMS) to develop a hybrid membrane reactor for the removal of Co (II) and Ni (II) ions from model wastewater samples. The specific surface area of the novel material was found to be 444 m2 /g. X-Ray Diffraction (XRD) and Fourier Transform Infra-Red (FTIR) spectra reveal the formation of exfoliated composite material. The efficiency of this membrane material was determined by subjecting it to continuous filtration experiments. It was able to remove 50% of Co (II) and 75% of Ni (II) ions, respectively, in one cycle of filtration in less than three minutes of operation with a permeate flux rate of 675 L/m2/H and 975 L/m2/H respectively, for both the ions.

This work presents the production and incorporation of calcium hydrolyzed nano-solutions at three concentrations (1, 2, and 3 wt.%) in alkali-activated gold mine tailings (MTs) from Arequipa, Perú. A first activator solution of sodium hydroxide (NaOH) at 10M was used. The calcium hy-drolyzed nano-solutions acted as a secondary activator and as additional calcium resource for the alkali-activated materials (AAMs) based on the low-calcium gold MTs. High-resolution trans-mission electron microscopy/energy-dispersive x-ray spectroscopy (HR-TEM/EDS) analyses were carried out to characterize the morphology, size, and structures of the calcium hydrolyzed na-noparticles. Fourier transform infrared (FTIR) analyses then were used to understand the chemical bonding interactions in the calcium hydrolyzed nanoparticles and in the AAMs. Scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS) and Quantitative X-ray diffraction (QXRD) were performed to study the structural, chemical, and phase compositions of the AAMs; uniaxial compressive tests evaluated the compressive strength of the reacted AAMs; and Nitrogen adsorption-desorption analyses measured the porosity changes at nanostructure level in the AAMs. The results indicate that each increase in the concentration of the calcium hydrolyzed nano-solution had a direct/proportional effect on the mechanical properties of the AAMs samples. The AAMs with 3 wt.% calcium-hydrolyzed nano-solution had the highest compressive strength value of 15.16 MPa, which represented an increase of 62% compared to the original system without nanoparticles and aged under the same conditions of 70°C for seven days. These results provide useful information about the positive effect of calcium-hydrolyzed nanoparticles on gold MTs and their conversion into sustainable building materials through alkali activation.

Of course, now we know that quantum mechanics has been a fundamental structure of our world since our universe came into being. However, it has been only a century since the experimental and theoretical discovery of quantum mechanics and its extensions into many implications and applications. In particular, there are implications across many disciplines that most likely will affect education, health, security, etc. Examples are given of the need for starting education as early as possible in schools, the use of nano-robots to deliver drugs targeted to specific molecular sites, to developing new cryptographic systems to safeguard our privacy.

The size of TiO2 (either nanometric or micrometric) can significantly affect both its photocatalytic and photoelectrochemical properties, thus altering the photooxidation of organic pollutants in air or water. The purpose of this work is to give an account of the photoelectrochemical and photocatalytic features of some nano- and micro-sized TiO2 commercial powders towards a model reaction, the photooxidation of acetone. Cyclic voltammograms (CV) of TiO2 particulated electrodes under UV illumination experiments were carried out in either saturated O2 or N2 solutions for a direct correlation with the photocatalytic process. In addition, the effect of different reaction conditions on the photocatalytic efficiency under UV light in both aqueous and gaseous phases was also investigated. CV curves with the addition of acetone under UV light showed a negative shift of the photocurrent onset, confirming the efficient transfer of photoproduced reactive oxygen species (ROSs), e.g., hydroxyl radicals, or holes to acetone molecules. The photocatalytic experiments showed that the two nano-sized samples exhibit the best photocatalytic performance. The different photoactivity of the micro-sized samples is probably attributed to their morphological differences, affecting both the amount and distribution of free ROSs involved in the photooxidation reaction.

Evaporation studies of water using classical molecular dynamics simulations are largely limited due to its high computational expense. We aim at addressing the computational issues by developing a coarse grain model for evaporation of water on solid surfaces by combining four water molecules into a single bead. Most commonly used mono atomic pair potentials like Lennard Jones, Morse, Mie and three body potential like Stillinger-Weber are optimized using a combination of Genetic algorithm and Nelder-Mead algorithm. Among them, Stillinger-Weber based model shows excellent agreement of density and Enthalpy of vaporization with experimental results for a wide range of temperatures. Further, the new water model is used to simulate contact angle of water and thin film evaporation from surfaces with different wettabilities.

Amorphous ferrite-type rare-earth (RE) substituted garnets and garnet-oxide nanocomposite layers are prepared on clear glass substrates by using RF magnetron sputter-deposition process. By using a combination approach employing custom-built spectrum-fitting software in conjunction with Swanepoel’s envelope method, the spectral dispersion function of optical constants and the layer thicknesses are derived accurately from the transmission spectra of the as-deposited samples. The effects of excess metal-oxides added to the base material systems during the co-deposition process are found to affect the refractive index and the optical absorption coefficients of garnet-oxide composites. A number of optical constant datasets are presented, enabling the experimentalists to design nanophotonic or integrated-optics devices employing these functional materials.

Aiming at the damage detection and continuous monitoring of high efficiency composite structure, the method of carbon nano sensor embeded in the 3-D braided composite material specimen was put forward and the method of damage location for 3-D braided composite material was described. In this paper, the mechanical and electrical characteristics of carbon nano-fiber were analyzed. Considering the massive data of the health monitoring of composite processing, to ensure that the system has good real-time performance, quaternary matrix Singular Value Decomposition algorithm is proposed to simplify data and improve the efficiency of the algorithm. The existence of injury is determined and the damage location is calculated by comparing with key features of healthy specimen. Experimental results prove the method is feasible and effective.

A new class of nano-cube core-shell heterostructures containing Ag coating on the top of WO₃ was fabricated. Physical vapor deposition was used to produce WO₃ based nano-heterostructures. All kind of wet toxic chemical process was avoided to make the process simple and contaminant free. Sputtering of WO₃ and a subsequent thermal annealing process was done to create nano-cubes of WO₃. After that, sputtering of Ag was performed to form the Ag-WO₃ core-shell nano-heterostructures (CSNH). The CSNHs were characterized using field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD) and UV-vis spectroscopy. The morphologies, elemental analysis, interfaces, crystallinity, phases, and chemical compositions were analyzed. The bottom-up growth of WO₃ nanocubes was studied using different time periods at 900°C. Ag coating was also studied before and after annealing. Finally, an optical property (band gap) was also analyzed using Tauc plot derive from absorption spectra. The tailoring the band gap of WO₃ from ~2.9eV to ~ 2.45 eV was observed while Ag-WO₃ CSNH formed.

Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of 4 different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.

Three-dimensional flexible piezoresistive porous sensors have been used in health diagnosis and wearable devices. By utilizing 3D printed sacrificial mold casting with highly conductive polymer composites, porous sensors with high conductivity and complex structures were achieved. Traditional conductive polymer composites are usually heated using external heat sources, which are time and energy consuming. However, in this study, conductive porous sensors with triply periodic minimal surface (TPMS) structures were manufactured using self-resistance electric heating by utilizing the conductive nature of the polymers. The porous structures were designed based on Diamond (D), Gyroid (G), and I-WP (I) unit cells of the TPMS. The use of self-resistance electric heating has improved the compression performance and piezoresistive response of the structures. Among these structures, the D-based structure exhibited the maximum response strain (61 %), with a corresponding resistance response value of 0.97, which increased by 10.26 % compared to that of the structures heated by using the external heat sources. This study offers a new perspective on designing and optimizing porous materials with specific functionalities, particularly in the realm of flexible and portable piezoresistive sensors.

Membrane-based sensors (MePSs) exhibit remarkable precision and sensitivity in detecting pressure changes. MePSs are commonly used to monitor catalytic reactions in solution, generating gas products crucial for signal amplification in bioassays. They also allow catalyst quantification by indirectly measuring the pressure generated by the gaseous products. This is particularly interesting to detect enzymes in biofluids associated with disease onset. To enhance the performance of a MePS, various structural factors influence membrane flexibility and response time, ultimately dictating the device's pressure sensitivity. In this study, we fabricated MePSs using polydimethylsiloxane (PDMS) and investigated how structural modifications affect the Young's modulus (E) and residual stress (σ0) of the membranes. These modifications have a direct impact on the sensors' sensitivity to pressure variations, observed as a function of the volume of the chamber (Σ) or of the mechanical properties of the membrane itself (S). MePSs exhibiting the highest sensitivities were then employed to detect catalyst quantities inducing the dismutation of hydrogen peroxide, producing dioxygen as gaseous product. As a result, catalase enzyme was successfully detected using these optimized MePSs, achieving a remarkable sensitivity of (22.7 ± 1.2) µm/nM and a limit of detection (LoD) of 396 pM.

Seafood, especially from the ocean, is now seen as a greener and more sustainable source of protein causing an increase in its demand. This has also led to people making choices towards seafood as a replacement of carbon intensive protein sources. As a result, the demand for seafood is growing, and the Aquaculture industry is required to increase their produce while keeping the produce safe and sustainable. There are many challenges faced by the aquaculture industry in meeting these increased demands. One such challenge is the presence of harmful algal blooms (HAB) in the ocean which can have a major impact on aquatic life. In this paper we look at the impact of this challenge on aquaculture and the mitigating strategies. We will focus on Abagold Limited, a land-based marine aquaculture business that specializes in large scale production of abalone (Haliotis midae) based in Hermanus, South Africa. HABs are considered a threat to commercial scale abalone farming along the South-African coastline and requires continuous monitoring. The most recent HAB was in February-April 2019, the area experienced a severe red-tide event with blooms of predominantly Lingulodinium polyedrum. We present some of the mitigation strategies employing digital technologies for future proofing the industry.

Electrochemical sensors have been used for many decades. Yet, modeling of such sensors used in electrolysis mode is poorly documented, especially in the case parallel action of multiple gases. Those ones are of great interest since they constitute the first brick to bring information on natures and concentrations of a gaseous mixture composition thanks to grey box modeling of sensors array for example. Based on Butler Volmer’s equations, a model assuming parallel reactions at gold cathode has been introduced in this article and confronted to experimental results. Establishment of the model is based on the extraction of 3 variables: the charge transfer coefficient “α”, the reaction order γ, and the reaction constant rate k0. Tests performed without pollutants and with different concentrations of oxygen could be nicely fitted by the model. Then, the influence of polarization current on the 3 variables of the model has been evaluated showing a clear influence on the constant rate and the reaction order. Also, increasing the polarization current enabled us to get selectivity to oxidant gases. Similarly, the effect of the oxygen concentration was evaluated. Results showed that, in this case, the charge transfer coefficients “α” obtained for oxidant gases is quite different that the ones obtained in the polarization current varying conditions. Therefore, the model will be interesting in situations where polarization current and oxygen content are not varied together. Variation of polarization current can be quite interesting to get increased information for multivariate analysis purpose in constant oxygen content situations. Besides, other parameters have are required for applications in which the oxygen content is bound to change like exhaust gases or combustion.

This study investigates novel ferrocene-based electrochemical sensors for metal cation detection through the design, synthesis and characterisation of ferrocene derivatives. Specifically, the re-search determines the redox potentials of ferrocene versus decamethylferrocene to provide in-sight into the redox potential variations between them. The investigation also examines how electrochemical oxidation of the ferrocene moiety can modulate host affinity for transition metals cations via effects such as electrostatic interactions and changes to coordination chemistry. Metal ion coordination to receptors containing functional groups like imine and quinoline is explored to elucidate selectivity mechanisms. These findings advance the fundamental understanding of ferrocene electrochemistry and host-guest interactions, supporting the development of improved cation sensors with optimised recognition properties, sensitivity, and selectivity. Overall, the work lays the necessary groundwork for applications in analytical chemistry and sensor technologies through customised ferrocene-derived materials.

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as “walking” or “non-walking”. One algorithm had generic thresholds, while the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82 respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

Infrared sensors incorporating suspended zinc oxide (ZnO) pyroelectric films and thermally-insulated silicon substrates are fabricated using conventional MEMS-based thin-film deposition, photolithography, and etching techniques. The responsivity of the pyroelectric film is improved through annealing at 500℃ for 4 h. The voltage response of the fabricated sensors is evaluated experimentally for a substrate thickness of 1 µm over a sensing range of 30 cm. The results show that the voltage signal varies as an inverse exponential function of the distance. A positioning system based on three infrared sensors is implemented in LabVIEW. It is shown that the position estimates obtained using the proposed system are in excellent agreement with the actual locations. In general, the results presented in this study provide a useful source of reference for the further development of MEMS-based pyroelectric infrared sensors.

At present, aeroponic control is mainly regulated according to the environmental factors of aeroponics, such as the temperature, humidity, light intensity, carbon dioxide concentration, pH, and electrical conductivity (EC) of the nutrient solution. There is a lack of knowledge available in the literature for an aeroponic control system for plant growth information as the primary feedback signal. Therefore, a fuzzy logic-controlled aeroponics system was developed for the growth of lettuce as a feedback signal. After designing, the effectiveness test of system was performed with experiment having three treatments T1 (180C), T2 (220C) and without control (CK). The results show an increasing trend in shoot length, root length, and shoot area over time in all treatments. In terms of shoot area, the maximum of 345 cm² was observed in the T1 treatment, while the CK treatment had a minimum of 241.8 cm². Statistical analysis indicated that the T1 and T2 treatments were not significantly different, but both were significantly different from the CK treatment. The study demonstrates the effectiveness of the fuzzy logic control system and an 18°C nutrient solution temperature, resulting in improved lettuce growth and reduced root-to-shoot ratio. The findings suggest the potential of using fuzzy logic control in aeroponics for optimized cultivation outcomes.

The condition of surface transportation infrastructure directly affects the economic health of a nation. However, it is difficult to justify the large sums of money needed to extend current methods to monitor all the multimodal infrastructure. The convergence of connected vehicle and cloud computing technologies presents an opportunity to automate the collection of ride quality and imagery data to continuously assess the condition of all roadways and railways. This paper presents several perspectives to help policy and standardization initiatives promote adoption.

Rapid growth of IoT applications and their interference in our daily lives led to many different IoT devices which generates enormous data. The IoT devices’ resources are very limited, so storing and processing IoT data in the devices is very inefficient. Several resources of cloud-computing are efficiently used to handle some IoT resources issues. While using resources in the cloud centers cause some other issues, like latency in the IoT applications, which are time-critical. Thus, the technology of edge cloud has evolved recently. This technology permits storage and data processing at the network edge. This paper studies edge computing in-depth for timeless sensitive devices in IoT. In-depth, cutting-edge IoT computing systems (ECAs-IoT) are evaluated and characterized in this paper according to numerous criteria, such as information placement, improvisation facilities, reliability, and data visualization. Moreover, according to distinctive properties, the paper aims at comparing each structure in detail. The paper also highlights the significant limitations of the new ECAs-IoT and recommends solutions to them. The studies also introduce and propose solutions to some of the most important restrictions of the current ECAs-IoT. Consequently, in the edge computing domain, this survey outlines the IoT implementations. Lastly, with the use of IoT implementations for ECAs-IoT, the paper suggests four distinct scenarios.

This mini-review critically summarizes the knowledge of imprinted polymer-based electrochemical sensors, for the detection of pesticides, metal ions and waterborne pathogenic bacteria, focusing on the period the last 5 years (citation of 78 papers published in 2017-2021, ie 63% of total citations). MIP-based electrochemical sensors exhibit low limit of detection, high selectivity, high sensitivity and low cost. Herein, we focused on the timely topics of water pollution by organics, inorganics and micro-organisms represented by pesticides, metal ions and bacteria, respectively. We put the emphasis on the design of imprinted polymers and their composites and coatings by radical polymerization, oxidative polymerization of conjugated monomers or sol-gel chemistry. Whilst most imprinted polymers are used in conjunction with differential pulse or square wave voltammetry for sensing organics and metal ions, electrochemical impedance spectroscopy (EIS) appears as the chief technique for detecting bacteria. This successful combination of EIS and imprinting technology should be harnessed in the coming years in the case of bacteria. Interestingly, bacteria are not always probed by bacteria-imprinted polymers; we report here their detection by monitoring specific (macro)molecules that reflect bacterial activity, for example quorum sensing signaling molecules or flagella proteins. If much has been developed in the past decade with glassy carbon or gold electrodes, it is clear that carbon paste electrodes of imprinted polymers are more and more investigated due to their versatility. Shortlisted case studies were critically reviewed and discussed; clearly a plethora of tricky strategies of designing selective electrochemical sensors are offered to “Imprinters”. We anticipate this review will be of interest to experts and new comers in the field who are paying time and effort combining electrochemical sensors with MIP technology.

In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes a first approach for the generation of fake electrocardiograms (ECGs) based on Generative Adversarial Networks (GANs) with the objective of anonymizing users’ information for privacy issues. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. As GANs are mainly exploited on images and video frames, we are proposing general raw data processing after transformation into an image, so it can be managed through a GAN, then decoded back to the original data domain. The feasibility of our transformation and processing hypothesis is primarily demonstrated. Next, from the proposed procedure, main drawbacks for each step in the procedure are addressed for the particular case of ECGs. Hence, a novel research pathway on health data anonymization using GANs is opened and further straightforward developments are expected.

Increased smart devices in various industries is creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction should be useful for decision maker to drive the business decision; hence Binary classification is performed, and business case analysis is performed. Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset. Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

We present an Arduino-based automatic robotic system which is used for cutting grass or lawns, mostly healthy grass which needs to cut neatly like in a public park or a private garden. The purpose of this proposed project is to design a programmable automatic pattern design grass cutting robot with solar power which no longer requires time-consuming manual grass-cutting, and that can be operated wirelessly using an Android Smartphone via Bluetooth from a safe distance which is capable of cutting the grass in indeed required shapes and patterns; the cutting blade can also be adjusted to maintain the different length of the grass. The main focus was to design a prototype that can work with a little or no Physical user interaction. The proposed work is accomplished by using an Arduino microcontroller, DC geared Motors, IR obstacle detection sensor, motor shield, relay module, DC battery, solar panel, and Bluetooth module. The grass-cutting robot system can be moved to the location in the lawn remotely where the user wants to cut the grass directly or in desired patterns. The user can press the desired pattern button from the mobile application, and the system will start cutting grass in the similar design such as a circle, spiral, rectangle, and continue pattern. Also, with the assistance of sensors positioned at the front of the vehicle, an automatic barrier detection system is introduced to enhance safety measurements to prevent any risks. IR obstacle detector sensors are used to detect obstacles, if any obstacle is found in front of the robot while traveling; it avoids the barrier by taking a right/right turn or stop automatically appropriately, thereby preventing the collision. Also, the main aim of this project is the formation of a grass cutter that relieves the user from mowing their own grasses and reduces environmental and noise pollution. The proposed system is designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (Simple and Pattern based grass cutting with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We note that the systems proposed can be implemented on a large scale under real conditions in the future, which will be useful in robotics applications and cutting grass in playing grounds such as cricket, football, and hockey, etc.

It is since long known, that SAW devices, resonators as well as delay lines, can be used as passive wireless sensors for physical quantities like temperature and pressure as well as gas sensors or ID-Tags. The sensors are robust, work passively without battery, can be applied at high temperatures and provide a high resolution. Nevertheless, if the devices should be readout wirelessly in an industrial environment, several constraints have to be taken into account, especially when more than one quantity or device needs to be measured at the same time. The paper addresses the challenges that have to be tackled when establishing multi-sensor-wireless-readout for industrial applications. Major issues here are the legal ISM-band regulations, as well as sampling time and costs, which impose severe restrictions to any system design. We describe several design approaches and their constraints. We have successfully designed sensors based on reflective delay lines that allow the parallel readout of four independent temperature sensors in the 2.45 GHz ISM-band. These devices have been fabricated, positively tested and demonstrate the applicability of SAW sensors for industrial applications.

In this work, a tailored extreme learning machine (ELM) algorithm to enhance the overall robustness of gas analyzer based on the tunable diode laser absorption spectroscopy (TDLAS) method is presented. The ELM model is tailored through activation function selection, input weight and bias searching, and cross validation method to address the analyzer robustness issues for industrial process analysis field application. The two particular issues are the inaccurate gas concentration measurement caused by the process gas background components variation, and the inaccurate spectra shift calculation caused by spectral interference. By using our algorithm, the concentration error is reduced by one order of magnitude over a much larger stream pressure and component range compared with that obtained by classical least square (CLS) fitting methods based on reference curves. Additionally, it is shown that with our algorithm, the wavelength shift accuracy is improved to less than 1 count over 1000 counts spectra length. In order to test the viability of our algorithm, a trace ethylene (C₂H₄) TDLAS analyzer with coexisting methane was implemented, and its experimental measurements support analyzing robustness enhancement effect.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data "from the ground up" and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers' hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

Micro nano bubbles (MNBs) can generate ·OH in situ, which provides a new idea for the safe and efficient removal of pollutants in water supply systems. However, the difficulty in obtaining stable MNBs causes the low efficiency of ·OH generation, which makes the inability to guarantee the removal efficiency of pollutants. This paper reviews the application research of MNBs technology in water security from three aspects: the generation process of MNBs in water, the generation rule of ·OH during MNBs collapse, and the control mechanism of MNBs on pollutants and biofilms. We found that MNBs generation methods are divided into chemical and mechanical (about 10 kinds) categories, and the instability of bubble size restricts the application of MNBs technology. The generation of ·OH by MNBs is affected by pH, gas source, bubble size, temperature, external stimulation. And pH and external stimulus have more influence on ·OH generation in situ than other factors. Adjusting pH to alkaline or acidic conditions and selecting ozone or oxygen as the gas source can promote the ·OH generation. MNBs’ collapse also releases a large amount of energy, which the temperature and pressure can reach 3000K and 5Gpa respectively, making it efficient to remove ≈90 % of pollutants (i.e., trichloroethylene, benzene, and chlorobenzene). The biofilm can also be removed by physical, chemical, and thermal effect. MNBs technology also has great application potential in drinking water, which can be applied to improve water quality, optimize household water purifiers, and enhance the taste of bottled water. Under the premise of safety, let people of different ages taste, finding that compared with ordinary drinking water, 85.7 % of people think MNBs water is softer, 73.3 % of people think MNBs water is sweeter. This further proves that MNBs water has a great prospect in the drinking water application. This review provides innovative theoretical support for solving the problem of drinking water safety.

Protein–nanoparticle hybridization can ideally lead to novel biological entities characterized by emerging properties, which can sensibly differ from those of the parent components. Herein, the effect of ionic strength on the biological functions of recombinant, His-tagged spermine oxidase (i.e., SMOX) was studied for the first time. Moreover, SMOX was integrated to colloidal surface active maghemite nanoparticles (SAMNs) by direct self-assembly, leading to a biologically active nano-enzyme (i.e., SAMN@SMOX). The hybrid was subjected to an in-depth chemical-physical characterization highlighting that protein structure was perfectly preserved. The catalytic activity of the nanostructured hybrid (SAMN@SMOX) was assessed by extracting kinetics parameters using spermine as substrate and compared to the soluble enzyme as a function of ionic strength. Results revealed that the catalytic function was dominated by electrostatic interactions and that they were drastically modified upon hybridization with colloidal ɣ-Fe2O3. Noteworthy, the affinity of SMOX toward spermine was significantly higher for the nanohybrid at low salinity. The present study supports the vision of using protein–nanoparticle conjugation as a mean for modulating biological functions.

Significant amount of oil remains in the reservoir after primary and secondary operations and to recover the remaining oil, enhanced oil recovery (EOR) can be applied as one of the feasible options remaining nowadays. In this study new nano polymeric materials have been prepared from purple yam and cassava starches. The yield of purple yam nanoparticles (PYNPs) was 85% and that of cassava nanoparticles (CSNPs) is 90.53%. Synthesized materials were characterized through particle size distribution (PSA), zeta potential distribution, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and transmission electron microscopy (TEM). The performance of PYNPs in recovering oil was better than CSNPs as found from recovery experiments. Zeta potential distribution results confirmed the stability of PYNPs over CSNPs (-36.3 mV for PYNPs and -10.7 mV for CSNPs). The optimum concentration for these nanoparticles has been found from interfacial tension measurements and rheological properties and it was 0.60 wt.% for PYNPs and 0.80 wt.% for CSNPs. More incremental recovery (33.46%) has achieved for the polymer that contained PYNPs in comparison to the other nano-polymer (31.3%). This opens the inception for a new technology for polymer flooding that may replace the conventional method that depends on partially hydrolyzed polyacrylamide (HPAM).

We synthesized iron(II)-triazole spin crossover compounds of the type [Fe(atrz)3]X2 and incorporated and deposited them on electrospun polymer nanofibers. In view of possible applications, we chose iron(II)-triazole-complexes that are known to exhibit spin crossover close to ambient temperature. Therefore we used the complexes [Fe(atrz)3]Cl2 and [Fe(atrz)3](2ns)2 (2ns = 2Naphthalenesulfonate) and analyzed them with IR-, UV/Vis and Mossbauer spectroscopy and with a SQUID magnetometer. The synthesized complexes were then in one attempt deposited on fibers of polymethylmethacrylate (PMMA) and in another attempt incorporated into core-shell like PMMA fiber structures. This was done via two different electrospinning methods. Deposition was performed by uniaxial electrospinning and incorporation was performed by coaxial electrospinning. The obtained polymer-complex-composites were then further investitgated by the same methods as the pure complexes as well as by SEM pictures and EDX measurements. The analysis by UV/Vis spectroscopy, Mössbauer spectroscopy and temperature-depended magnetic measurements with the SQUID magnetometer showed that the spin crossover properties were maintained and were not changed after the electrospinning processes and that the complexes were not harmed during the procedure.

The physicalchemistry properties and crystal structure of were nano-ZrO₂+3mol.%Y₂O₃ determined. The kinetics of the formation of H₂ as a result of the decomposition of H₂O on the surface of nano-ZrO₂+3mol.%Y₂O₃ was studied. Effects of adsorption and desorption process on ZrO₂+3 mol.%Y₂O₃ nanoparticles were studied at different (T=400÷1000⁰C) temperature. The study of H₂ in thermal processes at nano-ZrO₂+3 mol.%Y₂O₃ system increased. Such an increase in H₂ generation in comparison with a pure H₂O as thermal processes had formedactive centers for H₂O decomposition on the surface of the catalyst at the expense of δ-electrons emitted on the surface of nano-ZrO₂+3 mol.%Y₂O₃. This showed that the dimensions of the studied nanoscale particles systems are comparable to the free running distance of energy carriers generated by of nano-ZrO₂+3 mol.%Y₂O₃ as a result of thermal processes. These results are promising for hydrogen generation by waer spliting in near future.

This paper seeks to optimize the mechanical and durability properties of ultra-high performance concrete (UHPC). To meet this objective, concrete specimens were manufactured by using 1,100 kg/m3 of binder, water/binder ratio 0.20, silica sand and last generation of superplasticizer. Silica fume, metakaolin and two types of nano silica were used for improving the performances of the concrete. Additional mixtures included 13mm long OL steel fibers. Compressive strength, electrical resistivity, mercury intrusion porosimetry tests and differential and thermogravimetric thermal analysis were carried out. The binary combination of nano silica and metakaolin, and the ternary combination of nano silica with metakaolin and silica fume, led to the best performances of the UHPC, both mechanical and durable performances.

We fabricated the nano-aperture plasmonic platforms for single molecule detection and other various applications such as infrared thermal emission device. The nano-apertures including the nanopores on the pyramid, and the nano-slits on the Au flat membrane were fabricated using a Ga ion focused ion beam drilling technique, followed by high energy electron beam irradiations dependent upon the electron beam current density. The nanopores with a few nanometer size and the nanoslit array with order of ~ 100 nm width were fabricated. Optical characteristics for the various nanoslits were examined dependent upon the slit opening width and sample thickness. The broad emission spectra from the (7x 7) slit array are obtained from spp-mediated emission in the visible and infrared region. A sharp strong infrared emission peak is also obtained due to Au nanoparticle. The fabricated Au platform can be utilized as single molecule sensor and infrared thermal emission device.

The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

This review delves into the growing field of flexible and stretchable strain sensors utilized in human gait analysis, focusing on determining knee bending angle and muscle activities when a human subject walks. Recent advancements have enabled these sensors to accurately capture biomechanical parameters while accommodating the body's natural movements. We examine the principles underlying these sensors' design and fabrication techniques, emphasizing their flexibility, stretchability, and biocompatibility for seamless integration into wearable systems. Through critical evaluation of existing methodologies, we assess the reliability and validity of these sensors in quantifying gait parameters. Furthermore, we discuss this technology's clinical and research implications, highlighting its potential in personalized rehabilitation and sports performance optimization. We provide an overview of flexible strain sensors and their application for analyzing human walking gait, especially for observing walking gait in rehabilitation programs. First, we present the basic working principles of various flexible strain sensors. Second, by the application of flexible strain sensors as walking gait monitoring. Finally, current challenges and future opportunities in this research area are discussed. By synthesizing current knowledge and outlining future directions, this review aims to provide insights for researchers and practitioners seeking to leverage flexible and stretchable strain sensors for comprehensive human gait analysis.

In this study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited data sets, undocumented modifications to machine-settings and sensor failures, this task renders a challenging application area for Machine Learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues as sensor usability, data quality and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

The recently explored synergistic combination of graphene-based materials and deep eutectic solvents (DESs) is opening novel and effective avenues for developing sensing devices with optimized features. In more detail, remarkable potential in terms of simplicity, sustainability, and cost-effectiveness have been demonstrated for sensors, resulting in the creation of hybrid devices with enhanced signal-to-noise ratio, linearity, and selectivity. Therefore, this review aims to provide a comprehensive overview of the currently available scientific literature discussing investigations and applications of sensors that integrate graphene-based materials and deep eutectic solvents, with an outlook for the most promising developments of this approach.

This paper describes design of experimental methodology developed to measure the working properties of accuracy of the path traversed by a collaborative robot. The methodology proposed here uses a collaborative robot and a laser measuring system Gepard from Raytec. The main parts of the measuring chain and the ISO 9283 standard are described. The proposed experimental methodology should examine working properties of industrial robots, such as the properties of position and path. The focus of this paper lies on the path accuracy of robots. Currently, the interest in this topic is on the rise and the measuring systems capable of recording this parameter are too costly. This paper focuses on experimental measuring of the path properties, describing them in more detail. The measuring and results were processed in the software tool developed for Gepard.

Collecting and recording physiological parameters and forwarding this data via telemedicine or remote surveys may significantly increase the health and safety of people working under extreme environmental conditions, like firefighters, mountain rescuers, disaster control employees, military personnel, etc. First, ruggedized systems, as small, lightweight, and flexible as possible, are needed to optimize the goal of increasing occupational health and safety of persons at risk and maximizing their performance during missions. Previous systems could only record a very limited number of parameters, and a few could forward the data online to return information to the user. Therefore, in order to record several meaningful, valid, and reliable physiological parameters synchronized, an extension of existing systems is required. This review highlights this problem and gives an overview of the historical development. The actual status of the systems available was listed and described comparatively. In the end, a proven, promising, and innovative solution is presented that can solve the problem and allow people to work more safely in extreme environments, which may represent the future.

Glass has emerged as a highly versatile substrate for various sensor and MEMS packaging applications, including electromechanical, thermal, optical, biomedical, and RF devices, due to its exceptional properties such as high geometrical tolerances, outstanding heat and chemical resistance, excellent high-frequency electrical properties, and the ability to be hermetically sealed. In these applications, Through-Glass-Via (TGV) plays a vital role in manufacturing and packaging by creating electrical interconnections through glass substrates. This paper provides a comprehensive summary of the research progress in TGV fabrication along with their integrations, including through-via formation and metallization. The paper also reviews the significant qualification and reliability achievements obtained by the scientific community for TGV. Additionally, the paper summarizes the application of TGV technology in various sensors.

The purpose of this paper is to compare daily sunshine duration (SD) measured by Campbell-Stokes sunshine recorder (CS) with three automatic instruments (Kipp & Zonen pyranometers (CM6B), pyrheliometer (CHP1) and CSD3 sunshine recorders). The comparisons are essential, since in recent decades automatic sunshine duration sensors have been systematically replacing the manual measurements affecting therefore the continuity of the CS time series. The comparisons were applied to the daily SD data of three synoptic stations (Larnaca A/P, Pafos A/P and Athalassa) where parallel measurements of manual and automatic instruments are carried out during the periods 2009-2012 and 2012-2015. The CSD3 measurements were obtained from two automatic weather stations (AWS) (Mennogia and Pentakomo) which are closed to the synoptic station of Larnaca A/P. The comparisons cover the daily and monthly sums, their statistical distributions and their relationships. The daily differences of the pairs of the sunshine recorders installed at the same location are mostly within the range of -1.0 to +1.0 h. At Larnaca, in the period 2009-2012, the pyranometric method (Gl) is higher than the CS in the summer months, while during the winter months it is lower than CS. Pafos and Athalassa showed higher values of CS comparing to Gl and the pyrheliometric method (Pr), respectively, almost throughout the year. Generally, the annual average daily differences between the different pairs range between 0.3 and 0.5 h. Regression equations were determined allowing the estimation of daily SD under different sky conditions. The closest relations between different pairs were obtained in ‘clear sky’ and ‘all sky’ conditions, as it is indicated by the high coefficient of determinations. R2 reached the values of 0.92 and 0.96 under ‘all sky’ conditions, and between 0.80 and 0.91 under ‘clear’ sky conditions, respectively. An increase in cloud cover causes an increase in the SD differences recorded by the relevant devices. Comparing statistically the different pairs using the Mann-Whitney nonparametric test which is the alternative to the Two Sample T parametric test, it was found that the medians are not statistically different. However, significant differences were only detected in the case of the pair of CS and Pr at Athalassa (2012-2015) using both tests. In the rest of the cases the differences are not statistically significant at the 5% level.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

To monitor adherence to diets and to design and evaluate nutritional interventions it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries), and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using non-obtrusive technology in daily life would overcome this. We here provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. 1328 studies were screened and after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors, and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.

The increase and diversity of low-cost Air Quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into a broad AQ wireless sensor networks with the aim of enabling real-time monitoring and higher spatial sampling density of pollution in all parts of the cities. Considering that the vast majority of the population lives in these cities and the increase in respiratory/allergic problems in a large part of the population, it is of great interest to offer services and applications to improve their quality of life. In the ECO4RUPA project we focus on this kind of service, proposing an inclusive and intelligent routing ecosystem carried out by using a network of low-cost AQ sensors with the support of 5G communications along with official AQ monitoring stations, assisted with artificial intelligence to improve the AQ monitoring data and by spatial interpolation techniques to enhance its spatial resolution. The goal of this service is to calculate healthy walking and/or cycling routes according to the particular citizen’s profile and needs. We provide and analyse the results of the proposed route planner under different scenarios (different time tables, congestion road traffic and routes) and different user’s profiles, with special interest on citizens with asthma and pregnant women, since both require special needs. In summary, our approach can lead to an approximately average reduction in pollution exposure of 17.82%, while experiencing an approximately average increase in distance travelled of 9.8 %.

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

This contribution shows the possibilities of a low-cost multipurpose data logger that was built around an Arduino Mega 2560 single board computer. To transform the Arduino Mega into such a data logger, a sensor shield was designed that contains an SD-card, real-time clock, and different kinds of connectors so that sensors can easily be attached to the device. The software considers a wide range of predefined sensors from which a user can choose. To assess the performance of such a device, short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator were realized. Besides the possibilities to apply the data logger in different kinds of applications, a method is proposed to assess the credibility of such a system. The assessment based on the various short-term campaigns showed that the system scores positively on most of the performance indicators, but that unexpected (black swan) events affect the assessment over the longer term. This makes the development of low-cost scientific instruments harder than expected. The stability and long-term performance of this type of design requires continuous evaluation and engineering correction.

The Internet of Things (IoT) technology and devices constitute an exciting field in computer science that is rapidly emerging worldwide. IoT devices function by connecting real-world objects to the internet, resulting in a higher number of interconnected devices than ever witnessed in history. Through internet connectivity, these devices can be utilized in various ways, such as monitoring and tracking. Their prevalence is increasing exponentially, coinciding with advancements in wireless networking technologies. The internet’s enhanced connectivity has played a vital role in fostering the proliferation of IoT devices. Presently, almost any everyday object can be network-connected. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. This paper aims to review the emergence of IoT devices, analyze their common applications, and explore the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. IoT technology can be applied to nearly any domain, and each use case has unique requirements. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Moreover, a comprehensive understanding of the prevalent methodologies will enable readers to distinguish between current practices and future methods. Given the rapid rate at which IoT technology is advancing, this paper aims to provide researchers with an understanding of the factors that have brought us to this point and the ongoing efforts to shape the future of IoT.

Copper-based nanomaterials in the last decade attracted many researchers due to their extensive practical applications, unique, inexpensiveness, and wide availability. In addition to this, copper-based nanomaterials possess good thermal stability, and selectivity and also possess high activity. This review emphasis on the recent advances in the synthesis of copper nanomaterials and their wide applications in the field of environmental catalysis. This review aims to fill a significant knowledge gap in the different areas of environmental pollution management. Also, the paper concentrates on the recent applications of copper-based nanomaterials for environmental remediation, including the removal of heavy metals, and degradation of organic pollutants such as pharmaceuticals, and other environmental contaminants. Also, it will be helpful to young researchers in improving the suitability of implementing the Copper nanomaterials in the right way establishing and achieving sustainable goals for environmental remediation.

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, physical or chemical mechanisms of sensors, and location of sensor platforms. In this study, we analyzed all available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.

Plants have acquired sets of highly regulated and complex signaling pathways to respond to unfavorable environmental conditions during evolution. Calcium signaling, as a vital mechanism, enables plants to respond to external stimuli, including abiotic and biotic stresses, and coordinate the basic processes of the growth and development. In the present study, the calcium sensor families, including CBL and CIPK, were investigated in the halophyte plant, Aeluropus littoralis, with a comprehensive analysis. Here, six AlCBL genes, and twenty AlCIPK genes were presented. The analysis of the gene structure and conserved motifs, as well as physicochemical properties, showed that these genes are highly conserved during evolution. The expression levels of AlCBL genes and AlCIPK genes were evaluated under salt stress in leaf and root tissue. Based on the real-time RT-PCR results, AlCIPK gene family had a higher variation in mRNA abundance compared to the AlCBL gene family. The AlCIPKs were found to have a higher abundance in leaves than in roots. The results suggest that the interaction pattern of AlCBL genes with AlCIPK is tissue-specific, and different interactions can be expected in leaves and roots. Based on these patterns AlCIPK3.1 - AlCBL4.1 and AlCIPK1.2 - AlCBL4.4 can interact in root tissue, while the AlCBL10 has the potential to interact with the AlCIPK5, AlCIPK26 and AlCIPK12.3 in the leaf tissue. These findings provide valuable information on the structure and function of calcium sensor families in Aeluropus littoralis a halophyte plant, for future research on the biological function of CBLs and CIPKs on salt stress resistance.

Objective To measure, exposure to PM2.5 particles in outdoor smoking areas and changes in breathing rates in patients with asthma or chronic obstructive pulmonary disease (COPD). Setting Sixty venues in Czechia, Ireland and Spain, in an open, non-randomised, clinical trial. Participants We studied 60 patients-30 asthma patients (Female 63.3%), with a mean age (+/-standard deviation [SD]) of 47.4 (19.0 SD), and 30 COPD patients (Female 51.6%), mean age 63.5 (10.1 SD), smokers, non-smokers or ex-smokers, recruited through medical clinics. Intervention Patients wore a PM2.5 particle monitor (AirSpeck), and a breath monitor (RESpeck) for 24 hours to determine changes in breathing rates (Br) at rest and during a visit to an outside smoking area. Spirometry and breath CO were measured before and the day after visiting an outdoor smoking area. Results PM2.5 levels in the 60 venues were highly variable, in 1 premises levels of PM2.5 were sustained for at least 15 minutes at ≥ 2,000 µg/m3, in 4 premises, ≥ 500 (range 1,933-539) µg/m3, in 8 premises, ≥ 200(range 480-203) µg/m3, in 9 premises, ≥ 100 (range 170-108) µg/m3, in 8 premises, ≥ 40 (range 80.5- 40.1) µg/m3, in 9 premises, ≥ 25 µg/m3, in 10 premises, ≥ 10 µg/m3, in 8 premises, and ≤10 µg/m3 in only 3 premises, with a single wall. The overall breathing rates/minute (Br)did not change significantly but in 28 patients mean Br increased from 21.47 (1.74 SD) to 22.8 (2.29 SD), change of -1.35 (-1.80-0.91 C.I), p value 0.00 and mean in 29 patients Br decreased from 21.95(2.43 SD) to 20.38(2.79 SD), 1.57(1.03-2.12 C.I), p value 0.00. Conclusion Exposure to high levels of PM2.5, and associated alteration of patients’ breathing rates occurred in outdoor smoking areas despite national comprehensive smokefree laws. These exposure levels support the abolition of such areas. (Words 295) ClinicalTrials.gov ID: NCT03074734 Ethics: Approval Number: Ref: 15-103

Reliable and accurate flood prediction is a challenging task in poorly gauged basins due to data scarcity. Data is an essential component of any AI/ML model today, and the performance of such models hugely depends on the availability of sufficient amount of trusted, representative data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still insufficiently monitored, which significantly hinges the design and development of advanced flood prediction models and early warning systems. This paper presents a multi-modal, sensor-based and near-real time river monitoring system to produce a multi-feature data set for the Kikuletwa river in Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which gather information about river depth levels and weather at several locations, aims at widening the ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We provide details on the monitoring system used to gather the data as well as report on the methodology and the nature of the data. Finally, we present the relevance of the data set in the context of flood prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting some applications of the data set beyond flood warning systems.

The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

Objective: The aim of this study was to provide a new machine learning method to determine temporal events and inner-cycle parameters (e.g., cycle, poles and skis contact and swing time) in cross-country roller ski skating on the field, using a single deported inertial measurement unit (IMU). Methods: The developed method is based on long short-term memory neural networks to detect poles and skis initial and final contact with the ground during the cyclic movements. Eleven athletes skied four laps of 2.5 km at low and high intensity using skis with two different rolling coefficients. They were equipped with IMUs attached to the upper back, lower back and to the sternum. Data from force insoles and force poles were used as reference system. Results: The IMU placed on the upper back provided the best results, as the LSTM network was able to determine the temporal events with an accuracy ranging from 49 to 55 ms and the corresponding inner-cycles parameters were calculated with a precision of 63 to 68 ms. The method detected 95% of the events for the poles and 87% of the events for the skis. Conclusion: The proposed LSTM method provides a promising tool for assessing temporal events and inner-cycle phases in roller ski skating showing the potential of using a deported IMU to estimate different spatio-temporal parameters of human locomotion.

Horse riding therapy, also known as hippotherapy, is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapists, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients in Indonesia. This problem motivates us to consider mechanical horse riding simulator machines to replace actual horse hippotherapy. However, most patients are children and are easily bored when asked to do monotonous activities for an extended period. The room setting also does not give the patient visual inputs that usually help motivates the children in real-horse hippotherapy activities. To solve this problem, we designed an exercise game (exergaming) software which we named \textit{Sirkus Apel}, providing the patients with fun activities while doing the therapy. We also design an inertial sensor-based controller that lets the patients control the in-game horse by their back movements, which also benefits CP patients. To make the visual input enjoyable to the user while also considering the user's safety, we built a convex mirror-based dome virtual reality to provide an immersive 3-D experience. We then project the game content to the dome to provide an immersive experience to the patients making it as if they are riding a real horse inside the game.

A position sensing glove, called SmartScan, that creates a 3D virtual model of a real object is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, and has a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

Biosensors are essential tools which have been traditionally used to monitor environmental pollution, detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from the in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single-biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications.

In the article we present the results concerning the impact of structural and chemical properties of zinc oxide in various morphological forms, on its gas-sensitive properties tested in an atmosphere containing a very aggressive gas such as chlorine. Two types of ZnO sensor layers obtained by two different technological methods were used. Their morphology, crystal structure, specific surface area, porosity, surface chemistry and structural defects were characterized, and then compared with gas-sensitive properties in a chlorine-containing atmosphere. To achieve this goal scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and photoluminescence spectroscopy (PL) methods were used. The sensing properties of obtained active layers were tested by temperature stimulated conductance method (TSC). We have noticed that their response in chlorine atmosphere is not determined by the size of the specific surface or porosity. The obtained results showed that the structural defects of ZnO crystals play the most important role in chlorine detection. We demonstrated that the Cl2 adsorption is a concurrent process to oxygen adsorption. Both of them occur on the same active species (oxygen vacancies). They concentration is higher on the side planes of the zinc oxide crystal than the others. Thanks to the conducted studies authors demonstrated that to develop a new gas sensor devices not only changing of active layer chemical composition but also controlling its crystal structure and morphology could be used.

We report on the development of several different thin-film functional material systems prepared by RF magnetron sputtering at Edith Cowan University nanofabrication labs. We conduct research on the design, prototyping, and practical fabrication of high-performance magneto-optic (MO) materials, oxide based sensor components, and heat regulation coatings for advanced construction and solar windows.

The deaf impairment is among the most substantial health problem worldwide, that can lead to various personal, economical, and social crisis. Therefore, it is critical for developing an efficient way to facilitate communication between deaf-dumb impaired and normal people. Herein, we have rationally designed a new digitally computerized and mobile smart system as an efficient communication tool between deaf impaired and normal Arabian people. This is based on two main steps, including creating a digital output for the hand gestures using gloves flex sensors equipped with a three-axis accelerometer that is controlled using a microcontroller. The digital results are compared to that in a words-based “database”, where Arabs use expressions not alphabet in their communication. The second step is translation or conversion the outputs of the first stage into written texts and voices. The newly developed system allows Arabian deaf to translate words of ordinary people into gestures using a speech recognition system with an impressive accuracy over 90 % without the needing for a webcam, colored gloves, and/or online translator. The presented system can be used on any android or windows.

MEMThe PdNi film hydrogen sensors with Wheatstone bridge structure were designed and fabricated by the micro-electro-mechanical system (MEMS) technology. The integrated sensors consisted of four PdNi alloy film resistors. The interval two of them were shielded with silicon nitride film and used as reference resistance, while the others were used for hydrogen sensing. The PdNi alloy films and SiN films were deposited by magnetron sputtering. The morphology and microstructure of the PdNi films were characterized with X-ray diffraction (XRD). The output resistance signal was converted to millivolt output voltage signal for easy data acquisition. Hydrogen (H2) sensing properties of PdNi film hydrogen sensor with Wheatstone bridge structure was investigated under different temperatures (30℃, 50℃ and 70℃) and H2 concentrations (from 10 ppm to 0.4%). The hydrogen sensor demonstrated good response at different hydrogen concentrations and high repeatability in cycle testing under 0.4% H2 concentration. Under 10ppm hydrogen, the PdNi film hydrogen sensor had evident and collectable output voltage of 600 μV.

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

Campylobacter infection affects more than 200,000 people every year in Europe and in the last four years trend shows an increase of campylobacteriosis. Main vehicle for transmission of the bacterium is contaminated food like meat, milk, fruit and vegetables. In this study, the aim was to find characteristic VOCs of C. jejuni in order to detect its presence with an array of MOX gas sensors. Using a starting concentration of 10³ CFU/mL, VOCs were analyzed using GC-MS with SPME technique at the initial time (T0) and after 20 hours (T20). It has been found that Campylobacter sample at T20 is characterized by a higher number of alcohol compounds that the one at T0 and this is due to sugar fermentation. Sensors results showed the ability of the system to follow bacteria curve growth from T0 to T20 using PCA. In particular, this results in a decrease of ΔR/R₀ value over time. For this reason, MOX sensors are a promising technology for the development of a rapid and sensitive system for C. jejuni.

The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

A novel type of graphene-like quantum dots, synthesized by oxidation and cage-opening of C60 buckminsterfullerene, has been studied as a fluorescent and absorptive probe for heavy-metal ions. The lattice structure of such unfolded fullerene quantum dots (UFQDs) is distinct from that of graphene since it includes both carbon hexagons and pentagons. The basic optical properties, however, are similar to those of regular graphene oxide quantum dots. On the other hand, UFQDs behave quite differently in the presence of heavy-metal ions, in that multiple sensitivity to Cu2+, Pb2+ and As(III) was observed through comparable quenching of the fluorescent emission and different variations of the transmittance spectrum. By dynamic light scattering measurements we confirmed, for the first time in metal sensing, that this response is due to multiple complexation and subsequent aggregation of UFQDs. Nonetheless, the explanation of the distinct behaviour of transmittance in the presence of As(III) and the formation of precipitate with Pb2+ require further studies. These differences, however, also make it possible to discriminate between the three metal ions in view of the implementation of a selective multiple sensor.

One of the most common limits to gas sensor performance is the presence of unwanted interference fringes or etalons arising, for example, from multiple reflections between surfaces in the optical path. Additionally, since the amplitude and the frequency of these interference depend on the distance and alignment of the optical elements, they are affected by temperature changes and mechanical disturbances, giving rise to a drift of the signal. In this work, we present a novel semi-parametric algorithm which allows the extraction of a signal, like the spectroscopic absorption line of a gas molecule, from a background containing arbitrary disturbances, without having to make any assumption on the functional form of these disturbances. The algorithm is applied first to simulated data and then to oxygen absorption measurements in presence of strong fringes.To the best of the authors' knowledge, the algorithm enables an unprecedented accuracy particularly if the fringes have a free spectral range and amplitude comparable to those of the signal to be detected. The described method presents the advantage of being based purely on post processing, and to be of extremely straightforward implementation if the functional form of the Fourier transform of the signal is known. Therefore it has the potential to enable interference-immune absorption spectroscopy. Finally, its relevance goes beyond absorption spectroscopy for gas sensing since it can be applied to any kind of spectroscopic data.

To achieve a narrow bandwidth optical filter with a wide swept range for new generation optical spectrum analysis (OSA), an optoelectronic equivalent narrowband filter (OENF) is investigated and a swept optical filter with bandwidth of several MHz and swept range of several ten nanometers is built using electric filters and a swept laser as local oscillator (LO). The principle of OENF is introduced and analysis of OENF system is presented. Two electric filters are optimized to be RBW filters for high and medium spectral resolution application. Both simulations and experiments are conducted to verify OENF principle and results show that power uncertainty is less than 1.2% and spectral resolution can reach 6 MHz. Then, a real-time wavelength calibration system consisting of HCN gas cell and FP etalon is proposed to guarantee a wavelength accuracy of ±0.4 pm at C-band and to reduce the influence of phase noise and nonlinear velocity of swept LO. Finally, experiments on OSA of actual spectra of various optical sensors using OENF system are conducted. Experimental results indicate that OENF system has an excellent capacity in analysis of fine spectrum structures.