Search

The increase in anthropogenic pollution raises serious concerns regarding contamination of water bodies and aquatic species with potential implications on human health. Pharmaceutical compounds are a type of contaminants of emerging concern that are increasingly consumed and, thus, being frequently found in the aquatic environment. In this sense, an electrochemical sensor based on an unmodified and untreated carbon fiber paper (CPS—carbon paper sensor) was simply employed for the analysis of trimethoprim antibiotic in fish samples. First, the analytical conditions were thoroughly optimized in order for the CPS to achieve maximum performance in trimethoprim determination. Therefore, an electrolyte (0.1 M Britton–Robinson buffer) pH of 7 was selected and for square wave voltammetry parameters, optimum values of amplitude, frequency and step potential corresponded to 0.02 V, 50 Hz, and 0.015 V, respectively, whereas the deposition of analyte occurred at +0.7 V for 60 s. In these optimum conditions, the obtained liner range (0.05 to 2 µM), sensitivity (48.8 µA µM−1 cm−2), and LOD (0.065 µM) competes favorably with the commonly used GCE-based sensors or BDD electrodes that employ nanostructuration or are more expensive. The CPS was then applied for trimethoprim determination in fish samples after employing a solid phase extraction procedure based on QuEChERS salts, resulting in recoveries of 105.9 ± 1.8% by the standard addition method.

A novel, rapid, and facile method for the colorimetric determination of calcium using micro-analytical paper-based devices (μ-PADs) was developed. The proposed analytical method utilizes the color differences developing, after the addition of calcium, on the surface of the devices because of the complexation reaction of calcium with Methylthymol Blue (MTB) at room temperature, in alkaline pH. The devices were manufactured with chromatographic paper, using wax barriers, and the analytical protocol was easily implemented without the need of any experimental apparatus except for a simple imaging device. The user must regulate the pH, add the solutions on the paper, and measure the color intensity of the formed Ca(II)–MTB complex with a flatbed scanner. The experimental conditions for optimum color development, the possible interfering substances, and the reliability of the paper devices in different preserving conditions were optimized, with satisfactory results. The method exhibited acceptable detection limits (2.9 mg L−1) with sufficiently good precision, which varied from 4.2% (intra-day) to 6.4% (inter-day). Saliva samples from healthy volunteers were successfully analyzed, and the calcium levels were calculated in the range of 30.71 to 84.15 mg L−1.

High-colored wastewater generated during the cellulose bleaching process causes the inhibition of biological activity when released into the environment. This study aimed to evaluate the bacterium’s capacity, identified as RGM2262, to degrade a complex phenolic structure such as lignin, which is found in high concentrations in the effluents generated during the production of cellulose, raw material for the manufacture of paper. To determine the values of the experimental variables that allow for a greater degradation of organic matter, an experimental model was carried out through experimental design. Thus, the experimental matrix was obtained with the variables pH 7 (−1) to 9 (+1) and a treatment time of 1 day (−1) to 5 days (+1). The results show that, at pH 8 and pH 9, both treatments—with bacteria in bio-films and without bio-films—were efficient. On the second day of treatment, 100% of the color and the phenolic structure were removed, with a similar rate constant, and at the same time, 80% COD and 70% of TOC, respectively.

The paper describes the development of an electrochemical sensor to be used for the determination of synthetic food colorants such as Sunset Yellow FCF (SY) and Tartrazine (TZ). The sensor is a carbon paper (CP) electrode, manufactured by using hot lamination technology and volume modified with fine-grained graphite powder (GrP). The sensor (GrP/CP) was characterized by scanning electron microscopy, energy dispersive spectrometry, electrochemical impedance analysis, cyclic, linear sweep and differential pulse voltammetry. The mechanism of SY and TZ electrochemical oxidation on GrP/CP was studied. The developed sensor has good electron transfer characteristics and low electron resistance, high sensitivity and selectivity. Applying the differential pulse mode, linear dynamic ranges of 0.005–1.0 μM and 0.02–7.5 μM with limits of detection of 0.78 nM and 8.2 nM for SY and TZ, respectively, were obtained. The sensor was used to detect SY and TZ in non-alcoholic and alcoholic drinks. The results obtained from drink analysis prove good reproducibility (RSD ≤ 0.072) and accuracy (recovery 96–104%).

The technique of using biochar-based catalysts in persulfate activation is a promising alternative to remov emerging and refractory pollutants (e.g., tetracycline-) in wastewater. However, the situation of biochars derived from waste pulp/paper mill sludge is still unclear. The pulp/paper mill sludge biochars (SBC300, SBC500, and SBC700) were obtained and characterized at pyrolysis temperatures of 300, 500, and 700 °C. Tetracycline degradation using peroxydisulfate activated by SBCs was investigated. The results demonstrated the removal efficiencies of tetracycline in SBC300-, SBC500- and SBC700-peroxydisulfate systems, which increased with the pyrolysis temperatures and were 4.3, 4.8, and 5.0 times that of a system with peroxydisulfate alone. The experiments of free radical quenching, singlet oxygen quenching, and electrochemistry indicated that the degradation of tetracycline in SBC-peroxydisulfate systems was mainly not a free radical pathway, but a non-radical pathway. Singlet oxygen (1O2) and electron transfer could play main roles in the degradation removal of tetracycline. The removal efficiencies of tetracycline in the SBC-peroxydisulfate systems could be up to 96.0% (SBC700-peroxydisulfate) under the optimum dosage of SBC, the molar ratio of peroxydisulfate to tetracycline and the solution pH value. The results indicate that a SBC700-peroxydisulfate system could be an effective “trash-to-treasure” treatment technique for wastewater containing antibiotics.

In this study, a paper-integrated analytical device that combined a paper-based colorimetric assay with a paper-based cell culture platform was developed for the in situ detection of hydrogen sulfide (H2S) in three-dimensional (3D)-cultured, live prostate cancer cells. Two kinds of paper substrates were fabricated using a simple wax-printing methodology to form the cell culture and detection zones, respectively. LNCaP cells were seeded directly on the paper substrate and grown in the paper-integrated analytical device. The cell viability and H2S production of LNCaP cells were assessed using a simple water-soluble tetrazolium salt colorimetric assay and H2S-sensing paper, respectively. The H2S-sensing paper showed good sensitivity (sensitivity: 6.12 blue channel intensity/μM H2S, R2 = 0.994) and a limit of quantification of 1.08 μM. As a result, we successfully measured changes in endogenous H2S production in 3D-cultured, live LNCaP cells within the paper-integrated analytical device while varying the duration of incubation and substrate concentration (L-cysteine). This paper-integrated analytical device can provide a simple and effective method to investigate H2S signaling pathways and drug screening in a 3D culture model.

A new method for the determination of chloride anions in sweat is described. The novelty of the method relies on the different photochemical response of silver ions and silver chloride crystals when exposed to UV light. Silver ions undergo an intense colorimetric transition from colorless to dark grey-brown due to the formation of nanosized Ag while AgCl exhibits a less intense color change from white to slightly grey. The analytical signal is obtained as mean grey value of color intensity on the paper surface and is expressed as the absolute difference between the signal of the blank (i.e., in absence of chloride) and the sample (i.e., in the presence of chloride). The method is simple to perform (addition of sample, incubation in the absence of light, irradiation, and offline measurement in a flatbed scanner), does not require any special signal processing steps (the color intensity is directly measured from a constant window on the paper surface without any imager processing) and is performed with minimum sample volume (2 μL). The method operates within a large chloride concentration range (10–140 mM) with good detection limits (2.7 mM chloride), satisfactory recoveries (95.2–108.7%), and reproducibility (&lt, 9%). Based on these data the method could serve as a potential tool for the diagnosis of cystic fibrosis through the determination of chloride in human sweat.

Mycelium-based composites have the potential to replace petrochemical-based materials within architectural systems and can propose biodegradable alternatives to synthetic sound absorbing materials. Sound absorbing materials help improve acoustic comfort, which in turn benefit our health and productivity. Mycelium-based composites are novel materials that result when mycelium, the vegetative root of fungi, is grown on agricultural plant-based residues. This research presents a material study that explores how substrate variants and fabrication methods affect the sound absorption properties of mycelium-based composites grown on paper-based waste substrate materials. Samples were grown using Pleurotus ostreatus fungi species on waste cardboard, paper, and newsprint substrates of varying processing techniques. Measurements of the normal-incidence sound absorption coefficient were presented and analyzed. This paper outlines two consecutive acoustic tests: the first round of experimentation gathered broad comparative data, useful for selecting materials for sound absorption purposes. The second acoustic test built on the results of the first, collecting more specific performance data and assessing material variability. The results of this study display that cardboard-based mycelium materials perform well acoustically and structurally and could successfully be used in acoustic panels.

The detection of hydrogen peroxide and the control of its concentration are important tasks in the biological and chemical sciences. In this paper, we developed a simple and quantitative method for the non-enzymatic detection of H2O2 based on the selective etching of Au@Ag nanorods with embedded Raman active molecules. The transfer of electrons between silver atoms and hydrogen peroxide enhances the oxidation reaction, and the Ag shell around the Au nanorod gradually dissolves. This leads to a change in the color of the nanoparticle colloid, a shift in LSPR, and a decrease in the SERS response from molecules embedded between the Au core and Ag shell. In our study, we compared the sensitivity of these readouts for nanoparticles with different Ag shell morphology. We found that triangle core–shell nanoparticles exhibited the highest sensitivity, with a detection limit of 10−4 M, and the SERS detection range of 1 × 10−4 to 2 × 10−2 M. In addition, a colorimetric strategy was applied to fabricate a simple indicator paper sensor for fast detection of hydrogen peroxide in liquids. In this case, the concentration of hydrogen peroxide was qualitatively determined by the change in the color of the nanoparticles deposited on the nitrocellulose membrane.

In this work, the authors explored the interaction of a suite of fluorescent zinc complexes with H2S. The authors provide evidence that HS− binds the zinc center of all the complexes under investigation, allowing them to possibly function as sensors by a ‘coordinative-based’ approach. Naked-eye color changes occur when treating the systems with HS−, so the fluorescence responses are modulated by the presence of HS−, which has been related to a change in the energy level and coupling of excited states through a computational study. The results show the potential of the systems to function as HS−/H2S colorimetric and fluorescent sensors. Paper-strip-based sensing experiments foresee the potential of using this family of complexes as chemosensors of HS− in more complex biological fluids.

Measuring the colorimetric signals produced by the biospecific accumulation of colorimetric probes and recording the results is a key feature for next-generation paper-based rapid tests. Manual processing of these tests is time-consuming and prone to a loss of accuracy when interpreting faint and patchy signals. Proprietary, closed-source readers and software companies offering automated smartphone-based assay readings have both been criticized for interoperability issues. Here, we introduce a minimal reader prototype composed of open-source hardware and open-source software that has the benefits of automatic assay quantification while avoiding the interoperability issues associated with closed-source readers. An image-processing algorithm was developed to automate the selection of an optimal region of interest and measure the average pixel intensity. When used to quantify signals produced by lateral flow immunoassays for detecting antibodies against SARS-CoV-2, results obtained with the proposed algorithm were comparable to those obtained with a manual method but with the advantage of improving the precision and accuracy when quantifying small spots or faint and patchy signals.

A practical hydrazine-carbothioamide-based fluorescent chemosensor TCC (N-(4-chlorophenyl)-2-(thiophene-2-carbonyl)hydrazine-1-carbothioamide) was applied for Zn2+ detection. TCC exhibited selective fluorescence emission for Zn2+ and did not show any interference with other metal ions. In particular, TCC was utilized for the detection of Zn2+ in paper strips, zebrafish and real water samples. TCC could detect Zn2+ down to 0.39 μM in the solution phase and 51.13 μM in zebrafish. The association ratio between TCC and Zn2+ was determined to be 2:1 by ESI-mass and Job plot. The sensing mechanism of TCC for Zn2+ was illustrated to be a chelation-enhanced fluorescence process through spectroscopic experiments and theoretical calculations.

Microbial fuel cells (MFCs) using rumen bacteria have been proposed as a power source for running devices inside cattle. In this study, we explored the key parameters of the conventional bamboo charcoal electrode in an attempt to improve the amount of electrical power generated by the microbial fuel cell. We evaluated the effects of the electrode’s surface area, thickness, and rumen content on power generation and determined that only the electrode’s surface area affects power generation levels. Furthermore, our observations and bacterial count on the electrode revealed that rumen bacteria concentrated on the surface of the bamboo charcoal electrode and did not penetrate the interior, explaining why only the electrode’s surface area affected power generation levels. A Copper (Cu) plate and Cu paper electrodes were also used to evaluate the effect of different electrodes on measuring the rumen bacteria MFC’s power potential, which had a temporarily higher maximum power point (MPP) compared to the bamboo charcoal electrode. However, the open circuit voltage and MPP decreased significantly over time due to the corrosion of the Cu electrodes. The MPP for the Cu plate electrode was 775 mW/m2 and the MPP for the Cu paper electrode was 1240 mW/m2, while the MPP for bamboo charcoal electrodes was only 18.7 mW/m2. In the future, rumen bacteria MFCs are expected to be used as the power supply of rumen sensors.

This work is focused on the age-old challenge of developing optical sensors for acidity measurements in low-pH aqueous solutions (pH < 5). We prepared halochromic (3-aminopropyl)amino-substituted quinoxalines QC1 and QC8 possessing different hydrophilic–lipophilic balance (HLB) and investigated them as molecular components of pH sensors. Embedding the hydrophilic quinoxaline QC1 into the agarose matrix by sol-gel process allows for fabrication of pH responsive polymers and paper test strips. The emissive films thus obtained can be used for a semi-quantitative dual-color visualization of pH in aqueous solution. Being exposed to acidic solutions with pH in the range of 1–5, they rapidly give different color changes when the analysis is performed in daylight or under irradiation at 365 nm. Compared with classical non-emissive pH indicators, these dual-responsive pH sensors allow for an increase in the accuracy of pH measurements, particularly in complex environmental samples. pH indicators for quantitative analysis can be prepared by the immobilization of amphiphilic quinoxaline QC8 using Langmuir–Blodgett (LB) and Langmuir–Schäfer (LS) techniques. Compound QC8 possessing two long alkyl chains (n-C8H17) forms stable Langmuir monolayers at the air–water interface, and these monolayers can be successfully transferred onto hydrophilic quartz and hydrophobic polyvinylchlorid (PVC) substrates using LB and LS techniques, respectively. The 30-layer films thus obtained are emissive, reveal excellent stability, and can be used as dual-responsive pH indicators for quantitative measurements in real-world samples with pH in the range of 1–3. The films can be regenerated by immersing them in basic aqueous solution (pH = 11) and can be reused at least five times.

This work proposes a new compact triple-band triangular patch antenna for RF energy harvesting applications in IoT devices. It is realized on Teflon glass substrate with a thickness of 0.67 mm and a relative permittivity of 2.1. Four versions of this antenna have been designed and realized with inclinations of 0°, 30°, 60° and 90° to study the impact of the tilting on their characteristics (S11 parameter, radiation pattern, gain) and to explore the possibilities of their implementation in the architectures of electronic equipment according to the available space. The antenna is also realized on waterproof paper with a thickness of 0.1 mm and a relative permittivity of 1.4 for biomedical domain. All the antennas (vertical antenna, tilted antennas and antenna realized on waterproof paper) have a size of 39 × 9 mm2 and cover the 2.45 GHz and 5.2 GHz Wi-Fi bands and the 8.2 GHz band. A good agreement is obtained between measured and simulated results. Radiation patterns show that all the antennas are omnidirectional for 2.45 GHz and pseudo-omnidirectional for 5.2 GHz and 8.2 GHz with maximum measured gains of 2.6 dBi, 4.55 dBi and 6 dBi, respectively. The maximum measured radiation efficiencies for the three antenna configurations are, respectively, of 75%, 70% and 72%. The Specific Absorption Rate (SAR) for the antenna bound on the human body is of 1.1 W/kg, 0.71 W/kg and 0.45 W/kg, respectively, for the three frequencies 2.45 GHz, 5.2 GHz and 8.2 GHz. All these antennas are then applied to realize RF energy harvesting systems. These systems are designed, realized and tested for the frequency 2.45 GHz, −20 dBm input power and 2 kΩ resistance load. The maximum measured output DC power is of 7.68 µW with a maximum RF-to-DC conversion efficiency of 77%.

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

Simultaneous sensing of multiple gases by a single fluorescent-based gas sensor is of utmost importance for practical applications. Such sensing is strongly hindered by cross-sensitivity effects. In this study, we propose a novel analysis method to ameliorate such hindrance. The trial sensor used here was fabricated by coating platinum(II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) and eosin-Y dye molecules on both sides of a filter paper for sensing O2 and NH3 gases simultaneously. The fluorescent peak intensities of the dyes can be quenched by the analytes and this phenomenon is used to identify the gas concentrations. Ideally, each dye is only sensitive to one gas species. However, the fluorescent peak related to O2 sensing is also quenched by NH3 and vice versa. Such cross-sensitivity strongly hinders gas concentration detection. Therefore, we have studied this cross-sensitivity effect systematically and thus proposed a new analysis method for accurate estimation of gas concentration. Comparing with a traditional method (neglecting cross-sensitivity), this analysis improves O2-detection error from −11.4% ± 34.3% to 2.0% ± 10.2% in a mixed background of NH3 and N2.

Rice straw, an agricultural waste product generated in huge quantities worldwide, is utilized to remediate diesel pollution as it possesses excellent characteristics as a natural sorbent. This study aimed to optimize factors that significantly influence the sorption capacity and the efficiency of oil absorption from diesel-polluted seawater by rice straw (RS). Spectroscopic analysis by attenuated total reflectance infrared (ATR-IR) spectroscopy and surface morphology characterization by variable pressure scanning electron microscopy (VPSEM) and energy-dispersive X-ray microanalysis (EDX) were carried out in order to understand the sorbent capability. Optimization of the factors of temperature pre-treatment of RS (90, 100, 110, 120, 130 or 140 °C), time of heating (10, 20, 30, 40, 50, 60 or 70 min), packing density (0.08, 0.10, 0.12, 0.14 or 0.16 g cm−3) and oil concentration (5, 10, 15, 20 or 25% (v/v)) was carried out using the conventional one-factor-at-a-time (OFAT) approach. To eliminate any non-significant factors, a Plackett–Burman design (PBD) in the response surface methodology (RSM) was used. A central composite design (CCD) was used to identify the presence of significant interactions between factors. The quadratic model produced provided a very good fit to the data (R2 = 0.9652). The optimized conditions generated from the CCD were 120 °C, 10 min, 0.148 g cm−3 and 25% (v/v), and these conditions enhanced oil sorption capacity from 19.6 (OFAT) to 26 mL of diesel oil, a finding verified experimentally. This study provides an improved understanding of the use of a natural sorbent as an approach to remediate diesel pollution.

A set of CALUX in vitro bioassays was applied for long-term toxicity monitoring at an advanced wastewater treatment plant comprising ozonation and granular activated carbon filtration for the abatement of contaminants of emerging concern (CEC). During the 13-month monitoring, eight reporter gene assays targeting different modes of action along the cellular toxicity pathway were accessed to evaluate the suitability and robustness of the technologies. Two approaches were followed: on the one hand, signal reduction during advanced treatment was monitored, on the other hand, results were compared to currently available effect-based trigger values (EBTs). A decrease of the corresponding biological equivalent concentrations after the multibarrier system could be observed for all modes of action, while the estrogenic activity decreased below the EBT already during ozonation, the potencies of oxidative stress-like and toxic PAH-like compounds still exceeded the discussed EBT after advanced treatment. Overall, the long-term monitoring confirmed the positive effect of the multibarrier system, commonly evaluated only by CEC abatement based on chemical analysis. It could be demonstrated that advanced WWTPs designed for CEC abatement are suitable to significantly decrease toxicity responses not only in the frame of pilot studies but under real-world conditions as well.

The use of electrochemistry is a promising approach for the treatment of direct osmosis concentrate that contains a high concentration of organic pollutants and has high osmotic pressure, to achieve the safe discharge of effluent. This work addresses, for the first time, this major environmental challenge using perforated aluminum electrodes mounted in an electrocoagulation–flotation cell (PA-ECF). The design of the experiments, the modeling, and the optimization of the PA-ECF conditions for the treatment of DO concentrate rich in Pb were explored using a central composite design (CCD) under response surface methodology (RSM). Therefore, the CCD-RSM was employed to optimize and study the effect of the independent variables, namely electrolysis time (5.85 min to 116.15 min) and current intensity (0.09 A to 2.91 A) on Pb removal. Optimal values of the process parameters were determined as an electrolysis time of 77.65 min and a current intensity of 0.9 A. In addition to Pb removal (97.8%), energy consumption, electrode mass-consumed material, and operating cost were estimated as 0.0025 kWh/m3, 0.217 kg Al/m3, and 0.423 USD/m3, respectively. In addition, it was found that DO concentrate obtained from metallurgical wastewater can be recovered through PA-ECF (almost 94% Pb removal). This work demonstrated that the PA-ECF technique could became a viable process applicable in the treatment of DO concentrate containing Pb-rich for reuse.

The effective purification of large-capacity wastewater from heavy non-ferrous metals and organic pollutants using inexpensive sorption technology remains a crucial task. Analyzing the current objectives of sustainable development, biochar obtained from chicken manure has prospects as a source for sorption purification of industrial wastewater. To determine the characteristics of organic material and the composition of the initial and equilibrium phases, the following analysis methods were used: volumetric, complexometric, pH-metric, X-ray fluorescence, chromatographic, spectrophotometric, luminescence-photometric and X-ray diffraction. The thermal nitrogen desorption method was used for determining the specific surface area and pore sizes with the Quantachrome Nova 1000e automatic analyzer. The sorption properties of the reprocessing product of the fresh poultry manure were investigated in relation to organic and inorganic compounds: phenols, benzene, ketones, cations of lead, mercury, manganese and iron. Based on the results obtained, technical conditions were developed for the use of the reprocessed organic waste-based product as a sorption material for household, industrial and technological wastewater purification from organic and inorganic components.

To determine the effects of a gradual temperature decrease on reactor performance and the microbial community, four fixed-bed reactors that were packed with a biofilm carrier were operated for 217 days. The temperature of the reactors was decreased from 30 °C to 3 °C. The reactors initially soured at 3 °C and recovered when they were returned to 4 °C, as indicated by the stabilization of biogas production, methane production, VFA concentration, pH, and the COD removal rate. Our results also revealed that methanomicrobiales were the dominant methanogen, the concentration of the 16S rRNA gene in the carbon-fiber carrier sludge exceeded the same gene concentration in the deposited sludge, and that the carbon-fiber carrier played an important role in methanomicrobiale colonization at low temperatures. We suggest that 4 °C is the low-temperature threshold for optimal reactor performance.

The table olive industry is producing a huge amount of wastewater, which is a post-processing cost and an environmental concern. The present study aims to valorize this processing by-product to obtain a value-added product, thereby enhancing resource efficiency and contributing to achieving sustainable development goals (SDGs). In this sense, a chemical reaction-based platform was developed to obtain valuable components, such as levulinic acid (LA) and 5-hydromethylfurfural (HMF). The products were then analyzed using NMR identification of the antioxidant phenolic fraction and microwave single-phase reaction of the sugary fraction. According to the results, the highest concentration of phenolic compounds does not correspond to the sample directly obtained from NaOH treatment (S1), indicating that water washing steps (S2–S5) are fundamental to recover phenolic substances. Moreover, glucose was presented in the sugary fraction that can be transformed into levulinic acid by a single-phase reaction under microwave irradiation. The information provided in this manuscript suggests that the wastewater from the olive processing industry can be valorized to obtain valuable products.

The biofilms generated in a fire extinguishing water supply system can cause corrosion and a reduction in the water supply capacity, thus, degrading the system performance. To mitigate microbial corrosion, appropriate disinfection measures are necessary. In this study, the secondary addition of chlorine is employed to investigate the kinetics of chlorine decay, and shock disinfection is applied to investigate the removal efficiency of corrosion bacteria, and the microbial composition of a biofilm on the pipe wall was also clarified. The results show that the residual chlorine content in the secondary chlorination process was directly correlated with the decay rate of residual chlorine and the corrosion rate of the pipe wall. Additionally, the chlorine impact disinfection method could reduce the electrochemical corrosion phenomenon of the pipe wall. When the concentration of chlorine was 3 mg/L, the removal rate of corrosion bacteria was higher in 60 min than in 30 min. Specifically, most of the bacteria were inactivated in 60 min and the biofilm was severely damaged. Shock disinfection could significantly inactivate all microflora in the biofilm, the relative abundances of microflora varied significantly, while the change of microflora at the phylum level was insignificant. This study can provide theoretical support for the secondary addition of chlorine and shock disinfection in a fire extinguishing water supply system.

Phosphorus is one of the main factors causing water eutrophication, and the traditional phosphorus removal process causes phosphorus-rich sludge pollution. The facultative MBR process uses phosphate-reducing bacteria to convert phosphate into directly recyclable gaseous phosphine to solve this malpractice and make sewage become a new phosphorus resource. In order to investigate the phosphorus removal efficiency and the mechanism under facultative conditions, run the facultative MBR reactor for 30 days. The COD value, phosphate concentration, and phosphine yield were measured, and the changes of sludge metabolic pathway abundance and community composition in different periods were detected. According to the measurement, the maximum phosphorus removal efficiency is 43.11% and the maximum yield of phosphine is 320 μg/m3 (measured by the volume of sewage). Combined with thermodynamic analysis, the microbial mechanism of the reactor was proposed, and the possible transformation pathway of phosphorus was analyzed. At last, changes the phosphorus removal process from the ‘removal type’ to the ‘recycling type’.

The stable and efficient operation of the activated sludge sequencing batch reactor (ASSBR) in heavy oil refineries has become an urgent necessity in wastewater biotreatment. Hence, we constructed a green and efficient solid bioaugmentation agent (SBA) to enhance the resistance of the reactor to loading shock. The impact of bioaugmentation on the performance and microbial community dynamics under three patterns of heavy oil refinery wastewater (HORW) loading shock (higher COD, higher toxicity, and higher flow rate) was investigated on an industrial-scale ASSBR. Results showed that the optimal SBA formulation was a ratio and addition of mixed bacteria Bacillus subtillis and Brucella sp., of 3:1 and 3.0%, respectively, and a glucose concentration of 5.0 mg/L. The shock resistance of ASSBR was gradually enhanced and normal performance was restored within 6–7 days by the addition of 0.2% SBA. Additionally, the removal efficiency of chemical oxygen demand and total nitrogen reached 86% and 55%, respectively. Furthermore, we found that Burkholderiaceae (12.9%) was replaced by Pseudomonadaceae (17.1%) in wastewater, and Lachnospiraceae (25.4%) in activated sludge was replaced by Prevotellaceae (35.3%), indicating that the impact of different shocks effectively accelerated the evolution of microbial communities and formed their own unique dominant bacterial families.

This study offers a comprehensive analysis of unconventional renewable and sustainable energy production options by tapping into the energy associated with the natural tendency to cancel salinity gradients that occur when salt water and fresh water come into contact. Furthermore, this paper assesses the potential for generating blue energy in Romania by examining historical data on flow rates from relevant fresh water sources, such as the Danube River, and the salt loading of potential receiving water bodies, including salt lakes, and the Black Sea. In addition, this study briefly references pilot-level technologies for blue energy production in other countries, including Norway, Japan, and the European Union. Based on literature data, this paper highlights the most important technological methods with applicative potential for harnessing energy from salinity gradients. The performances and limitations of these methods have been presented, acknowledging that despite recent years of advancements in materials and technologies, large-scale implementation and commercialization efforts are still limited. The analyzed data reveals that Romania possesses substantial potential blue energy resources for both large capacity installations, such as the Danube River-Black Sea with a potential achievable capacity of about 50 MW, as well as smaller capacity devices situated around saline lakes and salt deposits. This study concludes that Romania has a unique opportunity to capitalize on its vast blue energy potential and drive sustainable energy development.

Three-dimensional (3D) reconstruction of objects using the polarization properties of diffuse light on the object surface has become a crucial technique. Due to the unique mapping relation between the degree of polarization of diffuse light and the zenith angle of the surface normal vector, polarization 3D reconstruction based on diffuse reflection theoretically has high accuracy. However, in practice, the accuracy of polarization 3D reconstruction is limited by the performance parameters of the polarization detector. Improper selection of performance parameters can result in large errors in the normal vector. In this paper, the mathematical models that relate the polarization 3D reconstruction errors to the detector performance parameters including polarizer extinction ratio, polarizer installation error, full well capacity and analog-to-digital (A2D) bit depth are established. At the same time, polarization detector parameters suitable for polarization 3D reconstruction are provided by the simulation. The performance parameters we recommend include an extinction ratio ≥ 200, an installation error ∈ [−1°, 1°], a full-well capacity ≥ 100 Ke−, and an A2D bit depth ≥ 12 bits. The models provided in this paper are of great significance for improving the accuracy of polarization 3D reconstruction.

The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.

This paper presents an assessment of the quality of selected filtration methods for the postprocessing of multibeam echosounder data. In this regard, the methodology used in the quality assessment of these data is an important factor. One of the most important final products derived from bathymetric data is the digital bottom model (DBM). Therefore, quality assessment is often based on factors related to it. In this paper, we propose some quantitative and qualitative factors to perform these assessments, and we analyze a few selected filtration methods as examples. This research makes use of real data gathered in real environments, preprocessed with typical hydrographic flow. The methods presented in this paper may be used in empirical solutions, and the filtration analysis may be useful for hydrographers choosing a filtration method for DBM interpolation. The results showed that both data-oriented and surface-oriented methods can be used in data filtration and that various evaluation methods show different perspectives on data filtration quality assessment.

Asphalt mixes comprise aggregates, additives and bitumen. The aggregates are of varying sizes, and the finest category, referred to as sands, encompasses the so-called filler particles present in the mixture, which are smaller than 0.063 mm. As part of the H2020 CAPRI project, the authors present a prototype for measuring filler flow, through vibration analysis. The vibrations are generated by the filler particles crashing to a slim steel bar capable of withstanding the challenging conditions of temperature and pressure within the aspiration pipe of an industrial baghouse. This paper presents a prototype developed to address the need for quantifying the amount of filler in cold aggregates, considering the unavailability of commercially viable sensors suitable for the conditions encountered during asphalt mix production. In laboratory settings, the prototype simulates the aspiration process of a baghouse in an asphalt plant, accurately reproducing particle concentration and mass flow conditions. The experiments performed demonstrate that an accelerometer positioned outside the pipe can replicate the filler flow within the pipe, even when the filler aspiration conditions differ. The obtained results enable extrapolation from the laboratory model to a real-world baghouse model, making it applicable to various aspiration processes, particularly those involving baghouses. Moreover, this paper provides open access to all the data and results used, as part of our commitment to the CAPRI project, with the principles of open science.

Accurately detecting falls and providing clear directions for the fall can greatly assist medical staff in promptly developing rescue plans and reducing secondary injuries during transportation to the hospital. In order to facilitate portability and protect people’s privacy, this paper presents a novel method for detecting fall direction during motion using the FMCW radar. We analyze the fall direction in motion based on the correlation between different motion states. The range–time (RT) features and Doppler–time (DT) features of the person from the motion state to the fallen state were obtained by using the FMCW radar. We analyzed the different features of the two states and used a two-branch convolutional neural network (CNN) to detect the falling direction of the person. In order to improve the reliability of the model, this paper presents a pattern feature extraction (PFE) algorithm that effectively eliminates noise and outliers in RT maps and DT maps. The experimental results show that the method proposed in this paper has an identification accuracy of 96.27% for different falling directions, which can accurately identify the falling direction and improve the efficiency of rescue.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.

Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR.

Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be “patches” on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

This paper presents a new microstrip implantable antenna (MIA) design based on the two-arm rectangular spiral (TARS) element for ISM band (Industrial, Scientific, and Medical 2.4–2.48 GHz) biotelemetric sensing applications. In the antenna design, the radiating element consists of a two-arm rectangular spiral placed on a ground-supported dielectric layer with a permittivity of ϵr = 10.2 and a metallic line surrounding this spiral. Considering the practical implementation, in the proposed TARS-MIA, a superstrate of the same material is used to prevent contact between the tissue and the metallic radiator element. The TARS-MIA has a compact size of 10 × 10 × 2.56 mm3 and is excited by a 50 Ω coaxial feed line. The impedance bandwidth of the TARS-MIA is from 2.39 to 2.51 GHz considering a 50 Ω system, and has a directional radiation pattern with directivity of 3.18 dBi. Numerical analysis of the proposed microstrip antenna design is carried out in an environment with dielectric properties of rat skin (Cole–Cole model ϵf (ω), ρ = 1050 kg/m3) via CST Microwave Studio. The proposed TARS-MIA is fabricated using Rogers 3210 laminate with dielectric permittivity of ϵr = 10.2. The in vitro input reflection coefficient measurements are realized in a rat skin-mimicking liquid reported in the literature. It is observed that the in vitro measurement and simulation results are compatible, except for some inconsistencies due to manufacturing and material tolerances. The novelty of this paper is that the proposed antenna has a unique two-armed square spiral geometry along with a compact size. Moreover, an important contribution of the paper is the consideration of the radiation performance of the proposed antenna design in a realistic homogeneous 3D rat model. Ultimately, the proposed TARS-MIA may be a good alternative for ISM-band biosensing operations with its miniature size and acceptable radiation performance compared to its counterparts.

In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI—Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession.

Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for the timely intervention and prevention of MSDs. While existing approaches require human resources for computing the RULA score, which is highly subjective and untimely, the proposed DULA achieves automatic and objective assessment of musculoskeletal risks using a wireless sensor band embedded with multi-modal sensors. The system continuously tracks and records upper limb movements and muscle activation levels and automatically generates musculoskeletal risk levels. Moreover, it stores the data in a cloud database for in-depth analysis by a healthcare expert. Limb movements and muscle fatigue levels can also be visually seen using any tablet/computer in real-time. In the paper, algorithms of robust limb motion detection are developed, and an explanation of the system is provided along with the presentation of preliminary results, which validate the effectiveness of the new technology.

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen’s center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8–3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

Damping is an important factor contributing to errors in the measurement of rotational inertia using the torsion pendulum method. Identifying the system damping allows for minimizing the measurement errors of rotational inertia, and accurate continuous sampling of torsional vibration angular displacement is the key to realizing system damping identification. To address this issue, this paper proposes a novel method for measuring the rotational inertia of rigid bodies based on monocular vision and the torsion pendulum method. In this study, a mathematical model of torsional oscillation under a linear damping condition is established, and an analytical relationship between the damping coefficient, torsional period, and measured rotational inertia is obtained. A high-speed industrial camera is used to continuously photograph the markers on a torsion vibration motion test bench. After several data processing steps, including image preprocessing, edge detection, and feature extraction, with the aid of a geometric model of the imaging system, the angular displacement of each frame of the image corresponding to the torsion vibration motion is calculated. From the characteristic points on the angular displacement curve, the period and amplitude modulation parameters of the torsion vibration motion can be obtained, and finally the rotational inertia of the load can be derived. The experimental results demonstrate that the proposed method and system described in this paper can achieve accurate measurements of the rotational inertia of objects. Within the range of 0–100 × 10−3 kg·m2, the standard deviation of the measurements is better than 0.90 × 10−4 kg·m2, and the absolute value of the measurement error is less than 2.00 × 10−4 kg·m2. Compared to conventional torsion pendulum methods, the proposed method effectively identifies damping using machine vision, thereby significantly reducing measurement errors caused by damping. The system has a simple structure, low cost, and promising prospects for practical applications.

Making internet-of-things (IoT)-based applications context-aware demands large amounts of raw data to be collected, interpreted, stored, and reused or repurposed if needed from many domains and applications. Context is transient but interpreted data can be distinguished from IoT data in many aspects. Managing context in cache is a novel area of research that has been given very little attention. Performance metric-driven adaptive context caching (ACOCA) can have a profound impact on the performance and cost efficiency of context-management platforms (CMPs) when responding to context queries in realtime. Our paper proposes an ACOCA mechanism to maximize both the cost and performance efficiency of a CMP in near realtime. Our novel mechanism encompasses the entire context-management life cycle. This, in turn, distinctively addresses the problems of efficiently selecting context for caching and managing the additional costs of context management in the cache. We demonstrate that our mechanism results in long-term efficiencies for the CMP that have not been observed in any previous study. The mechanism employs a novel, scalable, and selective context-caching agent implemented using the twin delayed deep deterministic policy gradient method. It further incorporates an adaptive context-refresh switching policy, a time-aware eviction policy, and a latent caching decision management policy. We point out in our findings that the additional complexity of adaptation introduced to the CMP through ACOCA is significantly justified, considering the cost and performance gains achieved. Our algorithm is evaluated using a real-world inspired heterogeneous context-query load and a data set based on parking-related traffic in Melbourne, Australia. This paper presents and benchmarks the proposed scheme against traditional and context-aware caching policies. We demonstrate that ACOCA outperforms the benchmarks in both cost and performance efficiency, i.e., up to 68.6%, 84.7%, and 67% more cost efficient compared to traditional data caching policies to cache context, redirector mode, and context-aware adaptive data caching under real-world-like circumstances.

Precast concrete block slope protection is widely used due to its advantages of easy detection and laying, ease of organization, and the limited time required for construction. In order to prevent the soil or gravel bedding of precast concrete from being subjected to wind and wave pressures, the joints between precast concrete blocks are usually filled with mortar. However, the existing standards do not specify the width or material of the joints. Furthermore, excessively wide mortar joints or shrinkage of the mortar can result in loss, a hollowed-out cushion, and damage to the slope, thus compromising the quality of slope protection engineering. To establish standards for controlling the quality of slope protection seams, this paper designed and conducted a physical model test of precast concrete block revetment seams. By embedding pore water pressure sensors in the cushion layer, changes in the pore water pressure were observed under varying conditions, including different water pore pressure sensor locations, water levels in front of the embankment, and different joint widths. Based on the test results, design standards for joint widths and recommendations for the treatment of joint mortar materials were proposed. After adding different amounts of a calcium oxide–calcium sulfoaluminate composite expansion agent (HME) into a joint mortar material, the paper also carried out a shear test on the contact surface between the joint mortar and the slope protection concrete after adding varying amounts of a calcium oxide-calcium sulfoaluminate composite expansion agent (HME) to the joint mortar material. Following a microporous structure test, recommendations for joint mortar construction treatment were proposed. The results indicate that the pore water pressure of the precast concrete slope protection cushion is closely related to the position of the cushion, the water level in front of the embankment, and the width of the paving seam. When the masonry seam width increased from 0.5 mm to 1 mm and from 1 mm to 1.5 mm, the variation ranges of the pore water pressure were 40–80% and 6–20%, respectively, with the latter being significantly lower than the former. Therefore, in practical engineering, joint treatment should take into account the impact of the cushion position, the water level in front of the dike, and the joint mortar width. Mortar shedding within the range of wave climbing height should be addressed promptly, and joint width should be controlled to below 1 cm as much as possible to effectively prevent damage to the cushion surface. The addition of an expansion agent can improve the bond strength of the concrete and mortar to a certain extent. The study found that an 8% content of the expansion agent resulted in the best mortar bond strength and the densest microstructure. These research findings can serve as a basis for the development of quality control standards for precast concrete slope protection.

In the process of producing winding coils for power transformers, it is necessary to detect the tilt angle of the winding, which is one of the important parameters that affects the physical performance indicators of the transformer. The current detection method is manual measurement using a contact angle ruler, which is not only time-consuming but also has large errors. To solve this problem, this paper adopts a contactless measurement method based on machine vision technology. Firstly, this method uses a camera to take pictures of the winding image and performs a 0° correction and preprocessing on the image, using the OTSU method for binarization. An image self-segmentation and splicing method is proposed to obtain a single-wire image and perform skeleton extraction. Secondly, this paper compares three angle detection methods: the improved interval rotation projection method, quadratic iterative least squares method, and Hough transform method and through experimental analysis, compares their accuracy and operating speed. The experimental results show that the Hough transform method has the fastest operating speed and can complete detection in an average of only 0.1 s, while the interval rotation projection method has the highest accuracy, with a maximum error of less than 0.15°. Finally, this paper designs and implements visualization detection software, which can replace manual detection work and has a high accuracy and operating speed.

The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels.

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater’s physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment.

The use of fitness apps to track physical exercise has been proven to promote weight loss and increase physical activity. The most popular forms of exercise are cardiovascular training and resistance training. The overwhelming majority of cardio tracking apps automatically track and analyse outdoor activity with relative ease. In contrast, nearly all commercially available resistance tracking apps only record trivial data, such as the exercise weight and repetition number via manual user input, a level of functionality not far from that of a pen and paper. This paper presents LEAN, a resistance training app and exercise analysis (EA) system for both the iPhone and Apple Watch. The app provides form analysis using machine learning, automatic repetition counting in real time, and other important but seldom studied exercise metrics, such as range of motion on a per-repetition level and average repetition time. All features are implemented using lightweight inference methods that enable real-time feedback on resource-constrained devices. The performance evaluation includes a user survey and benchmarking of all data science features using both ground-truth data from complementary modalities and comparisons with commercial apps.