Improved accuracy in IoT-Based water quality monitoring for aquaculture tanks using low-cost sensors: Asian seabass fish farming.

Traditional approaches to monitoring water quality in aquaculture tanks present numerous limitations, including the inability to provide real-time data, which can lead to improper feeding practices, reduced productivity, and potential environmental risks. To address these challenges, this study aimed to create an accurate water quality monitoring system for Asian seabass fish farming in aquaculture tanks. This was achieved by enhancing the accuracy of low-cost sensors using simple linear regression and validating the IoT system data with YSI Professional Pro. The system's development and validation were conducted over three months, employing professional devices for accuracy assessment. The accuracy of low-cost sensors was significantly improved through simple linear regression. The results demonstrated impressive accuracy levels ranging from 76% to 97%. The relative error values which range from 0.27% to 4% demonstrate a smaller range compared to the values obtained from the YSI probe during the validation process, signifying the enhanced accuracy and reliability of the IoT sensor by using simple linear regression. The system's enhanced accuracy facilitates convenient and reliable real-time water quality monitoring for aquafarmers. Real-time data visualization was achieved through a microcontroller, Thingspeak, Virtuino application, and ESP 8266 Wi-Fi module, providing comprehensive insights into water quality conditions. Overall, this adaptable tool holds promise for accurate water quality management in diverse aquatic farming practices, ultimately leading to improved yields and sustainability.

Catheterized chicken for training on ultrasound-guided vascular access: A simple, cost-effective, and effective model.

Ultrasound-guided vascular access is a medical procedure that is becoming increasingly common in daily practice and is recommended to avoid iatrogenic complications. One of the procedures with a high-risk rate of complications is the vascular puncture. However, training on this technique can be challenging due to the limited availability of simulation models. We propose a simple, cost-effective, and effective ultrasound-guided vascular access simulation model that utilizes chicken breast and a urine catheter to address this need.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning.

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring.

This study aimed to assess the performance, in terms of precision and accuracy, of a prototype (called "P.ALP"-Ph.D. Air Quality Low-cost Project) developed for monitoring PM2.5 concentration levels. Four prototypes were co-located with reference instrumentation in four different microenvironments simulating real-world and working conditions, namely (i) office, (ii) home, (iii) outdoor, and (iv) occupational environments. The devices were evaluated for a total of 20 monitoring days (approximately 168 h) under a wide range of PM2.5 concentrations. The performances of the prototypes (based on the light-scattering working principle) were tested through different statistical methods. After the data acquisition and data cleaning processes, a linear regression analysis was performed to assess the precision (by comparing all possible pairs of devices) and the accuracy (by comparing the prototypes against the reference instrumentation) of the P.ALP. Moreover, the United States Environmental Protection Agency (US EPA) criteria were applied to assess the possible usage of this instrumentation, and to evaluate the eventual error trends of the P.ALP in the data storage process, Bland-Altman plots were also adopted. The outcomes of this study underlined that the P.ALP performed differently depending on the microenvironment in which it was tested and, consequently, on the PM2.5 concentrations. The device can monitor PM2.5 variations with acceptable results, but the performance cannot be considered satisfactory at extremely low and remarkably high PM2.5 concentrations. Thanks to modular components and open-source software, the tested device has the potential to be customized and adapted to better fit specific study design needs, but it must be implemented with ad hoc calibration factors depending on the application before being used in field.

Direct and rapid thermal shock for recycling spent graphite in lithium-ion batteries.

Due to the rapid increase in the number of spent lithium-ion batteries, there has been a growing interest in the recovery of degraded graphite. In this work, a rapid thermal shock (RTS) strategy is proposed to regenerate spent graphite for use in lithium-ion batteries. The results of structural and morphological characterization demonstrate that the graphite is well regenerated by the RTS process. Additionally, an amorphous carbon layer forms and coats onto the surface of the graphite, contributing to excellent rate performance. The regenerated graphite (RG-1000) displays excellent rate performance, with capacities of 413 mAh g-1 at 50 mA g-1 and 102.1 mAh g-1 at 1000 mA g-1, respectively. Furthermore, it demonstrates long-term cycle stability, maintaining a capacity of 80 mAh g-1 at 1000 mA g-1 with a capacity retention of 78.4 % after 600 cycles. This RTS method enables rapid and efficient regeneration of spent graphite anodes for lithium-ion batteries, providing a facile and environmentally friendly strategy for their direct regeneration.

MaRGE: A graphical environment for MaRCoS.

The open-source console MaRCoS, which stands for "Magnetic Resonance Control System", combines hardware, firmware and software elements for integral control of Magnetic Resonance Imaging (MRI) scanners. Previous developments have focused on making the system robust and reliable, rather than on users, who have been somewhat overlooked. This work describes a Graphical User Interface (GUI) designed for intuitive control of MaRCoS, as well as compatibility with clinical environments. The GUI is based on an arrangement of tabs and a renewed Application Program Interface (API). Compared to the previous versions, the MaRGE package ("MaRCoS Graphical Environment") includes new functionalities such as the possibility to export images to standard DICOM formats, create and manage clinical protocols, or display and process image reconstructions, among other features conceived to simplify the operation of MRI scanners. All prototypes in our facilities are commanded by MaRCoS and operated with the new GUI. Here we report on its performance on an experimental 0.2 T scanner designed for hard-tissue, as well as a 72 mT portable scanner presently installed in the radiology department of a large hospital. The possibility to customize, adapt and streamline processes has substantially improved our workflows and overall experience.

Removal of antiretroviral drugs from wastewater using activated macadamia nutshells: Adsorption kinetics, adsorption isotherms, and thermodynamic studies.

Antiretroviral drugs (ARVDs) have been extensively employed in health care to improve the quality of life and lifecycle longevity. However, overuse and improper disposal of ARVDs have been recognized as an emerging concern whereby wastewater treatment major recipients. Therefore, in this work, the activated macadamia nutshells (MCNs) were explored as low-cost adsorbents for the removal of ARVDs in wastewater samples. Fourier transform infrared spectroscopy (FTIR), Scanning Electron microscopy (SEM), Brunauer-Emmet-Teller (BET), and Powder X-ray diffraction (PXRD). The highest removal efficiency (R.E) was above 86% for the selected analytes nevirapine, abacavir, and efavirenz. The maximum adsorption capacity of the functionalized MCN adsorbent was 10.79, 27.44, and 38.17 mg/g for nevirapine, abacavir, and efavirenz for HCl-modified adsorbent. In contrast, NaOH modified had adsorption capacities of 13.67, 14.25, and 20.79 mg/g. The FTIR showed distinct functional groups OH and CO, which facilitate the removal of selected ARVDs. From studying kinetics parameters, the pseudo-second-order (R2 = 0.990-0.996) was more dominant than the pseudo-first-order (R2 = 0.872-0.994). The experimental data was most fitted in the Freundlich model with (R2 close to 1). The thermodynamic parameters indicated that the adsorption process was spontaneous and exothermic. The study indicated that MCNs are an eco-friendly, low-cost, and effective adsorbent for the removal of nevirapine, abacavir, and efavirenz. PRACTITIONER POINTS: Modification macadamia nutshell with HCl and NaOH improved physio-chemical properties that yielded high removal efficiency compared with raw macadamia nutshells. Modification of macadamia by HCl showed high removal efficiency, which could be attributed to high interaction such as H-bonding that improves adsorption. The macadamia nutshell as an adsorbent showed so much robustness with regeneration studies yielding to about 69.64% of selected compounds.

An All-Soluble Fe/Mn-Based Alkaline Redox Flow Battery System.

Redox flow batteries (RFBs) are membrane-separated rechargeable flow cells with redox electrolytes, offering the potential for large-scale energy storage and supporting renewable energy grids. Yet, creating a cost-effective, high-performance RFB system is challenging. In this work, we investigate an Fe/Mn RFB alkaline system based on the [(TEA)Fe-O-Fe(TEA)]3-/4- and MnO4-/2- redox couples with a theoretical cell voltage of ∼1.43 V. This combination has not been systematically studied previously, but it can lead to a very low-cost and sustainable materials for high energy storage. Constant current cycling tests were performed at ±41 mA cm-2 between 20% and 80% SOC over 800 h (400 cycles) with an apparent Coulombic efficiency (CE) approaching 100%, while the voltage efficiency (VE) gradually decreased from ∼75.3% to ∼61.4% due to increasing internal resistances. The voltage efficiency loss can be mitigated through a periodic acid treatment to remove MnO2 deposits from the separator.

Robust capital cost optimization of generation and multitimescale storage requirements for a 100% renewable Australian electricity grid.

Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an interconnector between Australia's eastern and western grids. Subsequently, through an extensive sensitivity analysis, we investigate the impact of specific storage technologies and cost variations. Our findings demonstrate that the proposed interconnector offers a cost-effective solution, reducing generation and storage power capacity needs by 6 and 14%, respectively, resulting in 4% savings on overall investment costs. Moreover, the study's sensitivity analysis reveals that wind generation provides 50-70% of the energy demand for the least-cost solution. Despite storage inefficiencies, long-duration storage would need to be deployed to support power capacity for 2-4 days, representing 15-40% of peak demand, depending on future technology costs. Subsequently, achieving a fully renewable electricity sector in Australia requires a significant expansion of generation and storage infrastructure, with a 13-fold increase in storage power capacity and a 40-fold increase in storage energy capacity compared to existing levels.

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control.

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

Environmental Quality bOX (EQ-OX): A Portable Device Embedding Low-Cost Sensors Tailored for Comprehensive Indoor Environmental Quality Monitoring.

The continuous monitoring of indoor environmental quality (IEQ) plays a crucial role in improving our understanding of the prominent parameters affecting building users' health and perception of their environment. In field studies, indoor environment monitoring often does not go beyond the assessment of air temperature, relative humidity, and CO2 concentration, lacking consideration of other important parameters due to budget constraints and the complexity of multi-dimensional signal analyses. In this paper, we introduce the Environmental Quality bOX (EQ-OX) system, which was designed for the simultaneous monitoring of quantities of some of the main IEQs with a low level of uncertainty and an affordable cost. Up to 15 parameters can be acquired at a time. The system embeds only low-cost sensors (LCSs) within a compact case, enabling vast-scale monitoring campaigns in residential and office buildings. The results of our laboratory and field tests show that most of the selected LCSs can match the accuracy required for indoor campaigns. A lightweight data processing algorithm has been used for the benchmark. Our intent is to estimate the correlation achievable between the detected quantities and reference measurements when a linear correction is applied. Such an approach allows for a preliminary assessment of which LCSs are the most suitable for a cost-effective IEQ monitoring system.

A low-cost and eco-friendly recombinant protein expression system using copper-containing industrial wastewater.

The development of innovative methods for highly efficient production of recombinant proteins remains a prominent focus of research in the biotechnology field, primarily due to the fact that current commercial protein expression systems rely on expensive chemical inducers, such as isopropyl ß-D-thiogalactoside (IPTG). In our study, we designed a novel approach for protein expression by creating a plasmid that responds to copper. This specialized plasmid was engineered through the fusion of a copper-sensing element with an optimized multiple cloning site (MCS) sequence. This MCS sequence can be easily customized by inserting the coding sequences of target recombinant proteins. Once the plasmid was generated, it was introduced into an engineered Escherichia coli strain lacking copA and cueO. With this modified E. coli strain, we demonstrated that the presence of copper ions can efficiently trigger the induction of recombinant protein expression, resulting in the production of active proteins. Most importantly, this expression system can directly utilize copper-containing industrial wastewater as an inducer for protein expression while simultaneously removing copper from the wastewater. Thus, this study provides a low-cost and eco-friendly strategy for the large-scale recombinant protein production. To the best of our knowledge, this is the first report on the induction of recombinant proteins using industrial wastewater.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions.

This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO2, CO, NOX, and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions.

Characterization of Polylactic Acid Biocomposites Filled with Native Starch Granules from Dioscorea remotiflora Tubers.

Biocomposites were fabricated utilizing polylactic acid (PLA) combined with native starch sourced from mountain's yam (Dioscorea remotiflora Knuth), an underexplored tuber variety. Different starch compositions (7.5, 15.0, 22.5, and 30.0 wt.%) were blended with PLA in a batch mixer at 160 °C to produce PLA/starch biocomposites. The biocomposites were characterized by analyzing their morphology, particle size distribution, thermal, X-ray diffraction (XDR), mechanical, and dynamic mechanical (DMA) properties, water absorption behavior, and color. The results showed that the amylose content of Dioscorea remotiflora starch was 48.43 ± 1.4%, which corresponds to a high-amylose starch (>30% of amylose). Particle size analysis showed large z-average particle diameters (Dz0) of the starch granules (30.59 ± 3.44 µm). Scanning electron microscopy (SEM) images showed oval-shaped granules evenly distributed throughout the structure of the biocomposite, without observable agglomeration or damage to its structure. XDR and DMA analyses revealed an increase in the crystallinity of the biocomposites as the proportion of the starch increased. The tensile modulus (E) underwent a reduction, whereas the flexural modulus (Eflex) increased with the amount of starch incorporated. The biocomposites with the highest Eflex were those with a starch content of 22.5 wt.%, which increased by 8.7% compared to the neat PLA. The water absorption of the biocomposites demonstrated a higher uptake capacity as the starch content increased. The rate of water absorption in the biocomposites followed the principles of Fick's Law. The novelty of this work lies in its offering an alternative for the use of high-amylose mountain's yam starch to produce low-cost bioplastics for different applications.

Pilot-scale production of Bacillus subtilis MSCL 897 spore biomass and antifungal secondary metabolites in a low-cost medium.

OBJECTIVES: Bacillus subtilis is a plant growth promoting bacterium (PGPB) that acts as a microbial fertilizer and biocontrol agent, providing benefits such as boosting crop productivity and improving nutrient content. It is able to produce secondary metabolites and endospores simultaneously, enhancing its ability to survive in unfavorable conditions and eliminate competing microorganisms. Optimizing cultivation methods to produce B. subtilis MSCL 897 spores on an industrial scale, requires a suitable medium, typically made from food industry by-products, and optimal temperature and pH levels to achieve high vegetative cell and spore densities with maximum productivity. RESULTS: This research demonstrates successful pilot-scale (100 L bioreactor) production of a biocontrol agent B. subtilis with good spore yields (1.5 × 109 spores mL-1) and a high degree of sporulation (>80%) using a low-cost cultivation medium. Culture samples showed excellent antifungal activity (1.6-2.3 cm) against several phytopathogenic fungi. An improved methodology for inoculum preparation was investigated to ensure an optimal seed culture state prior to inoculation, promoting process batch-to-batch repeatability. Increasing the molasses concentration in the medium and operating the process in fed-batch mode with additional molasses feed, did not improve the overall spore yield, hence, process operation in batch mode with 10 g molasses L-1 is preferred. Results also showed that the product quality was not significantly impacted for up to 12 months of storage at room temperature. CONCLUSION: An economically-feasible process for B. subtilis-based biocontrol agent production was successfully developed at the pilot scale.

Review on the modifications of natural and industrial waste CaO based sorbent of calcium looping with enhanced CO2 capture capacity.

The calcium looping cycle (CaL) possesses outstanding CO2 capture capacity for future carbon-capturing technologies that utilise CaO sorbents to capture the CO2 in a looping cycle. However, sorbent degradation and the presence of inert materials stabilise the sorbent, thereby reducing the CO2 capture capacity. Consequently, the CaO sorbent that has degraded must be replenished, increasing the operational cost for industrial use. CaO sorbents have been modified to enhance their CO2 capture capacity and stability. However, various CaO sorbents, including limestone, dolomite, biogenesis calcium waste and industrial waste, exhibit distinct behaviour in response to these modifications. Thus, this work comprehensively reviews the CO2 capture capacity of sorbent improvement based on various CaO sorbents. Furthermore, this study provides an understanding of the effects of CO2 capture capacity based on the properties of the CaO sorbent. The properties of various CaO sorbents, such as surface area, pore volume, particle size and morphology, are influential in exhibiting high CO2 capture capacity. This review provides insights into the future development of CaL technology, particularly for carbon-capturing technologies that focus on the modifications of CaO sorbents and the properties that affect the CO2 capture capacity.

Improved Cordycepin Production by Cordyceps Militaris Using Corn Steep Liquor Hydrolysate as an Alternative Protein Nitrogen Source.

Cordycepin production in the submerged culture of Cordyceps militaris was demonstrated using hydrolyzed corn processing protein by-products, known as corn steep liquor hydrolysate (CSLH), as an alternative nitrogen source. The growth, metabolism, and cordycepin production of Cordyceps militaris were evaluated under various concentrations of CSLH induction. The results demonstrated that CSLH addition had positive effects on the growth and cordycepin production with various C. militaris strains. The optimum strain, C. militaris GDMCC5.270, was found to effectively utilize CSLH to promote mycelium growth and cordycepin production. Low concentrations of CSLH (1.5 g/L) in the fermentation broth resulted in 343.03 ± 15.94 mg/L cordycepin production, which was 4.83 times higher than that of the group without CSLH. This also enhanced the metabolism of sugar, amino acids, and nucleotides, leading to improved cordycepin biosynthesis. The increase in key amino acids, such as glutamic acid, alanine, and aspartic acid, in the corn steep liquor hydrolysate significantly enhanced cordycepin yield. The corn steep liquor hydrolysate was confirmed to be a cost-effective accelerator for mycelium growth and cordycepin accumulation in C. militaris, replacing partial peptone as a cheap nitrogen source. It serves as a suitable alternative for efficient cordycepin production at a low cost.

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection.

Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.