Enzymatic hydrolyzation of Cordyceps militaris mushroom extracts and its effect on spent hen chicken.

Objective: This study was aimed to investigate the effect of fresh and dried hydrolyzed Cordyceps militaris (CM) mushroom with proteolytic enzymes; bromelain (CMB), flavorzyme (CMF), and mixture of bromelain: flavorzyme (CMBF) on quality properties of spent hen chicken. Methods: Mushroom extract (CME) were combined with three proteolytic enzyme mixtures that had different peptidase activities; stem bromelain (CMB), flavorzyme (CMF), and mixture of stem bromelain:flavorzyme (CMBF) at (1 : 1). The effect of these hydrolysates was investigated on spent hen breast meat via dipping marination. Results: Hydrolyzation positively alters functional properties of CM protease. in which bromelain hydrolyzed group (CMB) displayed the highest proteolytic activity at 4.57 unit/mL. The antioxidant activity had significant increment from 5.32% in CME to 61.79% in CMB. A significantly higher emulsion stability index (ESI) and emulsification activity index (EAI) compared to CME were another result from hydrolyzation (p<0.05). Texture properties along with the shear force value and myofibrillar fragmentation index (MFI) were notably improved under CMB and CMBF in fresh condition. Marination with CM mushroom protease that previously hydrolyzed with enzymes were proven to also increase the nucleotide compounds, indicated by higher AMP and IMP in hydrolysate groups (p<0.05). The concentration of both total and insoluble collagen remained unchanged, meaning less effect from CM protease. Conclusion: This study suggested the hydrolyzation of CM protease with bromelain or a mixture of bromelain:flavourzyme to significantly improve functional properties of protease and escalate the taste-related nucleotide compounds and texture profiles from spent hen breast meat.

Associations of combined polygenic risk score and glycemic status with atrial fibrillation, coronary artery disease and ischemic stroke.

BACKGROUND: It is unknown whether high hemoglobin A1c (HbA1c) is associated with increases in the risk of cardiovascular disease among individuals with elevated genetic susceptibility. We aimed to investigate the association between HbA1c and atrial fibrillation (AF), coronary artery disease (CAD), and ischemic stroke according to the polygenic risk score (PRS). METHODS: The UK Biobank cohort included 502,442 participants aged 40-70 years who were recruited from 22 assessment centers across the United Kingdom from 2006 to 2010. This study included 305,605 unrelated individuals with available PRS and assessed new-onset AF, CAD, and ischemic stroke. The participants were divided into tertiles based on the validated PRS for each outcome. Within each PRS tertiles, the risks of incident events associated with HbA1c levels were investigated and compared with HbA1c < 5.7% and low PRS. Data were analyzed from November 2022 to May 2023. RESULTS: Of 305,605 individuals, 161,605 (52.9%) were female, and the mean (SD) age was 56.6 (8.1) years. During a median follow-up of 11.9 (interquartile range 11.1-12.6) years, the incidences of AF, CAD, and ischemic stroke were 4.6, 2.9 and 1.1 per 100 person-years, respectively. Compared to individuals with HbA1c < 5.7% and low PRS, individuals with HbA1c ≥ 6.5% and high PRS had a 2.67-times higher risk for AF (hazard ratio [HR], 2.67; 95% confidence interval (CI), 2.43-2.94), 5.71-times higher risk for CAD (HR, 5.71; 95% CI, 5.14-6.33) and 2.94-times higher risk for ischemic stroke (HR, 2.94; 95% CI, 2.47-3.50). In the restricted cubic spline models, while a U-shaped trend was observed between HbA1c and the risk of AF, dose-dependent increases were observed between HbA1c and the risk of CAD and ischemic stroke regardless PRS tertile. CONCLUSIONS: Our results suggest that the nature of the dose-dependent relationship between HbA1c levels and cardiovascular disease in individuals with different PRS is outcome-specific. This adds to the evidence that PRS may play a role together with glycemic status in the development of cardiovascular disease.

Air-soil cycling of oxygenated, nitrated and parent polycyclic aromatic hydrocarbons in source and receptor areas.

Polycyclic aromatic hydrocarbons (PAHs) and their oxygenated and nitrated derivatives, OPAHs and NPAHs, are semivolatile air pollutants which are distributed and cycling regionally. Subsequent to atmospheric deposition to and accumulation in soils they may re-volatilise, a secondary source which is understudied. We studied the direction of air-soil mass exchange fluxes of 12 OPAHs, 17 NPAHs, 25 PAHs and one alkylated PAH in two rural environments being influenced by the pollutant concentrations in soil and air, by season, and by land cover. The OPAHs and NPAHs in samples of topsoil, of ambient air particulate and gas phases and in the gas-phase equilibrated with soil were analysed by GC-APCI-MS/MS. The pollutants soil burdens show a pronounced seasonality, a winter maximum for NPAHs and PAHs and a summer maximum for OPAHs. One order of magnitude more OPAH and parent PAH are found stored in forest soil than in nearby grassland soil. Among a number of 3-4 ring PAHs, the OPAHs benzanthrone and 6H-benzo(c,d)pyren-6-one, and the NPAHs 1- and 2-nitronaphthalene, 9-nitrophenanthrene and 7-nitrobenz(a)anthracene are found to re-volatilise from soils at a rural background site in central Europe in summer. At a receptor site in northern Europe, net deposition of polycyclic aromatic compounds (PACs) prevails and re-volatilisation occurs only sporadic. Re-volatilisation of a number of PACs, including strong mutagens, from soils in summer and even in winter indicates that long-range atmospheric transport of primary PAC emissions from central Europe to receptor areas might be enhanced by secondary emissions from soils.

Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging.

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

Robot Technology for Pork and Beef Meat Slaughtering Process: A Review.

Recently, many slaughterhouses have begun to introduce automation and quality evaluation sensing equipment to the slaughter processing line to overcome insufficient human resources, improve the efficiency of the slaughter process, and standardize meat quality. Various processing instruments and sensing technologies may be used depending on the livestock to be slaughtered, but a standardized process design for a smart slaughterhouse remains to be established. Slaughterhouses are becoming more industrialized, leveraging data collection and analysis to drive growth and increase production. Therefore, slaughterhouse automation is essential for meeting production demand, and an optimized design suitable for the size of each company is required to maximize economical equipment and systems. We introduce robot technology used in the slaughterhouse and detail the visceral laparotomy, carcass preprocessing, and deboning robot technology. In this study, we examine slaughterhouse automation equipment and technologies, focusing on optimizing the processing lines, the direction of application, and the hygiene of robot technique. We hope this review will provide insight into slaughterhouse automation for decision making in the slaughter industry.

Associations of high-normal blood pressure and impaired fasting glucose with atrial fibrillation.

OBJECTIVE: To investigate the association of high-normal blood pressure (BP) and impaired fasting glucose (IFG) with the risk of atrial fibrillation (AF) in two cohorts. METHODS: The Korean National Health Insurance Service-Health Screening (K-NHIS-HealS, 2002-2003, follow-up until 2013) Study and the UK Biobank (2007-2010, follow-up until 2021) were evaluated. We used Cox proportional hazards regression models to evaluate the associations of high-normal BP and IFG with incident AF. RESULTS: In the K-NHIS-HealS and the UK Biobank, 2346 and 5314 incident AF events were recorded during the mean follow-up of 7.4 and 11.8 years. The adjusted HRs (95% CIs) for AF in the Korean and UK cohorts were 1.11 (1.02 to 1.21) and 1.07 (1.01 to 1.13) in individuals with high-normal BP; and 1.14 (1.04 to 1.25) and 1.10 (1.01 to 1.20) in individuals with IFG, respectively. The AF risk showed a dose-response relationship with BP and fasting blood glucose level. The risk of incident AF was increased by the combination of high-normal BP and IFG. CONCLUSIONS: In healthy individuals, high-normal BP and IFG were important risk factors for AF. When high-normal BP and IFG were combined, the risk of new-onset AF was significantly increased. These findings may suggest that lifestyle interventions for high-normal BP and IFG should be considered to reduce the risk of AF.

Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System.

Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192-1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs.

Design of a High-Efficiency DC-DC Boost Converter for RF Energy Harvesting IoT Sensors.

In this paper, an optimal design of a high-efficiency DC-DC boost converter is proposed for RF energy harvesting Internet of Things (IoT) sensors. Since the output DC voltage of the RF-DC rectifier for RF energy harvesting varies considerably depending on the RF input power, the DC-DC boost converter following the RF-DC rectifier is required to achieve high power conversion efficiency (PCE) in a wide input voltage range. Therefore, based on the loss analysis and modeling of an inductor-based DC-DC boost converter, an optimal design method of design parameters, including inductance and peak inductor current, is proposed to obtain the maximum PCE by minimizing the total loss according to different input voltages in a wide input voltage range. A high-efficiency DC-DC boost converter for RF energy harvesting applications is designed using a 65 nm CMOS process. The modeled total losses agree well with the circuit simulation results and the proposed loss modeling results accurately predict the optimal design parameters to obtain the maximum PCE. Based on the proposed loss modeling, the optimally designed DC-DC boost converter achieves a power conversion efficiency of 96.5% at a low input voltage of 0.1 V and a peak efficiency of 98.4% at an input voltage of 0.4 V.

Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds.

The viability status of seeds before sowing is important to farmers as it allows them to make yield predictions. Monitoring the seed quality in a rapid and nondestructive manner may create a perfect solution, especially for industrial sorting applications. However, current offline laboratory-based strategies employed for the monitoring of seed viability are time-consuming and thus cannot satisfy industrial needs where there is a substantial number of seeds to be analyzed. In this study, we describe a prototype online near-infrared (NIR) hyperspectral imaging system that can be used for the rapid detection of seed viability. A wavelength range of 900-1700 nm was employed to obtain spectral images of three different varieties of naturally aged watermelon seed samples. The partial least square discriminant analysis (PLS-DA) model was employed for real-time viability prediction for seed samples moving through a conveyor unit at a speed of 49 mm/sec. A suction unit was further incorporated to develop the online system and it was programmatically controlled to separate the detected viable seeds from nonviable ones. For an external validation sample set showed classification accuracy levels of 91.8%, 80.7%, and 77.8% in relation to viability for the three varieties of watermelon seed with healthy seedling growth. The regression coefficients of the classification model distinguished some chemical differences in viable and nonviable seed which was verified by the chromatographic analysis after the detection of the proposed online system. The results demonstrated that the developed online system with the viability prediction model has the potential to be used in the seed industry for the quality monitoring of seeds.

Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease.

Background: In patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models. Methods: Eight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)]. Twelve clinical and blood biomarker features assessed on admission were used to inform the models. We compared the best-performing ML model and established the pre-test probability of CAD (updated Diamond-Forrester and CAD consortium) models. Results: The CatBoost algorithm model showed the best performance (area under the receiver operating characteristics, AUROC, 0.796, and 95% confidence interval, CI, 0.740-0.853; Matthews correlation coefficient, MCC, 0.448) compared to the seven other algorithms. The CatBoost algorithm model improved risk prediction compared with the CAD consortium clinical model (AUROC 0.727; 95% CI 0.664-0.789; MCC 0.313). The accuracy of the ML model was 74.6%. Age, sex, hypertension, high-sensitivity cardiac troponin T, hemoglobin A1c, triglyceride, and high-density lipoprotein cholesterol levels contributed most to obstructive CAD prediction. Conclusion: The ML models using clinically relevant biomarkers provided high accuracy for stable obstructive CAD prediction. In real-world practice, employing such an approach could improve discrimination of patients with suspected obstructive CAD and help select appropriate non-invasive testing for ischemia.

Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.

PURPOSE: Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography. METHODS: This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively. RESULTS: By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness. CONCLUSION: Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.

Association of Light-Intensity Physical Activity With Mortality in the Older Population: A Nationwide Cohort Study.

Background: There is a paucity of information about mortality related to light-intensity physical activity (LPA) in the older population. We examine the associations between physical activity and mortality, focusing on the effect of light-intensity physical activity and the dose-response relationship between physical activity and mortality. Methods: We analyzed a total of 58,537 participants aged ≥ 65 years (mean age, 73.9 ± 5.8 years; male, 36.0%) in the Korean National Health Insurance Service database between 2009 and 2012. The Date of the end of follow-up was December 31, 2013. Individuals were divided into four categories according to physical activity intensity: totally sedentary (43.3%), LPA only (35.8%), LPA and moderate- to vigorous-intensity physical activity (MVPA) (16.3%), MVPA only (4.5%). Physical activity was quantified using standardized self-reported questionnaires which composed of the duration and frequency of physical activity. Results: During a mean follow-up of 39.6 ± 14.0 months, 5,651 (9.7%) deaths occurred. Compared with totally sedentary individuals, those in the LPA only, LPA and MVPA, and MVPA only groups showed 26% [hazard ratio (HR) 0.74, 95% confidence interval (CI) 0.68-0.82], 27% (HR 0.73, 95% CI 0.63-0.84), and 34% (HR 0.66, 95% CI 0.54-0.79) lower all-cause mortality risk, showing an inverse relationship between physical activity intensity and mortality risk. In contrast, the LPA only, LPA and MVPA, and MVPA only groups represented a stronger inverse association with CV mortality (LPA: HR 0.76, 95% CI 0.62-0.92; LPA with MVPA: HR 0.74, 95% CI 0.55-0.999; MVPA, HR 0.57, 95% CI 0.37-0.87). Among participants performing LPA alone, participants performing less than the recommended dose of physical activity had lower all-cause mortality than those with sedentary activity (1-249 MET-min/week: HR 0.74, 95% CI 0.67-0.82, 250-499 MET-min/week: HR 0.65, 95% CI 0.59-0.72). Conclusion: Physical activity, even low doses of LPA, was associated with reduced mortality risk in the elderly population. This study may motivate sedentary individuals to engage in any physical activity for mortality benefits.

Improvement of the UV Barrier and Antibacterial Properties of Crosslinked Pectin/Zinc Oxide Bionanocomposite Films.

Pectin-based antibacterial bionanocomposite films were prepared by crosslinking with calcium chloride (CaCl2) and mixing with zinc oxide nanoparticles (ZnO-NPs) at various concentrations (0.5%, 1%, and 1.5% w/w, based on pectin). Crosslinking with 1% CaCl2 significantly (p < 0.05) improved the tensile strength of the pectin films, although their elongation at break was decreased. The UV-light barrier property of the pectin/ZnO bionanocomposite films was significantly (p < 0.05) improved with increasing ZnO-NP concentrations. In addition, the bionanocomposite films incorporating 1.5% ZnO-NPs showed excellent antibacterial effects against both Escherichia coli and Staphylococcus aureus, inhibiting over 99% of the bacteria. Therefore, the developed crosslinked pectin/ZnO bionanocomposite films show great potential as active packaging materials with excellent UV-blocking and antibacterial properties.

Association of proteinuria and incident atrial fibrillation in patients with diabetes mellitus: a population-based senior cohort study.

Diabetes mellitus (DM) is considered an independent risk factor for atrial fibrillation (AF). The excess risk in relation to the presence of proteinuria has not been well elucidated. Our aim was to determine the association between the incidence of AF and proteinuria in diabetic population. A total of 240,499 individuals aged ≥ 60 years from the Korea National Health Insurance Service-Senior cohort from 2004 to 2014 were included. 4.2% of individuals with DM and 3.7% of controls were diagnosed with AF during a median follow-up period of 7.2 years. Amongst controls (participants without proteinuria and DM), DM only, proteinuria only, and DM with proteinuria groups, the crude incidences of AF were 0.58, 0.70, 0.96, 1.24 per 100 person-years respectively. Compared with controls, the weighted risk of AF was increased by 11% (hazard ratio = 1.11, 95% confidence interval = 1.02-1.20, P = .001), 48% (hazard ratio = 1.48, 95% confidence interval = 1.30-1.69, P < .001), and 66% (hazard ratio = 1.66, 95% confidence interval = 1.26-2.18, P < .001) in the DM only, proteinuria only, and DM with proteinuria groups, respectively (P for trend < .001). Degree of proteinuria in diabetic patients was associated with a significantly higher rate of incident AF in dose dependent manner. Thus, assessing proteinuria by a simple urine dipstick test could provide a useful adjunct to risk assessment for AF in elderly population with DM.

Microplastic removal in conventional drinking water treatment processes: Performance, mechanism, and potential risk.

The effectiveness of traditional drinking water treatment plants for the removal of Microplastics (MPs) in the size range of tens of micrometers is currently uncertain. This study investigated the behavior and removal efficiency of four different sized polystyrene MPs (10-90 µm in diameter) in a simulated cascade of coagulation/sedimentation, sand filtration, and UV-based oxidation over technically relevant time frames. In the coagulation and sand filtration steps, the larger the MP size, the better it was removed. The coagulant type and water characteristics (i.e., pH and the presence of natural organic matter) influenced the coagulation efficiency for MPs. X-ray microcomputed tomography technique and two-site kinetic modeling were used to identify the mechanisms involved in sand filtration. The MPs > 20 µm could be completely retained in sand by straining, while the attachment to the sand surface was likely responsible for the retention of MPs < 20 µm. However, approximately 16% of 10 µm MPs injected passed through the sand, which were further fragmented by UV oxidation. UV/H2O2 treatment promoted the MP fragmentation and chemical leaching more significantly than UV treatment, resulting in a higher toxicity for UV/H2O2-treated water. Our findings pave the way for deeper understanding of how MPs behave and transform in a sequential drinking water treatment process.

Economic Analysis of the Use of VCS2000 for Pork Carcass Meat Yield Grading in Korea.

Currently, the pork industry is incorporating in-line automation with the aim of increasing the slaughtered pork carcass throughput while monitoring quality and safety. In Korea, 21 parameters (such as back-fat thickness and carcass weight) are used for quality grading of pork carcasses. Recently, the VCS2000 system-an automatic meat yield grading machine system-was introduced to enhance grading efficiency and therefore increase pork carcass production. The VCS2000 system is able to predict pork carcass yield based on image analysis. This study also conducted an economic analysis of the system using a cost-benefit analysis. The subsection items of the cost-benefit analysis considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BC ratio), and each method was verified through sensitivity analysis. For our analysis, the benefits were grouped into three categories: the benefits of reducing labor costs, the benefits of improving meat yield production, and the benefits of reducing pig feed consumption through optimization. The cost-benefit analysis of the system resulted in an NPV of approximately 615.6 million Korean won, an IRR of 13.52%, and a B/C ratio of 1.65.

Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model.

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000-2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

An Energy-Efficient UWB Transmitter with Wireless Injection Locking for RF Energy-Harvesting Sensors.

An ultralow-power ultrawideband (UWB) transmitter with an energy-efficient injection-locked radio frequency (RF) clock harvester that generates a carrier from an RF signal is proposed for RF energy-harvesting Internet-of-Things (IoT) sensor applications. The energy-efficient RF clock harvester based on the injection-locked ring oscillator (ILRO) is proposed to achieve optimal locking range and minimum input sensitivity to obtain an injection-locked 450 MHz clock in ultralow-power operation. A current-starved inverter-based delay stage is adopted that allows delay adjustment by bias voltage to minimize dynamic current consumption while maintaining a constant delay regardless of changes in process, supply voltage, and temperature (PVT). To minimize static current consumption, a UWB transmitter based on a digital-based UWB pulse generator and a pulse-driven switching drive amplifier is proposed. The proposed injection-locked RF clock harvester achieves the best RF input sensitivity of -34 dBm at a power consumption of 2.03 µW, enabling energy-efficient clock harvesting from low RF input power. In ultralow-power operation, a 23.8% locking range is achieved at the RF injection power of -15 dBm to cope with frequency changes due to PVT variations. The proposed UWB transmitter with RF clock harvester achieves the lowest energy consumption per pulse with an average power consumption of 97.03 µW and an energy consumption of 19.41 pJ/pulse, enabling operation with the energy available in RF energy-harvesting applications.

Economic Analysis of an Image-Based Beef Carcass Yield Estimation System in Korea.

To minimize production costs, reduce mistakes, and improve consistency, modern-day slaughterhouses have turned to automated technologies for operations such as cutting, deboning, etc. One of the most vital operations in the slaughterhouse is carcass grading, usually performed manually by grading staff, which creates a bottleneck in terms of production speed and consistency. To speed up the carcass grading process, we developed an online system that uses image analysis and statistical tools to estimate up to 23 key yield parameters. A thorough economic analysis is required to aid slaughterhouses in making informed decisions about the risks and benefits of investing in the system. We therefore conducted an economic analysis of the system using a cost-benefit analysis (the methods considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BCR)) and sensitivity analysis. The benefits considered for analysis include labor cost reduction and gross margin improvement arising from optimizing breeding practices with the use of the data obtained from the system. The cost-benefit analysis of the system resulted in an NPV of approximately 310.9 million Korean Won (KRW), a BCR of 1.72, and an IRR of 22.28%, which means the benefits outweigh the costs in the long term.