Efficient Test Research Articles

Image super-resolution based on improved ESRGAN and its application in camera calibration

Camera calibration is a key technique in the field of computer vision. The Zhang’s method is currently the most commonly used camera calibration method, but its accuracy is mainly dependent on the image quality, thusly constrained by camera hardware conditions. Considering that high-performance cameras are often expensive, a cost-effective method to improve the quality of calibration images is of great practical value. Without changing the existing hardware conditions, image super-resolution can be considered to enhance the quality of the calibration image. Image super-resolution (SR) is the process of reconstructing an image from low resolution (LR) to high resolution (HR). It can enhance the clarity and detail of the calibration images, which will be beneficial in improving the accuracy of camera calibration. However, there is very little research on the application of image super-resolution to camera calibration. Therefore, this study innovatively proposed a method and process for applying image super-resolution in camera calibration. Firstly, the ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) was optimized and improved, followed by the proposal of the MMRSRGAN (Multi-scale Multi-adaptive-weights Residual Super-Resolution Generative Adversarial Networks). Two methods for local image super-resolution were also proposed for efficient training and testing of datasets, and two evaluation metrics based on reprojection error were proposed evaluate the effectiveness of the proposed model. Training and testing experiments of image super-resolution for camera calibration were conducted on both virtual and real image datasets. The proposed MMRSRGAN was compared with several advanced image super-resolution networks developed in recent years across multiple dimensions and indicators. The positive results have demonstrated the feasibility of applying image super-resolution to camera calibration and showcased the good performance of the MMRSRGAN.

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.

In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods. Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

Hardware Testing Methodologies for Wide Bandgap High-Power Converters

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor-based converter bring-up before the control validation phase steps in. The proposed methods enable thorough evaluation and validation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilized the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT was further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test was introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements were conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

Use of NIR in COVID-19 Screening: Proof of Principles for Future Application.

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Development and validation of a model for early prediction of residual feed intake in beef cattle using plasma biomarkers

Identification of plasma biomarkers for feed efficiency in growing beef cattle offers a promising opportunity for developing prediction models to improve precision feeding strategies. However, these models must accurately predict feed efficiency at early stages of fattening. Our study aimed to evaluate the reliability of candidate biomarkers previously identified in late-fattening cattle when analyzed during early fattening stages and to develop diet-specific prediction equations for residual feed intake (RFI). From a total of 364 Charolais bulls across 7 cohorts, we selected 64 animals with extreme RFI values. The animals were fed either a corn‑ or grass-silage diets. These animals were chosen from 4 out of available 7 cohorts. Animals from three cohorts (24 high-RFI and 24 low-RFI, having a mean RFI difference of 1.48 kg/d) were used for biomarker confirmation and prediction model training. Animals from a fourth cohort (8 high-RFI and 8 low-RFI, having a mean RFI difference of 0.98 kg/d) were used for model external validation. Blood samples were collected at the beginning of the feed efficiency test (333±20 days), and plasma underwent targeted metabolomic for 630 metabolites, natural abundance of 15N (δ15N), insulin, and IGF-1 analysis. Seven previously identified plasma biomarkers for RFI in late-fattening beef cattle still kept their capability for discriminating low and high RFI animals when analyzed during early fattening stages (P < 0.05). Among these confirmed biomarkers, five were common for both grass- and corn-fed animals (creatinine, β-alanine, triglyceride TG18:0_34:2, symmetric dimethyl-arginine and phosphatidylcholine PC aa C30:2) while two were diet-specific (insulin-like growth factor 1 for grass silage-based diet, and isoleucine for corn silage-based diet. No new plasma biomarkers of RFI were identified at early-fattening stages (false discovery rate, FDR>0.05). Prediction models were developed based on seven confirmed RFI biomarkers analyzed during early-fattening. Two logistic regression models incorporating creatinine and either IGF-1 (for grass silage-based diet) or PC aa C30:2 (for corn silage-based diet) effectively distinguished between high and low-RFI animals with high sensitivity and specificity (area under the curve, AUC > 0.80). The biomarkers used in the models showed moderate to high repeatability between early and late fattening stages (45 < r <65). The models were successfully externally validated, with more than 85% of animals from the fourth cohort correctly classified. Once validated in larger cohorts and utilizing cost-effective and rapid analytical methods, these models could support precision feeding and breeding programs, aiming to reduce the cost of raising beef cattle.

ONLINE PRACTICE TEST BANK SOLUTION: A WEB-BASED REVIEW PLATFORM FOR LIBRARY AND INFORMATION SCIENCE PRACTITIONERS

Test construction and test administration in most institutions still adopt the traditional pen-and-paper method of examination, which is very tedious (Kotwal et al., 2016). Its lengthy process can limit the time instructors in devoting to their proactive instruction and assessment. The general purpose of the study is to develop an Online Practice Test bank for Library and Information Science Practitioners. To actualize the Online Practice Test bank, the researcher followed the three phases of the modified rapid application model: Planning or Analyze Phase, Design and Development or Prototyping Phase, and Testing. The result of the study revealed that the developed system was able to execute its different operations. Specifically, by providing an avenue to the contributors/review masters and the reviewees to communicate and interact regarding the different reviewers provided. The reviewees can also track their progress through the learning analytics feature of the system. The contributors/review masters can monitor if the review materials they have provided has efficient test items by accessing the test item analysis. The system also has undergone functionality testing through conducting test cases, which have also gained positive remarks. Based on the results of the developed Online Practice Test bank, it is concluded that the system attained its three objectives in having integrated features that would be a great help, especially to the future licensed professional librarians as they will have a platform where they do their review and assess their knowledge on the six core subjects of library science. It is therefore recommended that mobile or android-based user modules must be developed for it to be more accessible to the users. And also an automated intervention or scaffolding for coping with the results from the performance of the review. And it must have an option or platform for contributors outside the institution who want to share their review materials relevant to the Library and Information Science profession.

Metal-organic framework-derived hierarchical flower-like DyCo-layered double hydroxide amalgamated nitrogen-doped graphene for diphenylamine detection in fruit samples: Theoretical density functional theory interpretation

Diphenylamine (DPA) is an environmental pollutant that can be potentially toxic. As a result, it is crucial to use basic and affordable analytical techniques to detect DPA. Electrochemical detection of DPA is a cost-effective and simple method. Modifying the electrodes with nanomaterials can enhance the electrochemical characteristics and sensitivity of the sensor. Herein, metal-organic framework (MOF) derived dysprosium cobalt-layered double hydroxide integrated nitrogen-doped graphene (DyCo-LDH/NG) is reported for the fabrication of the DPA sensing platform. The electrochemical oxidation of DPA is enhanced by the exceptional electrocatalytic activity and electron transfer properties of the DyCo-LDH/NG nanocomposite. Interestingly, the glassy carbon electrode (GCE) modified with DyCo-LDH/NG nanocomposite demonstrates a large linear detection range (0.05-470μM) and a low limit of detection (0.012µM). The density functional theory (DFT) study is employed to examine the energy levels and electron transfer sites of DPA during the electro-oxidation process. Furthermore, the practical efficiency test of the developed DPA sensor demonstrates a substantial recovery in fruit samples.

Optimisation of detecting chronic thromboembolic pulmonary hypertension in acute pulmonary embolism survivors: the InShape IV study.

Chronic thromboembolic pulmonary hypertension (CTEPH) is often diagnosed late in acute pulmonary embolism survivors: more efficient testing to expedite diagnosis may considerably improve patient outcomes. The InShape II algorithm safely rules out CTEPH (failure rate 0.29%) while requiring echocardiography in only 19% of patients but may be improved by adding detailed reading of the computed tomography pulmonary angiography diagnosing the index pulmonary embolism. We evaluated 12 new algorithms, incorporating the CTEPH prediction score, ECG reading, N‑terminal pro-brain natriuretic peptide levels and dedicated computed tomography pulmonary angiography reading, in the international InShape II cohort (n=341) and part of the German FOCUS cohort (n=171). Evaluation criteria included failure rate, defined as the incidence of confirmed CTEPH in pulmonary embolism patients in whom echocardiography was deemed unnecessary by the algorithm, and the overall net reclassification index compared to the InShape II algorithm. The algorithm starting with computed tomography pulmonary angiography reading of the index pulmonary embolism for six signs of CTEPH, followed by ECG/N-terminal pro-brain natriuretic peptide level assessment and echocardiography resulted in the most beneficial change compared to InShape II, with a need for echocardiography in 20% (+5%), a failure rate of 0% and a net reclassification index of +3.5%, reflecting improved performance over the InShape II algorithm. In the FOCUS cohort, this approach lowered echocardiography need to 24% (-6%) and missed no CTEPH cases, with a net reclassification index of +6.0%. Dedicated computed tomography pulmonary angiography reading of the index pulmonary embolism improved the performance of the InShape II algorithm and may improve the selection of pulmonary embolism survivors who require echocardiography to rule out CTEPH.

Purification of a reactive dyes mixture solution by means of pre-hydrolysed coagulants in the standard tests and coagulating flow system

Reactive dyes, widely used in textiles, often leach into sewage due to incomplete bonding with fibers. Coagulation offers an effective solution, yet standard lab tests for coagulation efficiency lack realism and are significantly labour and time-consuming. To address this, a novel approach is proposed: optimizing coagulation through a prototype lab flow. This system automates solution flow, reagent dosing, coagulation, and sludge separation via sedimentation and filtration. Tests reveal effective coagulation of various reactive dyes with pre-polymerized Al coagulants, but limited efficacy with Fe(III) coagulant. The flow system demonstrates mostly better purification results than jar tests, achieving around 95% colour removal with Al coagulants. This study highlights the efficacy of coagulation and filtration in jar tests, offering a simplified yet efficient method for assessing coagulation performance.

A standing platform for cancer drug development using ctDNA-based evidence of recurrence.

The time required to conduct clinical trials limits the rate at which we can evaluate and deliver new treatment options to patients with cancer. New approaches to increase trial efficiency while maintaining rigor would benefit patients, especially in oncology, in which adjuvant trials hold promise for intercepting metastatic disease, but typically require large numbers of patients and many years to complete. We envision a standing platform - an infrastructure to support ongoing identification and trial enrolment of patients with cancer with early molecular evidence of disease (MED) after curative-intent therapy for early-stage cancer, based on the presence of circulating tumour DNA. MED strongly predicts subsequent recurrence, with the vast majority of patients showing radiographic evidence of disease within 18 months. Such a platform would allow efficient testing of many treatments, from small exploratory studies to larger pivotal trials. Trials enrolling patients with MED but without radiographic evidence of disease have the potential to advance drug evaluation because they can be smaller (given high probability of recurrence) and faster (given short time to recurrence) than conventional adjuvant trials. Circulating tumour DNA may also provide a valuable early biomarker of treatment effect, which would allow small signal-finding trials. In this Perspective, we discuss how such a platform could be established.

LITERATURE REVIEW: ADVANTAGES AND DISADVANTAGES OF BLACK BOX AND WHITE BOX TESTING METHODS

Software testing methods play a crucial role in ensuring the quality, security, and performance of applications. The two main approaches often used are black box and white box testing. This article presents a comprehensive literature review of the characteristics, advantages and disadvantages of both testing methods with the aim of providing in-depth insights for practitioners and researchers in the field of software engineering. The research method used is Systematic Literature Review (SLR), which is a technique of collecting information through the process of searching for literature based on research questions, filtering search results, evaluating, and analyzing relevant research from various academic database sources to gain a comprehensive understanding. Through the analysis of various literature studies, the results of this research are the characteristics of each testing method that is more effectively used in best practice to achieve efficient testing with the coverage of literature review sources in the range of 2020 to 2024. The review of these characteristics is based on seven criteria, namely: test focus, test base, tester's point of view, test case design, program error identification method, program code knowledge, and test effectiveness. In addition, this article presents a comparison of the two methods so that the differences in testing methods can be clearly recognised in terms of how they are implemented. Based on the results of review literature, both methods are better used together in order to ensure that the functional and internal code structure of the software is functioning properly.

An efficient multi‐probe enabled midfield over‐the‐air test method for 5G base station antenna pattern reconstruction

AbstractWith the accelerated deployment of 5G massive multi‐input multi‐output (MIMO)‐structured base stations (BSs), accurate and efficient testing of massive MIMO arrays is essential to ensure that the 5G massive MIMO antenna array can perform as expected. The increasing trend of both antenna element and array physical size makes massive MIMO antenna pattern measurement in the direct‐far‐field OTA environment unrealistic due to the signal attenuation, long measurement time, and ultra‐high test system construction cost. Therefore, the necessity of a compact, efficient, low‐complex, yet accurate OTA test method is evident, especially for 5G massive MIMO BS arrays. This paper proposes a novel multi‐probe‐enabled midfield (MF) OTA test method for 5G massive MIMO devices in compact measurement settings where antenna pattern measurement can be efficiently performed. The detailed theoretical analysis for the 5G massive MIMO array MF antenna pattern reconstruction method is presented, and the simulated validation results based on the typical 5G BS antenna arrays are provided and analysed, which demonstrate the effectiveness of the proposed MF OTA test method and can provide insights into the radio frequency parametric measurement for 5G massive MIMO devices.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Novel adaptive MPPT technique for enhanced performance of grid integrated solar photovoltaic system

In order to extract the maximum photovoltaic (PV) power under stochastic weather conditions, a maximum power point tracking (MPPT) technique is required. PV modules behave nonlinearly in unpredictable weather conditions, and the effectiveness of many control strategies under such conditions especially conventional ones—decreases significantly. It is essential to maximize the use of PV power in the system; thus, MPPT algorithms have been developed to ensure that grid integrated PV systems work best at the maximum power point regardless of weather conditions. This research presents a novel HLO-ANN (Horned Lizard optimized artificial neural network) MPPT technique for grid-integrated solar photovoltaic systems which is the improved version of the ANN-MPPT, developed using a novel HLO technique, which effectively optimizes weights of the neural network. The irradiance and temperature datasets from the NASA website have been utilized to train the neural network. The proposed methods have been evaluated by comparing their simulation results to other ANN optimized MPPT techniques: The results showed that the proposed method outperforms the other methods in terms of robustness, DC bus voltage regulation, tracking speed, convergence to the minimum error value, and tracking efficiency in maximizing the harvested power from the PV systems. Furthermore, the EN50530 MPPT efficiency test was carried out during both fast and slow-varying irradiance levels to examine the effectiveness of proposed algorithms.

An efficient branch‐and‐bound algorithm to optimize a function over a nondominated set

AbstractThis study introduces an algorithm based on the branch‐and‐bound approach for optimizing a main function over the nondominated set of a multiobjective integer programming (MOIP) problem. Initially, is optimized within the feasible solution set of the MOIP. A new efficiency test combining Benson's test with is then developed using an auxiliary optimization program. This program provides both an efficient solution and a lower bound for . Moreover, this solution is the best one for when compared to its alternative solutions for MOIP. Subsequently, efficient cuts are incorporated into the criteria space to eliminate dominated points. Furthermore, the algorithm is tailored to handle scenarios where the objective involves optimizing a linear combination of multiobjective programming criteria over the nondominated set. The study concludes by showcasing the superior performance of the proposed two algorithms through comparison with existing approaches on well‐known problem instances from the literature.

Tannic acid and Gallic Acid as Alternative Compounds for the Application as Preservatives in Cosmetic Products

Introduction: Tannic acid and gallic acid have been known as natural compounds with antibacterial activity. In this work, the antimicrobial efficiency of tannic acid (TA) and gallic acid (GA), as well as their mixtures, was tested. Method: During the research, the minimal inhibitory concentration, minimal bactericidal concentration, and fractional inhibitory concentration were defined. Based on them, for further study, mixtures of TA/GA in ratios 50:50 and 80:20 were chosen as a preservation mixture applied to the cosmetic emulsion. Results: The obtained cosmetics emulsions were analyzed for the total mesophilic aerobic bacteria count and the total combined yeast count. The preservation efficiency test was performed according to the ISO 11930 standard. A stability test for cosmetics formulation was also carried out. Conclusion: The preservation mixture of TA/GA in an 80:20 ratio incorporated in the cosmetics formulation showed good preservation efficiency and could be considered a natural origin preservative.

Detection of the stimulant clobenzorex using voltammetry and screen-printed electrodes: A simple and fast screening method for application in seized samples and oral fluid of drivers

Clobenzorex (CBZ) is a stimulant drug, recognized for its anorectic potential, legally used in some countries for obesity treatment. However, CBZ is banned in much of the world and has been often used illegally by drivers needing to stay alert for extended periods. Therefore, the rapid identification of CBZ in forensic samples is of paramount importance for public health and safety. In this context, we introduce an innovative electroanalytical screening method for detecting CBZ in seized tablet samples and human oral fluid using voltammetry with a graphite screen-printed electrode (SPE-Gr). The electrochemical detection was optimized in phosphate buffer solution (0.1 mol L−1, pH 7.0) using square wave voltammetry (SWV). The SPE-Gr combined with SWV exhibited a good stability of the electrochemical responses for CBZ detection, with a relative standard deviation of less than 5% across different days (N = 5) and using different SPEs (N = 3). A linear detection range of 2.5–100.0 μmol/L (R2 = 0.992) was achieved, with a low limit of detection (LOD) of 0.32 μmol L−1. Interference studies with other illicit drugs and adulterants confirmed the selectivity of the proposed method for CBZ detection. In addition, the presence of CBZ in seized tablet samples and authentic oral fluids, collected from drivers, was detected and identified using the proposed method, which results were also confirmed by HPLC-MS. Consequently, integrating SPE-Gr with SWV offers a reliable, rapid, sensitive, selective, reproducible, and direct approach for the preliminary qualitative and quantitative analysis of CBZ in forensic contexts. Furthermore, the proposed method provides an efficient oral fluid test for CBZ screening in drivers under the influence of drugs.

Effectiveness of Lifetime Fitness Course Activities in Improving Movement Efficiency

University physical education courses are meant to teach the fundamentals of various sports and exercise techniques. The main purpose of this study was to determine how effective lifetime fitness (LF) courses in higher education can enhance movement efficiency (ME). Eleven participants performed the Fusionetics movement efficiency test at the beginning, middle, and end of the academic semester. Overall ME scores showed a significant effect, whereby the scores increased from the pre- to the mid-test but fell from the mid- to the post-test. Both the 1- and 2-leg squat subtests revealed a similar pattern. The second half of the semester's increased use of endurance-based class activities may have caused the ME score to decline.

PSIII-15 Feeding patterns of yearling Angus bulls based on feed efficiency metrics

Abstract The study objective was to determine how feeding behavior relates to feed efficiency in yearling Angus bulls at the Cal Poly Bull test. Bulls (n = 102) were housed in four pens sorted according to initial body weight (BW; 460 ± 35, 407 ± 15, 374 ± 20, 323 ± 30; mean kg ± SD), resulting in 24 to 28 bulls/pen. Feeding behavior was monitored using an electronic feeding system (SmartFeed, C-Lock, Inc.) during an 85-d feed efficiency test. Four feeders in each pen were filled twice daily at approximately 0630 h and 1700 h. The number of visits to the feed bunk, visit duration, total feeding time, and hourly feed intake were determined from data collected from the feeding system. These data were summarized by hour in a 24-h period, across the 85-d period. Bulls were retrospectively classified into a high feed efficiency group (HIFE) and low feed efficiency group (LOFE) based on an assessment of their summed residual feed intake rank and their feed:gain ratio rank. Within each pen, 5 HIFE and 5 LOFE bulls were selected, resulting in 20 bulls/treatment used for the final analyses. Data were analyzed using the MIXED procedure in SAS 9.4. The model included the fixed effects of feed efficiency group (HIFE vs LOFE), hour (0 to 24), and the feed efficiency group × hour interaction. Bull within pen was considered a random effect and hour a repeated measure. LOFE bulls visited the bunk more frequently throughout the day during the entire testing period (P = 0.04). Differences between feed efficiency groups depended on hour and most differences were observed within 3 h of fresh feed delivery (P ≤ 0.02). A feed efficiency group × hour interaction (P &amp;lt; 0.001) indicated that LOFE bulls had longer feed bunk visit durations predominantly between fresh feed deliveries (P ≤ 0.048). LOFE bulls spent more time feeding throughout the entire day (P = 0.003) when compared with HIFE bulls. Feeding duration per hour was longer for LOFE bulls during the post-morning feeding period (7-h duration) and post-evening feeding period (4-h duration; P ≤ 0.045). More feed was consumed by LOFE bulls throughout the day (P &amp;lt; 0.001), particularly during the post-morning feeding period (P ≤ 0.05). Overall, bulls exhibiting decreased feed efficiency had different feeding behavior patterns in all behavioral categories than their more feed efficient counterparts, especially around feed delivery. Thus, more active bulls at the feed bunk were less feed efficient, and this behavioral pattern may be an important consideration in bull selection.

PSIX-28 Optimization of feeding trial length for evaluating feed efficiency in sheep

Abstract Feed efficiency tests are well established for beef cattle, but testing for sheep remains rare due to the lack of appropriate equipment and standardized protocols. To explore the optimum trial length for evaluating feed efficiency in sheep, freshly weaned Rideau Arcott ewe lambs [n = 80; 4to 5 mo of age and body weights (BW) of 16 to 25 kg] were enrolled in a 65-d feeding trial, following a 20-d adaptation period. A mixed ration containing barley gain and protein supplement pellets were offered twice daily, and lambs had ad libitum access to feed, water and bedding straw during the trial period. Daily feed intake of individual lambs was monitored with Vytelle SENSE feed bunks, BW was recorded biweekly, as well as on two consecutive days at the start and end of the trial. Lambs that had died or had abnormal growth patterns from sickness were removed, resulting in 62 lambs remaining in the final dataset. Optimum trial length for measuring feed intake (DMI) and average daily gain (ADG) were evaluated with R Software (v. 4.3.3) via a post-hoc analysis of constructing shortened trial lengths from the regular test. The DMI and ADG values from the shortened trial datasets were compared with those from regular trial days with correlation (Pearson and Spearman tests) and linear regression analyses. There were 64 shortened trial lengths for DMI by 1 d increments, and 15 sub-datasets for ADG consisting of trial lengths between every two BW measurements. Residual feed intake (RFI), residual average daily gain (RADG), and residual intake and BW gain (RIG) were calculated for and compared between the regular trial length (65 d) and favorable shortened trial lengths based on DMI and ADG evaluations. Correlation, regression and agreement analyses were used to determine the optimum trial lengths for RFI, RADG and RIG evaluations. Results showed that DMI and ADG measurements could be shortened to 42 (r &amp;gt; 0.95, R2 &amp;gt; 0.90, β &amp;gt; 0.95, P &amp;lt; 0.001) and 56 d (r &amp;gt; 0.96, R2 &amp;gt; 0.93, β &amp;gt; 0.96, P &amp;lt; 0.001), respectively. Additionally, trial length for RFI, RADG, and RG evaluations could be reduced to 56 d (r &amp;gt; 0.95, R2 &amp;gt; 0.91, β &amp;gt; 0.95, CCC &amp;gt; 0.95, P &amp;lt; 0.001), primarily due to the trial length requirement for accurate ADG measurement. Reducing the duration of trials for evaluating feed efficiency in sheep not only reduces testing costs, fostering greater uptake among sheep producers, but also allows for the evaluation of more lambs annually, thereby enhancing the overall productivity and profitability of the sheep industry.