Factory Settings Research Articles

Comprehensive tuning guide: IBM MDM, PME algorithm, services, and configurations

The research paper provides comprehensive guidance on tuning specifics to maximize the efficiency of IBM MDM, particularly in high-performance environments. It addresses several advanced topics and tuning guidelines, focusing on the architectural framework and the details required for sustained performance. Including the recommendations to Handle Long-Chained Duplicates in Suspect Duplicate Processing (SDP), Minimize Deadlocks During Duplicate Processing, and Advanced Database Configurations for High-Performance WebSphere Application Server (WAS) Java Virtual Machine (JVM) and Cache Optimization, and Thread and session management, upgrading the Java version to hava8, Garbage Collection (GC) and Heap Memory Settings, servlet caching, and reduced application logging, MDM Workbench recommendations, Services tuning, and Batch processor tuning. IBM Messaging Queue’s Max server connections and Queue Connection Factory Settings.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

Benevolent authoritarianism, paternalism and religious humanitarianism in Sri Lanka: a dependent or autonomous HR?

This article examines the cases of five Sri Lankan export-oriented companies, each situated within global production networks (GPN) with British companies at the apex. In examining their managerial practices, a strong similarity to the practices associated with Anglo-Saxon paternalism is identified. However, these observed parallels are a product of local circumstances and the particular location within the GPN rather than a consequence of any emulation of British practices. The resulting system reflects a high power-dependency relationship that employers hold over ­employees. In this sense, the emergent HR system is partly dependent on the position in the GPN and partly autonomous. The system is characterised by a gendered division of labour and by the proximity of managerial oversight over the issue of worker autonomy. The factory setting has lower levels of worker autonomy and dependency compared to tea harvesting. One outlying exception to the general paternalistic approach, in one of the companies studied, is where the religious and ethnic makeup of the workforce is not shared with management, leading to increased conflict with the unionised workforce. The nature of Sri Lankan paternalism is specific and endogenous, but the conditions creating this paternalism are likely to be replicated elsewhere.

Microbial composition and dynamics in environmental samples from a ready-to-eat food production facility with a long-term colonization of Listeria monocytogenes

Listeria monocytogenes is a foodborne pathogen of significant concern for the food industry due to its remarkable ability to persist through safety control efforts, posing a subsequent health threat to consumers. Understanding the microbial communities coexisting with L. monocytogenes in food processing environments provides insights into its persistence mechanisms. We investigated the microbial communities on non-food contact surfaces in a facility producing ready-to-eat foods, known to harbour a ST121 L. monocytogenes strain over multiple years. A 10-week sampling period was coordinated with the company and public health authorities. Metagenomic analysis revealed a stable microbial composition dominated by Pseudomonas fluorescens. While highly related populations were present in high-care production zones, distinctive taxa characteristic of specific areas were observed (e.g., Sphingomonas aerolata). Although Listeria spp. were not detected in metagenomes, they were detected in cultured samples, suggesting low relative abundance in factory settings. The findings suggest that a stable resident microbiota, with distinct adaptations to different areas within the factory, was selected for by their collective ability to survive control efforts in this environment. Listeria spp. was a member of this microbial community, albeit at low abundance, and may likewise benefit from the mutualism of the overall microbial community.

Forecasting models analysis for predictive maintenance

IntroductionThis study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency.MethodsThe research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes.ResultsA comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate.DiscussionThese findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.

Environmental Sustainability Analysis of Rotary-Screen Printing and Digital Textile Printing

Worldwide concern regarding the need for more sustainable textile supply chain prompted emphasis on innovation in the coloration process, often considered the most problematic segment of the supply chain. Digital textile printing, an emerging coloration technology, has the potential to improve sustainability significantly. This study undertakes an environmental sustainability analysis that compares the impacts of rotary-screen printing, the traditional, more established printing method, and digital textile printing. Researchers partnered with Creditex S.A.A., a vertically integrated textile company from Peru, to perform the research in a realistic factory setting. Creditex printed a 16-color design for an order of 1000m cotton fabric with reactive dye occupying both rotary-screen printing and digital textile printing and collected environmental impact data throughout the production process, including consumption, usage, and wastage. The findings suggest that digital textile printing is favorable to rotary-screen printing in terms of environmental sustainability impacts within the research context.

Exogenous putrescine application imparts salt stress-induced oxidative stress tolerance via regulating antioxidant activity, potassium uptake, and abscisic acid to gibberellin ratio in Zinnia flowers

This research was conducted to investigate the efficacy of putrescine (PUT) treatment (0, 1, 2, and 4 mM) on improving morphophysiological and biochemical characteristics of Zinnia elegans “State Fair” flowers under salt stress (0, 50, and 100 mM NaCl). The experiment was designed in a factorial setting under completely randomized design with 4 replications. The results showed that by increasing the salt stress intensity, the stress index (SSI) increased while morphological traits such as plant height decreased. PUT treatments effectively recovered the decrease in plant height and flower quality compared to the not-treated plants. Treatment by PUT 2 mM under 50 and 100 mM salt stress levels reduced the SSI by 28 and 35%, respectively, and increased plant height by 20 and 27% compared to untreated plants (PUT 0 mM). 2 mM PUT treatment also had the greatest effect on increasing fresh and dry biomass, number and surface area of leaves, flower diameter, internodal length, leaf relative water content, protein contents, total chlorophyll contents, carotenoids, leaf potassium (K+) content, and K+/Na+ ratio in treated plants compared to untreated control plants. The treatment of 2 mM PUT decreased the electrolyte leakage, leaf sodium (Na+) content, H2O2, malondialdehyde, and proline content. Furthermore, PUT treatments increased the activity of defense-related enzymes including catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and phenylalanine ammonium lyase (PAL), and reduced the abscisic acid (ABA) content while increased the level of gibberellin (GA) content compared to untreated samples under all different levels of salinity stress. In this research, enhancing the plant’s antioxidant system, increasing K+ absorption, K+/Na+ ratio, and reducing the ABA/GA ratio are likely the most important mechanisms of PUT treatment, which improved growth, and maintained the visual quality of zinnia flowers under salt stress conditions.

Optimal charging scheduling for Indoor Autonomous Vehicles in manufacturing operations

Indoor Autonomous Vehicles (IAVs) have become instrumental in modern logistics, particularly in dynamic operational environments. Their consistent availability is crucial for both timely task execution and energy efficiency in smart factories. Therefore, an inefficient charging schedule can waste energy, compromising production and warehousing efficiency. In addition, formulating an effective charging schedule is challenging due to the nonlinearity of its design and the uncertainties inherent in smart factory settings. In contrast to previous studies that either simplify the problem using linearization-based methods or approach it with computationally demanding algorithms, this paper efficiently addresses the nonlinear challenge, ensuring a closer alignment with real-world conditions. Hence, a simulation environment is first designed to replicate a smart factory, including validated models of IAVs, Charging Stations (CSs), and a variety of unpredictable static and dynamic obstacles. A comprehensive dataset is then provided from this simulation setup. Utilizing this dataset, a model tailored to ascertain the travel time of IAVs, accounting for inherent uncertainties, is trained using Deep Neural Networks (DNNs). Subsequently, a Mixed-Integer Nonlinear Programming (MINLP) problem is formulated to design the optimization task. Finally, integration of the DNNs’ model with the Branch-and-Bound (BnB) approach, forming the BnBD method, streamlines the determination of the optimal charging schedule. Experimental results highlight significant improvements in charging scheduling, establishing this approach as a viable and promising solution for manufacturing operations.

Covariate-constrained randomization in cluster randomized 2 × 2 factorial trials: application to a diabetes prevention study

BackgroundCluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs.MethodsMotivated by the BEGIN study—a CRT for weight loss among patients with pre-diabetes—we develop methods for performing CR in 2 × 2 factorial cluster randomized trials with a continuous outcome and continuous cluster-level covariates. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, the size of the constrained randomization space, and analysis strategies.ResultsCompared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small.ConclusionsCR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and type 1 error rates at their nominal levels.

Effectiveness of therapy of complicated forms of dental caries using an Er,Cr:YSGG laser with a wavelength of 2780 nm

BACKGROUND: The use of laser technologies in the treatment of complicated forms of dental caries is promising in dentistry. However, the available literature does not fully cover the use of the Er,Cr:YSGG laser (wavelength: 2780 nm) in the treatment of purulent pulpitis and pulp necrosis. AIM: To determine the effectiveness of therapy for complicated forms of dental caries using an erbium–chromium laser with a wavelength of 2780 nm for biomechanical treatment of root canals. MATERIALS AND METHODS: Overall, 106 patients were examined and divided into three groups: group 1 (control), 15 patients whose treatment was performed using a generally accepted method; group 2, 50 patients who were treated with erbium–chromium laser radiation at the second visit, immediately before root canal obturation; and group 3, 41 patients who were treated in one visit and divided into two subgroups. In group 3-A (n=18), the root canal was irradiated with a laser with factory settings, and in group 3-B (n=23), the frequency and power of the laser were increased from 20 to 40 Hz and from 1.25 to 1.5 Watts, respectively. RESULTS: In group 1, 59 microorganisms from all 17 strains were seeded before the treatment of the root canal system of teeth with chlorine-containing disinfectants. Moreover, the proportion of seeded microorganisms in each strain was 6.6%–40% (average value: 23.1%). After treatment, the seeding rate of Streptococcus mitis decreased from 40% to 13%, Streptococcus intermedius from 20% to 6.6%, Enterococcus spp. from 33.3% to 13.3%, and Candida spp. from 33.3% to 20%. In group 2, after complex biomechanical treatment of root canals and the use of a hydrokinetic laser, none of the 17 strains sown before treatment were observed. The Candida spp. (5.5%) were not neutralized due to the use of a hydrokinetic laser with a wavelength of 2780 nm in the factory settings in group 3-A (power, 1.25 W; pulse frequency, 20 Hz; air, 10%; water, off). The combined use of the traditional protocol and hydrokinetic Er,Cr:YSGG laser in group 3B enabled complete elimination of microorganisms from those sown before treatment (p 0.001). CONCLUSION: Treatment of purulent pulpitis and pulp necrosis in one visit using a hydrokinetic Er,Cr:YSGG laser with a wavelength of 2780 nm with increased parameters (i.e., power, 1.5 W; frequency, 40 Hz; air, 35%; water, 25%) is recommended.

Novel beer bitterness measurement instrument using optical fiber sensor

This paper presents a novel optical fiber sensor for measuring the bitterness of beer samples. The sensor is based on a Mach–Zehnder interferometer (MZI) composed of two long-period fiber gratings (LPFGs). The sensor exploits the sensitivity of the LPFGs to the refractive index of the surrounding medium, which varies according to the concentration of iso-α-acids that cause bitterness in beer. The sensor uses a two-point on-line self-calibrating procedure to estimate the bitterness of unknown samples by comparison with reference samples of known bitterness. The sensor performance was evaluated in both laboratory and factory settings, showing good repeatability, reproducibility, and low bias. The proposed sensor offers a fast, simple, and low-cost alternative to the conventional method of measuring beer bitterness based on UV spectroscopy. The sensor can be integrated into the Industry 4.0 framework to improve the quality and consistency of beer production.

Tunnel excavation slag volume measurement using triple-line structured-light vision: Rectification and optimization

The precise measurement of excavated tunnel slag volume is crucial for tunnel boring machine construction security and optimizing. Structured-light vision measurement method is a widely employed method for volume measurement of convey belt, comprising a camera and laser projector. However, maintaining alignment between the structured light plane and the belt is challenging, especially with machine vibrations, leading to significant errors in slag volume measurement. To address this problem, we propose an innovative excavated tunnel slag volume measurement method based on triple-line structured light vision. After capturing images with multiline structured light, our approach encompasses extracting centerlines, profile three-dimensional conversion, calibrating profile distortions with arbitrary structured light layouts, and registering to obtain the area of the structured light section. Finally, integral calculations along the movement speed of conveyor belt yield accurate, real-time measurements of tunnel slag volume. Extensive experiments in factory and construction site settings confirm the real-time capabilities, accuracy, and universal applicability of our approach.

Use of BIM with Modular Construction in Future Construction Techniques

Modular construction technology and applications are rapidly evolving. Modular construction is a process in which entire rooms or sections of rooms are built in a factory setting along with electrical, mechanical, and plumbing work and then transported to a final site for assembly. With modular construction, a building is built off-site, under controlled facility conditions, with the same materials and to the same codes and standards as conventionally built facilities, but in half the time. The modular construction method is used to build various types of buildings (whether they are apartment houses, office buildings, or hotels). This construction method is used for both permanent and relocatable projects. These projects can be built with two types of modules. These are 2D panels or 3D modules. These can be combined to form a third type, hybrid modular construction. Each has its advantages. 2D panels offer easy logistics and flexibility in building design and are mounted on site. Factory productivity is increased by using 3D volumetric solutions. They only need to be installed once they are delivered. The hybrid modular structure has the advantages of the previous two. With the recent development of Building Information Modeling (BIM), the use of modular construction methods in conjunction with BIM becomes more common. As with any method, this one has advantages as well as disadvantages. Disadvantages of this method, such as a higher number of complex decisions, front-loaded design, etc., can be solved with BIM. Furthermore, the BIM platform can resolve the disadvantages of traditional construction methods, such as the difficulty of pre-project planning and coordination among members of interdisciplinary professions. With BIM and the modular construction method, physical conflicts between the structural system and its mechanical, electrical, and plumbing systems can be easily identified early in the design process, and resolution can be expedited. This article includes general information about the modular construction method, future application scenarios, use, and advantages of BIM. The document analysis method, one of the qualitative methods, was used, and in the light of the data obtained, comments and scenarios were tried to be created about the future of BIM and modular construction techniques. What distinguishes this study is that the concept of quality is examined in detail by using these two methods together.

Sugarcane bagasse biochar boosts maize growth and yield in salt-affected soil by improving soil enzymatic activities

Salinization is a leading threat to soil degradation and sustainable crop production. The application of organic amendments could improve crop growth in saline soil. Thus, we assessed the impact of sugarcane bagasse (SB) and its biochar (SBB) on soil enzymatic activity and growth response of maize crop at three various percentages (0.5%, 1%, and 2% of soil) under three salinity levels (1.66, 4, and 8 dS m−1). Each treatment was replicated three times in a completely randomized block design with factorial settings. The results showed that SB and SBB can restore the impact of salinization, but the SBB at the 2% addition rate revealed promising results compared to SB. The 2% SBB significantly enhanced shoot length (23.4%, 26.1%, and 41.8%), root length (16.8%, 20.8%, and 39.0%), grain yield (17.6%, 25.1%, and 392.2%), relative water contents (11.2%, 13.1%, and 19.2%), protein (17.2%, 19.6%, and 34.9%), and carotenoid (16.3, 30.3, and 49.9%) under different salinity levels (1.66, 4, and 8 dS m−1, respectively). The 2% SBB substantially drop the Na+ in maize root (28.3%, 29.9%, and 22.4%) and shoot (36.1%, 37.2%, and 38.5%) at 1.66, 4, and 8 dS m−1. Moreover, 2% SBB is the best treatment to boost the urease by 110.1%, 71.7%, and 91.2%, alkaline phosphatase by 28.8%, 38.8%, and 57.6%, and acid phosphatase by 48.4%, 80.1%, and 68.2% than control treatment under 1.66, 4 and 8 dS m−1, respectively. Pearson analysis showed that all the growth and yield parameters were positively associated with the soil enzymatic activities and negatively correlated with electrolyte leakage and sodium. The structural equational model (SEM) showed that the different application percentage of amendments significantly influences the growth and physiological parameters at all salinity levels. SEM explained the 81%, 92%, and 95% changes in maize yield under 1.66, 4, and 8 dS m−1, respectively. So, it is concluded that the 2% SBB could be an efficient approach to enhance the maize yield by ameliorating the noxious effect of degraded saline soil.

P.036 Vagal nerve stimulation in three cases of continuous spike and wave in slow-wave sleep

Background: Continuous spike and waves during slow-wave sleep (CSWS) is a childhood-onset epileptic encephalopathy that is characterized by clinical seizures, electrical status epilepticus during sleep (ESES), and neurocognitive regression. Early intervention can preserve neurocognitive development, and vagus nerve stimulator (VNS) therapy had positive outcomes in the few previously reported case reports. We present three patients with intractable CSWS unresponsive to medications, who had a positive response to VNS therapy. Methods: Review of clinical records of three pediatric patients diagnosed with CSWS were compared for selected clinical outcomes and electrographic data both prior to and in the years following the initiation of VNS therapy. Results: Three patients now aged 13, 16 and 20 years, were treated with VNS following intolerance and a lack of response to multiple medications (5-9) for CSWS. The ketogenic diet was not an option. The CSWS resolved in all three patients, resulting in improved cognitive function. Patient 3 had resurgence of CSWS on EEG when the VNS settings inadvertently reset to the factory settings and improved with adjustment in the cycling. Conclusions: In patients who are unresponsive to medication, VNS provides an alternative option for resolving CSWS to preserve and, in some cases, potentially restore neurocognitive function.

Evaluation of Measurement System Analysis Techniques for Screw Shaft Manufacturing in Teaching Factory Setting

This study examines the Evaluation of Measurement System Analysis (MSA) Techniques for Screw Shaft Manufacturing in a Teaching Factory Setting at Politeknik STMI Jakarta. The research investigates the precision and accuracy of the measurement system using statistical methods such as Analysis of Variance (ANOVA), Number of Distinct Categories (NDC), and metrics like Total Gauge Repeatability and Reproducibility (GRR), Repeatability, and Reproducibility. Through comprehensive data collection, including multiple appraisers across various trials measured quantitative surveys and qualitative observations, Screw Shaft dimensions and tolerances. The collected data underwent thorough statistical analysis utilizing ANOVA to explore the sources of variation in measurements. The results of the MSA analysis provided critical insights into the measurement system's performance. Repeatability variability (9.97%) and reproducibility variability (3.80%) contribute to a total GRR of 10.67%, indicating an acceptable measurement system. Part-to-part variation emerged as the primary source of variability, emphasizing the importance of addressing part differences in quality control processes. Moreover, ANOVA highlighted the influence of different factors on measurement consistency. Part variations significantly impacted measurement variability, underscoring the need to manage part differences effectively. The assessment of repeatability and reproducibility revealed insights into measurement consistency and reliability. These findings carry significant implications for both industry and education. Understanding the factors affecting measurement variability enables the implementation of corrective measures to enhance product quality and consistency. Additionally, advancements in measurement techniques and quality management practices in Teaching Factory environments contribute to improved product quality and competitiveness in industrial settings. Continued research and evaluation are crucial for further enhancing measurement systems and manufacturing processes.

Evaluation of selected combustion parameters in a compression-ignition engine powered by hydrogenated vegetable oil (HVO)

The article carries out a detailed analysis and evaluation of indicators related to the combustion process (pressure and temperature in the engine combustion chamber, heat release rate, heat release fraction) in a JCB 444 TA4i compression-ignition engine fuelled with diesel and hydrogenated vegetable oil (HVO). During the empirical tests, the operation of the exhaust gas recirculation (EGR) system was stopped and no other changes were made to the engine settings (factory settings were used). In the first stage, the empirical tests were carried out at speed characteristics on an engine dynamometer. Then, an experiment was carried out at the engine crankshaft speed corresponding to the maximum torque - which consisted of determining the indicators related to the combustion process at a constant mass flow of fuel: diesel and HVO fuel. This provided information on the effect of hydrogenated vegetable oil on the combustion process in relation to the diesel engine feed. The conclusions drawn from the empirical study can be used to develop guidelines to change the operating map of a compression-ignition engine when it is fed with hydrogenated vegetable oil.

Application of deep learning in noise detection of electromechanical products

In recent years, machine learning (ML) techniques have been widely integrated into manufacturing processes. This study explores supervised learning-based machine learning models designed for noise detection. The approach involves feature engineering, extracting relevant features from signals to provide meaningful inputs for machine learning models. Various algorithms, including convolutional neural networks (CNN) and other deep learning architectures, are employed to capture complex dependencies within the data. Training processes involve optimization techniques to enhance model performance and generalizability. Performance evaluation on benchmark datasets compares the proposed model with existing noise detection methods. Results demonstrate the outstanding accuracy and robustness of the generated machine learning model in distinguishing noise from clear signals. Additionally, the study explores the transferability of these models to new scenarios, highlighting their adaptability in practical applications. Findings from this research contribute to advancing noise detection methods, showcasing the effectiveness of machine learning in addressing this crucial challenge. The proposed solution is cost-effective, structurally simple, and holds vast potential for widespread implementation in factory settings in the future.

Influence of biochar and NPK on soil chemical properties, growth and yield of cabbage (Brassica oleracea L.)

Few studies have examined how fertilizers affect soil chemical properties, cabbage (Brassica oleracea L.) yield, and nutrient uptake in Ghana. This study examined how corn cob biochar (CCB) and NPK (15:15:15) fertilizer affected cabbage growth, yield, soil chemical properties, and nutrient uptake. The study was conducted during the 2021 major season at Soil Research Institute, Kwadaso. A 3×3 factorial experiment set out in a Randomized Complete Block Design with three replications was conducted. The treatments applied were control (No fertilizer), 50% NPK, 100% NPK, 2,500 kg ha-1 CCB, 50% NPK + 2,500 kg ha-1 CCB, 100% NPK + 2,500 kg ha-1 CCB, 5,000 kg ha-1 CCB, 50% NPK + 5,000 kg ha-1 CCB and 100% NPK + 5,000 kg ha-1 CCB. Application of 100% NPK resulted in the largest (2.99 cm) stem diameter. Application of 100% NPK + 5,000 kg ha-1 CCB resulted in the tallest plants (42.3 cm) and cabbage leaf spread (71.23 cm). Application of 100% NPK + 5,000 kg ha-1 CCB resulted in the largest cabbage head circumference (67.43 cm). The 100% NPK + 2,500 kg ha-1 CCB gave the highest yield (40679 kg ha-1). 100% NPK + 2,500 kg ha-1 CCB increased nitrogen uptake, 50% NPK + 5,000 kg ha-1 increased phosphorus and calcium uptake, 100% NPK + 5,000 kg increased potassium uptake, and 50% NPK + 2,500 kg increased magnesium uptake. Therefore, it is suggested that CCB and NPK fertilizers be applied to enhance the soil’s physical and chemical properties, nutrient uptake, and other factors contributing to cabbage growth and yield.

Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles

Key messageWe validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations.Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.