Reduce Labor Costs Research Articles

Effect of loop reinforcement joints on the strength of eccentrically compressed reinforced concrete structures

Introduction. Using loop joints of working reinforcement in flexural reinforced concrete members reduces labor costs when installing reinforcement cages at the construction site. However, the analysis of the available research showed that they were carried out mainly for flexural members. In this regard, additional experimental studies were carried out to assess the effect of various types of design solutions of loop reinforcement joints on the strength of normal sections of eccentrically compressed reinforced concrete elements.Aim. To obtain the results of experimental studies and assess the effect of design solutions of loop reinforcement joints on the strength of eccentrically compressed reinforced concrete elements.Materials and methods. The research involved the results of tests of eccentrically compressed reinforced concrete elements with loop joints of working reinforcement. As part of the research, 7 series of samples with different design solutions of loop joints were tested. The total number of samples in the research comprised 22 items.Results. The research revealed the effect of design solutions of loop reinforcement joints on the strength of eccentrically compressed reinforced concrete elements – on average from 3 to 14 %, depending on the design solution of the loop joint.Conclusions. It was found that the design of eccentrically compressed elements should be carried out taking into account the design of the loop joint of longitudinal reinforcement.

Usefulness of laparoscopic inguinal hernia repair using the Endoscope Manipulator Robot (EMARO).

This study aimed to investigate the surgical outcomes of laparoscopic inguinal hernia repair using an Endoscope Manipulator Robot (EMARO). This retrospective study included 51 patients who underwent laparoscopic inguinal hernia repairs. The transabdominal preperitoneal approach (TAPP) has been used to treat inguinal hernias. The patients were divided into two groups: one group underwent laparoscopic surgery using EMARO (E-TAPP) and the other group underwent conventional laparoscopic surgery (L-TAPP). The EMARO is a pneumatically driven endoscope-holder robot. The surgical outcomes of laparoscopic inguinal hernia repair were compared between the two groups. Fifteen patients underwent E-TAPP, and 36 underwent L-TAPP. The L-TAPP operation requires two personnel, whereas E-TAPP can be performed by one surgeon. The median operation times of the E-TAPP and L-TAPP groups were 81min (range, 77-87) and 70min (range, 60-94), respectively, and the median blood loss was 5 mL (range, 1-5) and 2 mL (range, 1-5). However, these differences were not statistically significant. The setup time for EMARO was approximately 8min (range, 5-12). No patient experienced recurrence, and the postoperative complication rates were similar between the two groups. The number of patients who used postoperative analgesics in the E-TAPP and L-TAPP groups was four (n = 15) and 22 (n = 36), respectively, with a significant difference between the two groups (p = 0.042). Laparoscopic surgery using EMARO can reduce labor costs and postoperative pain. The surgical outcomes of the E-TAPP group were not inferior to those of the L-TAPP group, and E-TAPP could also be safely performed.

Development of modern irrigation systems for improving efficiency, reducing water consumption and increasing yields

The study was conducted to examine modern irrigation systems aimed at improving water efficiency, reducing water consumption and increasing crop yields. The study analysed the environmental and socio-economic aspects of modern irrigation systems, including their impact on the environment and social sustainability of agriculture. The study determined that the use of modern irrigation systems, such as drip irrigation and IoT-based automatic control systems, can significantly improve the efficiency of water use in agriculture. Data analysis demonstrated that such systems can reduce water consumption by up to 50% while increasing yields by up to 30%. The use of agricultural drones to monitor field conditions and precisely regulate irrigation helps to improve plant health and reduce labour costs. The study also determined that water recycling and reuse systems can significantly reduce the use of freshwater, which is especially important in regions with limited water resources. In general, modern irrigation technologies demonstrate high efficiency and economic feasibility, contributing to the sustainable development of agriculture. The study proved that the use of computer modelling and forecasting of plant water requirements provides optimal conditions for their growth, which further contributes to an increase in yields. The results of the study can be used in practice to develop and implement more efficient and environmentally friendly technologies in irrigation and drainage systems, which helps to reduce the negative impact on the environment through more efficient use of water resources and reduction of water runoff

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

ECLB: Efficient contrastive learning on bi-level for noisy labels

For contrastive learning has achieved remarkable success in self-supervised and supervised informative representation, learning with noisy labels based on contrastive learning is becoming the research consensus. However, under noisy labels, how to efficiently leverage informative representation of various levels and how to effectively screen reliable positive pairs for the optimization of the contrastive learning model are still challenges. To address these issues, we innovatively propose a method named efficient contrastive learning on bi-level for noisy labels (ECLB), which is jointly implemented by both self-supervised and supervised contrastive learning. For the accessible informative representation, we propose to perform contrastive learning at two different levels: (1) feature level, where feature representation is jointly optimized by supervised and self-supervised feature contrastive loss; (2) label level, where feature and label representation are optimized by label distribution supervised contrastive loss. Furthermore, to alleviate the impact of noisy labels on the selection of reliable positive pairs in supervised contrastive learning and to reduce labor cost and computational complexity, we propose an efficient adaptive mask, which is dynamically generated by label self-equality mask, prediction self-equality mask, label-prediction equality mask, and feature similarity mask. Extensive experiments show that our proposed method outperforms other state-of-the-art methods in terms of robustness and generalization. Our code is publicly available at: https://github.com/whyandbecause/ECLB

Multimodal fine-grained grocery product recognition using image and OCR text

Automatic recognition of grocery products can be used to improve customer flow at checkouts and reduce labor costs and store losses. Product recognition is, however, a challenging task for machine learning-based solutions due to the large number of products and their variations in appearance. In this work, we tackle the challenge of fine-grained product recognition by first extracting a large dataset from a grocery store containing products that are only differentiable by subtle details. Then, we propose a multimodal product recognition approach that uses product images with extracted OCR text from packages to improve fine-grained recognition of grocery products. We evaluate several image and text models separately and then combine them using different multimodal models of varying complexities. The results show that image and textual information complement each other in multimodal models and enable a classifier with greater recognition performance than unimodal models, especially when the number of training samples is limited. Therefore, this approach is suitable for many different scenarios in which product recognition is used to further improve recognition performance. The dataset can be found at https://github.com/Tubbias/finegrainocr.

Improvement in Productivity and Quality of Soilless Saffron Crops by Implementing Fertigation

Saffron cultivation is important in global agriculture and is mainly flourishing in Mediterranean climates. Although it originated in Asia Minor, it is widely grown in regions such as Iran, India, Spain, Morocco, Greece, and Italy. Labour-intensive harvesting, mainly by hand, characterises its production and offers substantial employment opportunities in cultivating areas. However, traditional saffron-producing countries such as Spain, Italy, and Greece have witnessed declining production due to labour demands and competition from low-wage countries. Mechanization remains unfeasible due to the delicate nature of the plant. To revitalise saffron cultivation, efforts have been focused on reducing labour costs, improving productivity, and improving quality through innovative techniques, such as soilless crops. In this study, the productivity and quality of saffron was evaluated in a soilless culture and three fertigation doses were evaluated: a control, consisting of Sonneveld and Voogt’s standard nutrient solution, and two treatments with two supplemented solutions, 30% K and 30% Ca. The results indicated that the solution with 30% K obtained higher corm productivity, as well as better quality saffron, as all the products of this treatment were included in Category I according to the ISO 3632 standard, while the quality of saffron grown with the control solution was lower.

ROI Analysis of RFID-Based Intelligent Inventory Management Systems

This paper investigates the return on investment (ROI) of RFID-based intelligent inventory management systems. RFID (Radio Frequency Identification) technology has revolutionized inventory management by providing real-time visibility, improved accuracy, and enhanced operational efficiency. The study employs a mixed-methods approach, integrating both qualitative insights from industry experts and quantitative data from surveys and existing literature. Initial setup costs, ongoing maintenance expenses, and tangible and intangible benefits are thoroughly analyzed. The findings reveal that while the initial investment in RFID technology can be substantial, the benefits in terms of reduced labor costs, minimized inventory shrinkage, and improved inventory accuracy significantly outweigh the costs. ROI calculations, including payback period and net present value (NPV), demonstrate the economic viability of RFID systems. Comparative analysis shows that RFID systems outperform traditional inventory management systems, especially in sectors such as retail and healthcare. This paper provides a comprehensive framework for evaluating the ROI of RFID technology and offers practical recommendations for businesses considering its adoption.

Keeping Up with the Joneses during an Economic Crisis: The Effect of Different Types of Pay Cuts on Employee Performance

ABSTRACT Many firms implement pay cuts to reduce labor costs during organizational crises and there are different ways to distribute pay cuts among employees. We experimentally investigate how employees respond to equal-share pay cuts compared to performance-based pay cuts. We predict and find that the effect of different ways of allocating pay cuts is moderated by employees’ relative performance before the pay cuts. Compared to equal-share pay cuts, performance-based pay cuts lead to higher performance but only for employees who underperform their peers before the pay cuts. Performance-based pay cuts also result in higher team performance. Interestingly, we also find that when pay cuts are caused by an organizational crisis, low performers do not perceive it as unfair to receive a larger share of pay reduction. Our findings contribute to both literature and practice on pay reduction during organizational crises. Data availability: Data are available from the authors upon request. JEL Classifications: D91; J33; M40.

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

Design and Development of Quadcopter for Agro-Chemical Spray in Agricultural Field

The advent of Agricultural Drones promises to revolutionize modern farming practices by employing autonomous drones for crop surveillance and pesticide application. Outfitting quadcopters with spraying mechanisms presents numerous advantages over traditional methods. Firstly, it facilitates precise and targeted application of agro-chemicals, thereby minimizing wastage and environmental pollution. Furthermore, quadcopters exhibit the capability to effortlessly navigate challenging terrains, accessing areas previously unreachable by conventional spraying equipment and ensuring comprehensive crop coverage. Additionally, their autonomous operation or remote human supervision enhances operational efficiency, resulting in reduced labor costs. This initiative explores the integration of unmanned aerial vehicles (UAVs) into agricultural practices, specifically emphasizing their role in precisely spraying agro-chemicals. The project encompasses the entire development cycle, from design and development to the deployment of customized agricultural drones capable of autonomous flight and accurate pesticide distribution. The incorporation of quadcopters for precise agro-chemical spraying marks a significant advancement in agricultural technology, offering precision, efficiency, and sustainability. Ongoing research, development, and collaboration play a crucial role in realizing the transformative benefits of this innovative approach, ultimately enhancing agricultural productivity and environmental stewardship.

Techno-economic needs assessment for a sustainable novel solar panel production system

Techno-economic needs assessment for an automated solar panel production system is presented. Solar energy, a rapidly growing renewable energy source, has the potential to meet a significant portion of the world's energy needs. Solar panels, the key component of solar energy systems, there are produced in a major industry. Automated solar panel production systems offer the potential to increase production efficiency and reduce costs. The study outlines key factors necessary for successful establishment and operation of an automated solar panel production factory, including market analysis, financial projections, operational strategies, and sustainability initiatives. The market analysis through examination global trends in the solar energy market, identifying growth trends, potential competitors, and target customer segments. The study used a five-step methodology to assess needs, identifying major requirement such as the application of high-speed and precision manufacturing equipment, investment in reliable and efficient manufacturing processes, and development of cost-effective manufacturing methods. The study’s results highlight several key economic benefits of automated solar panel production, including increased production efficiency, reduced labor costs, and improved product quality. The findings are valuable to various stakeholders, including government officials, business leaders, and community members, informing decisions regarding solar industry development and new manufacturing facilities in specific regions. The paper recommends the use of eco-friendly manufacturing processes, utilization of recyclable materials, and adoption of energy-efficient automated technologies to minimize the carbon footprint of solar panel production.

The single picker routing problem with scattered storage: modeling and evaluation of routing and storage policies

Despite ongoing automation efforts, most warehouses are still manually operated using a person-to-parts collection strategy. This process of collecting items of customer orders from different storage locations accounts for the majority of the operating costs of the warehouse. Hence, optimizing picker routes is an important instrument to reduce labor costs. We examine the scattered-storage variant of the single picker routing problem in a one-block parallel-aisle warehouse. With scattered storage, an article can be stored at several storage locations within the warehouse, whereas with classic storage, each article has a unique storage location. We use our recently published network-flow model with covering constraints that is based on an extension of the state space of the dynamic-programming formulation by Ratliff and Rosenthal. With modifications in the state graph, this model serves for both exact and all established heuristic routing methods for picker routing. The latter include traversal, return, largest gap, midpoint, and composite. We show that these routing policies can also be implemented through adaptations in the state space. Extensive computational studies highlight a comparison of the different routing and storage policies (in particular class-based storage policies) in the scattered storage context. Analyses demonstrate which combinations of policies are advantageous for the given warehouse layout. For class-based storage policies, we emphasize how the scattering of articles of different classes should be performed: scattering of C-articles is advantageous with reductions of up to 25%. In contrast, when articles are uniformly distributed, A-articles should be scattered.

Intelligent fertigation improves tomato yield and quality and water and nutrient use efficiency in solar greenhouse production

Intelligent fertigation is a sustainable solution for optimising water and fertiliser input, thus minimising environmental pollution in vegetable cultivation facilities and reducing labour costs in agricultural practices. It is important to optimise irrigation scheduling parameters to specific crops to ensure water and nutrient use efficiency. A field experiment was conducted in Shouguang, Shandong Province, to investigate the effects of irrigation scheduling with different treatments (farmer drip irrigation FI, intelligent irrigation II1, and intelligent irrigation II2) on tomato growth, irrigation water and nutrient use efficiency over two growth seasons. Intelligent irrigation II1 and II2 utilised FDR sensors to control the moisture range within 80–95% and 80–85% field capacity (FC) for automatic irrigation scheduling, respectively. Intelligent irrigation (II1 and II2 treatments) reduced irrigation rate by 24.3–63.8% in comparison with FI treatment, significantly increasing total dry matter accumulation, nutrient uptake, yield and fruit quality of tomato. II2 treatment further reduced the irrigation rate by 31.6–32.3% compared to II1 treatment, with no significant difference in tomato yield and quality. Root dry matter, root-shoot ratio, 0–2 mm diameter root length and root surface area, 0–1.5 mm diameter root tips, and >3.5 mm diameter root volume were significantly increased under intelligent irrigation treatments. Positive correlations between irrigation water productivity; nitrogen, phosphorus, and potassium use efficiency; and the indices of length, surface area, tips, and volume of roots were highly significant. Intelligent fertigation system (IFS) maintained soil moisture within a suitable range through high-frequency irrigation scheduling, promoted the growth of 0–2 mm diameter roots, which were responsible for absorbing, acquiring, and transporting water and nutrients in the soil, and reduced water loss and nutrient leakage. Taken together, the intelligent fertigation system presented herein is an effective fertigation strategy to improve irrigation water and nutrient use efficiency.

Assessment of Phytomass and Carbon Stock in the Ecosystems of the Central Forest Steppe of the East European Plain: Integrated Approach of Terrestrial Environmental Monitoring and Remote Sensing with Unmanned Aerial Vehicles.

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological-climatic and anthropogenic-technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations.

The Effects of Digitalization on the Sustainability of Small Farms

Digitalization of agriculture is one of the priorities of the EU’s rural development strategy “From Field to Table”, which promotes the creation of more added value and climate change mitigation in agriculture. A growing body of the literature argues that digitalization enables better information management, reduces production costs, and increases the potential for farm income growth, but only a few papers provide empirical studies on how digitalization improves the performance of small farms. To fill this gap in the literature, this paper presents a case study as empirical evidence of the impact of digital innovation on smallholder performance through a sustainable development lens. This paper reports research based on a pilot digitalization project implemented on a small organic farm. It examines the identification of logical links between the digitalization processes introduced and the impact of digitalization on the economic, social, and environmental performance of the small farm. The case study data were collected through semi-structured interviews and based on the results of a pilot project. The findings of this study provide evidence that the introduction of digital technologies has improved the economic performance of the farm, including a reduction in labor costs, improved customer relations, improvements in farmers’ investment planning, and process redesign. Based on this study, recommendations are made to policymakers on how to promote the uptake of digital technologies in smallholder farming.

Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.

Perceived likelihood of standard employment by client organizations and the career mobility of outsourced employees: a self-driving human capital increment model

Externalized employment, aimed at reducing labor costs, is becoming increasingly prevalent. However, the effective implementation of management practices to improve the career mobility of outsourced employees remains unknown. Drawing from social cognitive theory, this study constructs a self-driven career mobility model for outsourced employees. What’s more, this study proposes the concept of differentiation in perceived organizational supports (DPOS), that is ‘the relative degree of outsourced employee’s perception of support from client organizational exceeds that from supplier organization in triangular employment relationships’, and regards it as a key boundary condition for outsourced employees to interpret the cognition of employment opportunities. Data were collected from a three-wave survey of 271 outsourced employees from 52 interorganizational teams. Results showed that the perceived likelihood of standard employment (PLSE) by client organizations positively predicts outsourced employees’ turnover intention from supplier organization. Job-related human capital development (JRHCD) and occupational self-efficacy play a chain mediating role between PLSE and turnover intention. DPOS moderates the relationship between PLSE and JRHCD, and also moderates the mediating and chain mediating effect of JRHCD and occupational self-efficacy. We discuss the implications of findings for outsourcing management research and practice.

Coculture of abalone (Haliotis midae) and sea cucumber (Neostichopus grammatus) to reduce tank cleaning frequency in abalone farming

Abalone farming produces nutrient-rich sludge, and the frequent cleaning and removal of sludge from abalone tanks is labour and capital-intensive. This study aimed to assess the effect of culture methods and tank cleaning frequency on abalone growth, water quality, and sludge characteristics. The study was conducted for 16 weeks. Four treatments were used, namely, abalone cocultured with sea cucumber cleaned once (AS1) and twice weekly (AS2); abalone monoculture cleaned once (A1) and cleaned twice weekly (A2). Abalone (initial avg weight = 40.9 g) were maintained under commercial stocking conditions in flow through systems at a salinity of 35 ppt and fed a commercial formulated diet (Abfeed™) and fresh seaweed (Ulva lacinulata and Gracillaria gracilis). The stocking densities were 200 abalone m−2 and 50 sea cucumbers m−2. The sea cucumbers (initial average weight = 12.5 g) fed on the abalone faeces and leftover feed, which settled at the abalone tank's bottom (sludge). There was a significant interaction between the type of culture and the frequency of cleaning on abalone weight (F (1,476) = 12.41, p < 0.001). Abalone in the A2 treatment group showed higher growth (p = 0.006) than those of the A1 treatment group, while abalone in AS1 had significantly higher growth (p < 0.001) than those in the AS2 treatment group. Also, abalone in AS1 showed higher growth (p = 0.026) than abalone in the A1 treatment. However, abalone in AS1 had growth similar (p = 0.53) to those in A2. The survival rate of the sea cucumbers was high, however, the sea cucumbers experienced a reduction in weight regardless of the treatment. This study showed that growth optimisation in abalone farming can be achieved by cleaning tanks twice instead of once weekly. However, when abalone are cocultured with sea cucumber, tanks need only be cleaned once a week without compromising abalone growth or water quality. The coculture of abalone and sea cucumber results in an additional aquacultured product, a significant reduction in labour and other associated costs of cleaning, without an additional cost of production.

Blended controlled-release nitrogen fertilizer increases rice post-anthesis nitrogen accumulation, translocation and nitrogen-use efficiency.

One-time application of blended controlled-release nitrogen fertilizer (CRN) has the potential to solve the difficulty of top-dressing fertilizer in the cultivation of rice and reduce the cost of CRN fertilizer application. However, its effects on rice dry matter and nitrogen (N) accumulation and translocation, yield and N-use efficiency (NUE) remain uncertain. Field experiments were carried out at three sites (Mingguang, Chaohu, and Guichi) in the Yangtze River Delta in China to compare the effects of the conventional split applications of urea and the blended CRN and on post-anthesis dry matter and N accumulation and translocation, yield, and NUE in rice at 0, 60, 120, 180, and 240kg N ha-1. The results showed that at the equal N application rates, compared under the conventional N fertilizer treatment, the blended CRN application significantly increased the rice yield by an average of 0.9-6.9%, mainly due to increase the number of spikelets per panicle. The highest yield achieved with blended CRN treatment occurred at 200kg N ha-1, with an NUE of 45.9%. Moreover, in comparison to the conventional N fertilizer, the blended CRN treatment increased pre-anthesis N translocation (Pre-NT) by 1.0-19.8%, and the contribution of pre-NT to grain N by 0.2-8.7%, and NUE by 3.2-28.4%. Meanwhile, the blended CRN treatment reduced labor costs by 1800 Yuan ha-1 and enhanced the economic gains by 21.5-68.8%. Therefore, one-time application of blended CRN ≤ 200kg N ha-1 application rate improved rice yield, NUE, and economic profit compared to equivalent rates of split applied conventional N fertilizers.