Reduce Labor Costs Research Articles

Application of digital entertainment experience based on intelligent interactive system in personalized fitness training system

The application of digital media in fitness environments can improve the entertainment of the fitness process, allowing users to watch entertainment videos while engaging in fitness activities. At the same time, we can add relevant game elements to the fitness process, such as playing tennis through gesture movements, thereby increasing user interest in fitness participation. Therefore, this article has developed a personalized fitness training system using optical sensors and intelligent algorithms, and conducted testing experiments on blood oxygen saturation under two sports states: running and cycling. This article analyzes the intelligent optimization algorithm for optical signals. This algorithm has strong adaptability and intelligence level, and can directly learn and adjust the model based on the characteristics of the data. The automation performance of the intelligent algorithm can effectively reduce labor costs and improve efficiency. Finally, this article provides a basic analysis and testing of the personalized fitness training system. Through the obtained data, personalized guidance is provided to users, helping people make fitness more scientific and intelligent, and improving fitness efficiency.

Innovative structure to mount demand feeders to concrete raceways at a production fish hatchery

AbstractObjectiveAs a type of automatic feeder, demand feeders reduce labor costs and can improve fish‐rearing efficiencies. However, mounting demand feeders can be problematic because of the variety of nonstandardized fish‐rearing units. This article describes an innovative, simple, sturdy, and durable demand feeder mount for rectangular, concrete raceways.MethodsThe feeder mount was made from aluminum and sits flat on a raceway wall or walkway. A horizontal base plate, secured to the wall by vertical plates, holds a vertical riser of aluminum tubing. Attached to this tubing is a rod that secures the demand feeder.ResultThis feeder mount design has proven durable over several years of actual use. Over this time, it has needed no maintenance other than occasional cleaning with water to remove spilled fish food. The feeder can be easily and quickly removed from the mount by simply pulling a pin and sliding the feeder off of the tubing. The mount is constructed to dramatically reduce tripping hazards and occupational safety and health risks.ConclusionThis simple, inexpensive (~US$50 to construct), and no‐maintenance mount is a viable solution to the typically difficult problem of mounting demand feeders to concrete raceways used for trout production.

Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

Modelling the economy-wide effects of unilateral CO2 pricing under different revenue recycling schemes in Austria – Searching for a triple dividend

In this paper we identify policy implications for implementing carbon pricing in Austria, taking into account model structure uncertainty. Methodologically, we compare the results of two macroeconomic models, DYNK (a New Keynesian model) and WEGDYN-AT (a neoclassical general equilibrium model), to evaluate the effects of carbon pricing under different revenue recycling options. Specifically, we examine the model results with respect to their findings regarding a potential triple dividend, that we define as a simultaneous materialization of i) a reduction in CO2 emissions, ii) positive effects on GDP and employment, and iii) positive and distributionally progressive effects in household consumption possibilities. Accounting for model structure uncertainty improves the robustness of our results; at least within the macroeconomic frameworks applied here. Our results confirm the tradeoff between equity and efficiency identified in previous analyses. In the New Keynesian model, but not in the general equilibrium model, we find evidence for a triple dividend for a combination of the recycling options of non-wage labor cost reductions and lump-sum per-capita transfers (“climate bonus payments”).

Determination of the impact of innovative technologies in the system of strategic management of corporate social responsibility

The object of research is the process of using innovative technologies in the system of strategic management of social responsibility of enterprises. The main problem solved during the research was the need for a more effective integration of the principles of social responsibility in the business strategy, which will help enterprises to adapt to the global challenges and demands of modern society. The study of new technological approaches in this area allowed to identify the most effective tools for increasing transparency, responsibility and sustainable development of companies. As a result of the conducted research, the content of the concept «innovative technologies in the system of strategic management of social responsibility of enterprises» was clarified, which is proposed to be understood as a modern approach to the integration of technological innovations into strategic management, aimed at improving the social indicators of the enterprise. These technologies are found to include a wide range of tools such as Big Data, Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and others that help businesses effectively implement and monitor social programs and initiatives. It is substantiated that the use of innovative digital technologies in the system of strategic management of social responsibility of enterprises opens wide opportunities and generates significant advantages. Namely, increasing transparency and openness; optimization of resources and costs; increasing internal efficiency; strengthening of reputation and brand; automation of enterprise processes; innovativeness and competitive advantages; stimulating innovation. A model for determining the relationship between the level of profitability of enterprises by type: large, medium, and small enterprises and by the number of employees and their labor costs is proposed, which allows to establish the required number of employees for each type of enterprise. This enables the enterprise to function effectively, reducing labor costs, but not reducing the positive impact on supporting the social sector of the population, creating jobs and remaining a socially responsible enterprise. The possibility of using the latest digital technologies in the social responsibility management of enterprises is summarized.

Design and Performance Evaluation of a Cashew Apple (Anacardium occidentale L.) Slicing Machine

One challenge in cashew apple (CA) production is the absence of processing equipment, leading to manual operations. Cashew apple processors often resort to manual slicing, resulting in uneven thickness and increased manpower requirements. This study aimed to address these issues by developing a slicing machine that ensures uniform thickness and reduces the need for manual labor during the slicing process. The machine underwent evaluation at three different slicing speeds (28, 42, and 60 strokes per minute [spm]). The most efficient speed was found to be 60 spm, with a slicing capacity of 249.26 kg/h and a slicing efficiency of 86.66%. The corresponding rates for damaged sliced CAs and juice extraction during slicing were 4.93 and 22.33%, respectively. In comparison, manual slicing demonstrated a capacity of 32.21 kg/h and a slicing efficiency of 29.99%. The machine’s capacity was almost eight times higher than that of manual slicing. The optimal slicing speed for CAs was determined to be 60 spm, which significantly reduced losses in terms of thickness, extraction, and physical damage of CA during the slicing process. Using the machine not only minimized the time required for slicing but also reduced labor costs, making it a more efficient and economically viable option for CA processing.

Reactive shepherding along a dynamic path

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill employed in the herding of animals, crowd control, and evacuation operations. Integrating shepherding capabilities into robots holds promise to perform such tasks with increased efficiency and reduced labor costs. To date, robotic shepherds have only been designed to steer a herd towards a predetermined goal location without constraints on the trajectory. However, the tasks of a sheepdog encompass not only steering the herd but also (i) maintaining the herd within a designated area and (ii) averting dangers, obstacles, or undesirable terrain such as newly sown land. We present a decentralized control algorithm for multi-robot shepherding designed to guide a group of animals along a specified path delineated by two boundaries. The algorithm incorporates the additional objective of preserving the group within these boundaries. Simulation results reveal that, especially in sections of the path with sharp turns and a small distance between the boundaries, the group exhibits a tendency to deviate beyond the prescribed margin. Additionally, our findings emphasize the algorithm’s sensitivity to the ratio of robot-group sizes and the magnitude of the group’s velocity.

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.

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.