Reduce Labor Costs Research Articles

Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis

Replacing traditional manual sweeping with unmanned sweepers in closed parks can not only greatly reduce labor costs, but also improve sweeping efficiency. Efficient path planning is the key technology for unmanned sweepers to complete the sweeping task. Existing unmanned sweepers are often based on fixed path sweeping or completely traversing the sweeping mode, this mode does not have the environmental adaptability, in the actual sweeping is often high energy cost, and sweeping is not complete. In this paper, an environment-adaptive sweeping path planning method is proposed to improve the sweeping intelligence and environmental adaptability of unmanned sweepers, reduce the energy consumption of sweeping and improve the efficiency of sweeping. Specifically, in this paper, we first use YOLOv5 to complete the accurate identification of individual garbage and obstacles in the road, and then work with LIDAR and Gaussian Mixture Model(GMM) to remove redundant targets. We also propose a Permutation Entropy(PE) value-based discrimination method to complete the target distribution posture analysis of each complex garbage pile. Finally, the traditional path planning problem is transformed into a combinatorial optimization problem of garbage areas, and a sweeping path accurate method based on Simulated Annealing(SA) algorithm is proposed. Through comprehensive theoretical analysis and simulation study, the optimality and effectiveness of the proposed method are proved by comparing A star and Coverage Path Planning(CPP) algorithms in a variety of experimental scenarios.

Preference for young workers in mid-career recruiting using online ads for sales jobs: Evidence from Japan

This study uses an original dataset of online mid-career job ads for full-time sales jobs collected from July 2018 to December 2019 to examine the use of explicit and implied age limits on job applicants and the characteristics of firms that set them. Although Japanese law prohibits age discrimination in employment, several exemptions, such as hiring young workers without prior work experience on regular contracts, are allowed. Firms can set an age limit, require job-related experience, or search broadly; however, they can also express their age preference in other ways. In the sample, 24 % of ads included explicit age limits generally capped at 35 years, 26 % set experience requirements, and nearly all contained some form of implied age preference. Consistent with theoretical predictions, the results show that firms with higher capital, those with fewer employees, older firms and those located in urban centers tended to set requirements on applicants. Further, domestic firms, firms with fewer employees, in urban centers and firms using probation periods for new hires were more likely to set age limits. Moreover, firms setting either requirement did not seem to be sensitive to local labor market conditions. Firms searching broadly responded to population age-related increased wage expectations while reducing labor costs by increasing the number of working hours covered by a baseline wage.

Deep insights into biodegradability mechanism and growth cycle adaptability of polylactic acid/hyperbranched cellulose nanocrystal composite mulch

The widespread use of petroleum-based plastic mulch in agriculture has accelerated white and microplastic pollution while posing a severe agroecological challenge due to its difficulty in decomposing in the natural environment. However, endowing mulch film with degradability and growth cycle adaptation remains elusive due to the inherent non-degradability of petroleum-based plastics severely hindering its applications. This work reports polylactic acids hyperbranched composite mulch (PCP) and measured biodegradation behavior under burial soil, seawater, and ultraviolet (UV) aging to understand the biodegradation kinetics and to increase their sustainability in the agriculture field. Due to high interfacial interactions between polymer and nanofiler, the resultant PCP mulch significantly enhances crystallization ability, hydrophilicity, and mechanical properties. PCP mulch can be scalable-manufactured to exhibit modulated degradation performance under varying degradation conditions and periods while concurrently enhancing crop growth (wheat). Thus, such mulch with excellent performance can reduce labor costs and the environmental impact of waste mulch disposal to replace traditional mulch for sustainable agricultural production.

PSXII-13 The Design of Equipment That Combines a High-Pressure Jet and a Translational Scraper to Improve the Manure Treatment Efficiency of Pig Barn Cleaning Machine

Abstract Livestock farming generates a significant amount of manure, which must be effectively managed to prevent environmental pollution and ensure the health and well-being of animals. The cleaning machine is a commonly used tool for removing manure from pig barns: however, it often leaves behind residual waste that is difficult to remove. To address this issue, a new equipment design that combines a high-pressure jet and a translational scraper has been proposed. This design aims to improve the efficiency of manure treatment by weakening the adhesion and removing stubborn waste particles, making it easier for the cleaning machine to collect the manure. The high-pressure jet provides a powerful stream of water that can dislodge even the most stubborn waste particles. For high-pressure washing, a DC 10V/10A standard water pump and a 70L water tank are included. The translational scraper, on the other hand, can move left and right across the surface of the slatted floor, effectively scraping away any remaining waste. For this work, an actuator was installed to move the blade part and the hook type work part. These two components work together to improve the efficiency of manure treatment and reduce the amount of waste left behind. By improving the efficiency of manure treatment, this new equipment design can help to reduce environmental pollution and improve the overall health and well-being of pigs. In addition, it can also reduce labor costs and improve the profitability of pig farming operations. In conclusion, the design of equipment that combines a high-pressure jet and a translational scraper has the potential to significantly improve the manure treatment efficiency of a pig barn cleaning machine. This new design can help to address the challenges associated with manure management in pig farming and promote a more sustainable and efficient approach to livestock farming. This achievement was made by the support of the Ministry of Agriculture, Food and Rural Affairs, R&D project(421044043HD030)

An adaptive crack inspection method for building surface based on BIM, UAV and edge computing

Unmanned aerial vehicle (UAV) and building information modeling (BIM) have been applied to inspect building surfaces to improve efficiency and reduce labor costs. However, most of the current research focus on the inspection mode of “first collect and then identify” with limited intelligence. Therefore, this study proposes a “global-local” adaptive inspection method for building surfaces by integrating BIM, UAV (with multifunction camera), and edge computing, enabling real-time synchronization of crack data collection and analysis. First, the building surface area to be inspected is extracted from BIM to generate global inspection points using the selected camera's field of view (FOV) and considering coordinates transformation between BIM and reality. Then, A star algorithm integrating with genetic algorithm (GA) is used to optimize the global flight path based on generated inspection points. Finally, the coordinates of the inspection points based on BIM are converted into real-world coordinates, and the flight parameters such as the flight yaw angle (FYA) and the gimbal yaw angle (GYA) are calculated to generate the inspection mission. During global inspection, a local adaptive inspection is proposed for each FOV through real-time local crack identification and flight replanning using edge computing and zoom camera. In addition, multiple battery supply points are automatically added considering the endurance of the UAV. A real building has been used to validate the proposed “global-local” adaptive inspection method. The experiment results show that the inspection time has been significantly reduced compared with the traditional method, and the crack identification of building surfaces can be implemented with high efficiency, precision, quality, and low cost.

A design of a smart farm system for cattle monitoring

<span>The integration of technology in agriculture has led to the adoption of smart farming systems, which are becoming increasingly popular for optimizing resources, reducing labor costs, and improving efficiency. This article presents a design of a smart cattle monitoring farm system, which focuses on monitoring individual animal behavior and health, improving resource management, and optimizing overall farm efficiency. The proposed system integrates various internet of things (IoT) sensors, communication technologies, and cloud computing to provide a real-time monitoring solution <span>for cattle farms. The system uses machine learning (ML) algorithms to analyze data on cattle behavior, health, and performance, which can be accessed through web and mobile applications by farmers to proactively monitor their herd. The adoption of the system promises good results for intelligently transforming traditional cattle farming by reducing manual monitoring time and achieving a good accuracy in early detection of diseases, automated tracking of estrus cycles, and location mapping of cattle across the farm fields.</span></span>

Uphill struggle: collective bargaining for the self-employed in Poland

Poland, which has one of the highest shares of self-employment and civil law contracts in total employment in the European Union, provides an instructive example of a labour law reform that extended the personal scope of collective bargaining. Since 1 January 2019, the right to collective bargaining has been extended to all workers in paid employment. The first effects of the reform have however been disappointing. Collective agreements concluded since the new law came into force hardly ever cover non-employees. These results are rooted both in social partners’ attitudes and strategies and in the existing legal framework. Employers keen on reducing labour costs are reluctant to enter into collective bargaining with self-employed workers, while trade unions have not adopted a comprehensive strategy for representing non-employees. At the same time, the law scarcely fosters and encourages social dialogue; in some areas it can even be an obstacle to developing collective bargaining for workers.

Optimising multiplex immunofluorescence staining for characterising the tumour immune micro-environment.

Exploring the tumour microenvironment provides insight into the unique interaction between the host and tumour. Ultimately, its study improves understanding of how an individual mounts and achieves an anti-tumour immune response. In the context of colorectal cancer, immune biomarkers within the tumour microenvironment outperform traditional histopathological staging in predicting disease recurrence. Multiplex immunofluorescence enables simultaneous assessment of multiple markers to provide a highly accurate classification of immune cells and their spatial characterisation relative to tumour tissue. Further, automated slide staining provides staining consistency and reduces labour costs. Image acquisition using a non-spectral scanner allows more researchers to utilise multiplexed immunofluorescence for translational research. Herein we describe the optimisation process of conducting automated staining using a five-colour, tyramide signal amplification-based multiplex immunofluorescence panel. Using antibodies against CD3, CD8, CD103 and cytokeratin, the panel characterises T cell populations within human colorectal adenocarcinoma tissue. We provide an overview of primary antibody titration and the development of tyramide signal amplification immunofluorescence monoplex assays. We detail the processes of antibody stripping and the role of exogenous horseradish peroxidase inhibition to facilitate multiplexing. An account of determining the staining sequence and fluorophore assignment is provided. We describe image acquisition using a standard fluorescence microscope slide scanner and the management of spectral crosstalk using this system. Finally, we briefly document the digital image analysis required to characterise cells and determine their spatial distribution within the colorectal tumour microenvironment.

A small boat for fish feeding

This study focuses on development of a solar-powered Arduino Mega-driven robotic boat designed for efficient fish feeding in aquaculture. By integrating IoT technology and a mobile app, the project aims to reduce labor costs and improve feeding practices while addressing challenges associated with manual feeding, which previously failed to adequately cover the entire pond area. The research specifically centers on Nile tilapia production. Experimental results show that the robot emulates manual feeding with an average response time of 1.82 s for feeding initiation and 2.17 s for movement initiation. It efficiently distributes feed across a 4000 m² area in 9 min, outperforming the manual method, which takes 32 min. Fish fed by robotic boat exhibit an 18.71 % weight gain compared to those fed by hand. The robotic boat farm consumes a total of 11.480 kg of feed to produce 7.6 kg, while the hand-fed farm required 16.185 kg of feed to produce 6.5 kg. This results in feed conversion ratios of 1.51 and 2.49, respectively. The study finds an overall cost reduction of 46.13 % due to decreased labor hours. We strongly recommend adopting this robotic boat for optimizing tilapia feeding in earthen ponds.

A STUDY ON IMPACT OF EMPLOYEE ATTRITION TOWARDS ORGANISATIONAL PERFORMANCE AT ADVANCE CABLE TECHNOLOGIES PRIVATE LIMITED IN DODDABALLAPUR

This study explores the relationship between employee attrition and organizational performance, investigating its causes, consequences, and potential management strategies. Employee attrition, the departure of employees from an organization, impacts operational efficiency and success. The research analyzes various types of attrition, such as voluntary, involuntary, and demographic specific, and their effects on an organization's performance. The study highlights the benefits of managing attrition, including reduced labor costs, improved organizational culture, and enhanced opportunities for remaining employees. Literature review summaries present research on attrition prediction models using machine learning, factors affecting attrition, and methods to identify high-risk employees. The research focuses on Advance Cable Technologies Private Limited, using a descriptive research methodology, primary and secondary data sources, and statistical analyses such as Chi-square, ANOVA, and t-tests to interpret the findings. The study recommends practices to improve employee retention and organizational performance based on the research outcomes. Keywords: Employee Attrition, Employee Performance, Organisation performance, Management Strategies, involuntary attrition.

Realization of rule-based automated design for HVAC duct layout

In the traditional process of pipeline design, a designer typically creates a general layout of pipeline based on their experience, and then calculates whether the hydraulic calculation of pipeline meets the requirements. This method of design is both time-consuming and labor-intensive. Furthermore, the results are often suboptimal and prone to errors, which creates a lot of waste of human and material resources. The objective of this paper is to propose a computer-aided method for completely automating the heating, ventilation and air conditioning (HVAC) duct system design process. This method incorporates all the necessary design processes, such as duct selection and hydraulic calculation, and introduces a rule-based divide-and-conquer method to the pipe routing design (PRD). The research assesses the practical effect of this piping design algorithm in a BIM environment using varied building scenarios. The findings show that the proposed method is capable of meeting the design requirements of actual projects. The automatic piping design technique presented in this paper can significantly reduce labor costs associated with HVAC design while eliminating calculation errors and compressing the design timeline for most of the complex projects to within 5 min.

Surgical Instrument Recognition Based on Improved YOLOv5

Recognition of surgical instruments is a key part of the post-operative check and inspection of surgical instrument packaging. However, manual inventorying is prone to counting errors. The achievement of automated surgical instrument identification holds the potential to significantly mitigate the occurrence of medical accidents and reduce labor costs. In this paper, an improved You Only Look Once version 5 (YOLOv5) algorithm is proposed for the recognition of surgical instruments. Firstly, the squeeze-and-excitation (SE) attention module is added to the backbone to improve the feature extraction. Secondly, the loss function of YOLOv5 is improved with more global parameters to increase the convergence rate of the loss curve. Finally, an efficient convolution algorithm is added to the C3 module in the head to reduce computational complexity and memory usage. The experimental results show that our algorithm outperforms the original YOLOv5 with improvements observed across various metrics: mean average precision 50–95 (mAP50-95) achieved 88.7%, which improved by 1.8%, and computational requirements reduced by 39%. This study, with a simple but effective method, is expected to be a guide for automatically detecting, classifying, and sorting surgical instruments.

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

Технология создания сводных классификаторов цифровых топографических карт

The authors describe the technology of creating a summary style of displaying digital topographic maps (DTM) for a certain scale. The technology enables subjects of geodesic and cartographic activities that create and process spatial data in the form of DTMs to reduce labor costs at forming coatings from several DTMs made with classifiers of different release years (at different times), as well as their correct visualization. The method consists of five steps, representing both automated processes and those requiring manual work of specialists in the field of digital cartography and geoinformatics. During the development of the technology, several software utilities were established to minimize the time required to make a summary style of the DTM display with newly specified source data. One of the stages is creating a transcoding table of the DTM object composition. It can be used by GIS and information technology developers

Design of reinforced concrete structures with loop joints of rebar in accordance with State Standard R 70447-2022

Introduction. Currently, loop overlapping joints of reinforcing bars are used in reinforced concrete structures. These types of working rebar connections are most widely used in NPP structures for the construction of slabs and walls made of monolithic reinforced concrete. Loop joints in these structures are used to connect adjacent three-dimensional reinforced blocks that are assembled in advance and installed in the design position before concreting. This type of connection leads to an increase in the manufacturability of construction and installation works, as it significantly reduces labor costs on the construction site when installing reinforcement frames.The relevance of the development of the standard is due to the fact that the current regulatory framework did not contain detailed instructions on the design requirements for such connections. The absence of these provisions led to significant difficulties in the design of such structures, which led to excessive material and time costs.The aim of the work is to improve the system of urban planning activities in terms of clarifying and supplementing existing regulatory documents on the design of reinforced concrete structures.Materials and methods. The development of the standard was carried out taking into account modern requirements, as well as an analysis of the results of recent scientific research. Additionally, the practical domestic experience of using loop joints in the construction of nuclear power facilities was analyzed.Results. The result of the work is the State Standard R 70447-2022 standard, the provisions of which include new calculation methods, as well as clarifications of existing calculation and design methods, and updated requirements. The draft standard passed public discussions, as well as the necessary expertise and was put into effect.Conclusions. The standard State Standard R 70447-2022 "Reinforced concrete structures with loop joints of reinforcement for nuclear power facilities" has been developed, the provisions of which will allow developing reliable structural solutions for such structures, including achieving a certain economic effect. At the same time, certain areas for improving the provisions of this standard are noted.

Real-Time Obstacle Detection for Unmanned Surface Vehicle Maneuver

The rapid advancement and increasing demand for Unmanned Surface Vehicle (USV) technology have drawn considerable attention in various sectors, including commercial, research, and military, particularly in marine and shallow water applications. USVs have the potential to revolutionize monitoring systems in remote areas while reducing labor costs. One critical requirement for USVs is their ability to autonomously integrate Guidance, Navigation, and Control (GNC) technology, enabling self-reliant operation without constant human oversight. However, current study for USV shown the use of traditional method using color detection which is inadequate to detect object with unstable lighting condition. This study addresses the challenge of enabling Autonomous Surface Vehicles (ASVs) to operate with minimal human intervention by enhancing their object detection and classification capabilities. In dynamic environments, such as water surfaces, accurate and rapid object recognition is essential. To achieve this, we focus on the implementation of deep learning algorithms, including the YOLO algorithm, to empower USVs with informed navigation decision-making capabilities. Our research contributes to the field of robotics by designing an affordable USV prototype capable of independent operation characterized by precise object detection and classification. By bridging the gap between advanced visualization techniques and autonomous USV technology, we envision practical applications in remote monitoring and marine operations with object detection. This paper presents the initial phase of our research, emphasizing significance of deep learning algorithms for enhancing USV navigation and decision-making in dynamic environmental conditions, resulting in mAP of 99.51%, IoU of 87.80%, error value of the YOLOv4-tiny image processing algorithm is 0.1542.

Suitability of Modular Technology for House Construction in Sri Lanka: A Survey and a Case Study

Prefabricated volumetric modular building construction is an emerging technology in many countries used to develop the construction industry through its value-added benefits. The adoption of these novel technologies in the Sri Lankan construction industry has been relatively slow compared with other developing countries, delaying the development of the construction industry and the attainment of the added benefits of these technologies. Therefore, this study aims to identify the suitability of modular construction for the Sri Lankan construction industry via a survey and case study. A questionnaire survey was developed and distributed to assess and identify the benefits of implementing the concepts and constraints in Sri Lanka. Then, the construction cost of a proposed single-story house delivered through conventional and modular practices was compared. The survey results showed that introducing a modular concept to the industry is suitable and would benefit the Sri Lankan construction industry. Further, survey participants believed that the economic benefits brought in by modular construction are significantly more important than environmental and social benefits when selecting modular construction. The case study on a single-story affordable modular house showed a 32% reduction in total construction costs and a 36% reduction in labour costs compared to conventional house construction. Further, costs for total modular construction and labour were 32% and 36% less than those of conventional construction costs and labour costs. Moreover, a 16% reduction in embodied energy was observed when compared with conventional construction. Therefore, the modular concept could be used to construct affordable houses and will be cost-effective with the correct choice of material.

One-time fertilization of controlled-release urea with compound fertilizer and rapeseed cake maintains rice grain yield and improves nitrogen use efficiency under reduced nitrogen conditions.

Nitrogen (N) rate reduction and simplified fertilization can mitigate environmental impacts and reduce the involvement of manual labor in rice (Oryza sativa L.) production. Controlled-release urea (CRU) has been recommended as an effective alternative technique to conventional urea fertilization, and it can improve rice yield and N use efficiency (NUE) and reduce labor costs. However, the information on the effects of one-time fertilization with CRU on maintaining yield and improving NUE under reduced chemical N conditions is limited. In this study, controlled-release bulk blending fertilizer (CRF), consisting of CRU with release periods of 40 and 100 days, mixed with compound fertilizer, was applied as basal fertilizer. Increased ~20% plant density (ID) and rapeseed cake fertilizer (RC, increase 20% organic N) were combined with CRF, respectively. The N treatments with 20% chemical N reduction were as follows: reduced N fertilizer (RNF), CRF, CRF+ID, and CRF+RC. In addition, a conventional split fertilizer application with 300kg ha-1 N was applied as the control (CK). Rice yield and its components, dry matter accumulation, N uptake, and NUE were investigated to evaluate whether one-time N fertilization realized stable yield and high NUE under reduced 20% chemical N conditions. Compared with CK, the CRF+RC treatment exhibited a comparable grain yield, while the other reduced N treatments (RNF, CRF, and CRF+ID) had a lower grain yield. Moreover, CRF+ID exhibited a higher rice grain yield than RNF or CRF under the same N level. Irrespective of exogenous organic N, CRF+RC exhibited significantly higher NUE than CK. The CRF+ID treatment showed a significantly higher N partial factor productivity (PFN) than CK but comparable N agronomic efficiency (NAE) and N recovery efficiency (NRE). Therefore, a one-time fertilizer application of CRF+RC maintained grain yield and improved the NUE while reducing the N rate and fertilization times, demonstrating its potential application in rice production.

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.

Economic efficiency of the use of means of mechanization of intensive horticulture in Belarus

This article considers the issue of using means of mechanization of the most labor-intensive processes of intensive horticulture in Belarus. It has been established that the level of mechanization of the basic technology of cultivating an apple orchard is only 12,5 %, and requires financial resources – 6163 rubles/ha. A set of machines developed by RUE “SPC NAS of Belarus for agricultural mechanization” consists of an ASU-6 unit for harvesting fruits and pruning trees, a KUV-1,8 complex for swathing and chopping cut branches of fruit trees and a technological line LSP-4 for sorting and packing apples allows you to mechanize these processes. The use of the proposed train of machines in a promising garden cultivation technology allows for annual cost savings on the technological map in the amount of 1018,5 rubles/ha, a reduction in labor costs by 278,5 man-hours/ha, and the level of mechanization of garden cultivation – 50,1 %.