Reduce Labor Costs Research Articles

Quality assessment of 3D‐printed concrete through quantitative visual inspection

Abstract3D concrete printing is an advanced technology offering numerous benefits, including design flexibility, higher construction efficiency, and reduced labor cost. However, the unique processing requirements necessitate careful consideration of the quality of 3D‐printed concrete. To this end, this study developed a real‐time visual inspection method to quantify geometrical and surface quality metrics, integrating rheological parameters for enhanced accuracy. Three binder‐to‐sand ratios (0.6, 0.8, 1) and sand fineness moduli (2.5, 2.9, 3.3) were tested. We began by comparing various image processing technologies, followed by analyzing the geometrical contour and surface defects from a top‐down perspective. Results showed that increasing the binder‐to‐sand ratio from 0.6 to 1 and reducing the sand fineness modulus from 3.3 to 2.5 reduced defect area ratios by up to 98% (from 4.3% to 0.041%). Subsequently, we integrated rheological parameters to refine the image analysis results and validated the approach through three case studies. The results demonstrate that the developed visual inspection technology effectively assesses the quality of 3D‐printed concrete, ensuring precision and reliability in evaluating 3D‐printed structures.

Investment value analysis of smart pension projects based on the background of aging in China

With the acceleration of the aging process of China's population, smart pension, as a new model of "Internet plus pension", is becoming an important breakthrough to solve the pension problem. Based on the background of China's aging society, this study combines a three-dimensional perspective of policy orientation, market dimension, and technological development. Through policy text analysis and typical case studies of the elderly care industry, it explores the investment value and risk boundary of smart elderly care projects. The results show that the scale of the smart elderly care industry is expected to reach 7.21 trillion yuan by 2025. The flourishing development of AI technology brings great convenience to smart elderly care and effectively reduces labor costs. There are investment opportunities in segmented fields, and intelligent monitoring, but people need to be alert to the challenges brought by technological iteration risks and market information differences. Research suggests that investors should focus on relevant core technological barriers and develop smart elderly care projects that are highly compatible with policies and have clear business models; actively seek cooperation with the government; and establish an investment mechanism that integrates government-guided funds with market-oriented capital. In addition, investors need to balance long-term investment and short-term returns, and design project operation models that balance social capital and public welfare attributes. Through multi-party collaboration, investment risks can be effectively reduced, and the sustainability and social benefits of smart elderly care projects can be improved.

Do not benchmark phenomic prediction against genomic prediction accuracy

Abstract Phenomic selection is a new paradigm in plant breeding that uses high‐throughput phenotyping technologies and machine learning models to predict traits of new individuals and make selections. This can allow breeders to evaluate more plants in higher throughput more accurately, resulting in faster rates of gain and reduced labor costs. However, phenomic prediction models are frequently benchmarked against genomic prediction models using cross‐validation to demonstrate their usefulness to breeders. We argue that this is inappropriate for two reasons: (1) differences in the accuracy statistic measured by cross‐validation do not reliably indicate differences in the accuracy parameter of the breeder's equation, which we show analytically and through reanalysis of data from three representative phenomic prediction studies and (2) phenomic and genomic selection tools influence other parameters of the breeder's equation, so comparing accuracy, even if done properly, is insufficient to advocate for one approach over the other. We conclude that phenomic selection may be useful, but comparisons of accuracy between genomic prediction and phenomic prediction models are not.

Smart Farming for Groundnut Yield Prediction Using IoT and Machine Learning

The integration of IoT in agriculture has revolutionized crop production by enhancing productivity, quality, and efficiency while reducing labor costs and boosting farmer income. IoT sensors provide precise data on environmental, soil, and plant factors, critical for predicting crop yields. In this study, groundnut crops were cultivated in 20 pots and monitored using IoT devices over 120 days, generating 480 data instances. Parameters like temperature, soil moisture, and nutrients (nitrogen, phosphorus, potassium) were measured to track growth metrics. Machine learning models Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest (RF) were developed using bagging techniques to predict yield and model growth rates based on NPK levels. Model performance was evaluated using R-squared, MAE, RMSE, and RMSLE metrics. For yield prediction, KNN outperformed RF and MLP with the highest R-squared (0.87), lowest MAE (2.1033), and lowest RMSE (2.0119), while MLP performed worst. Conversely, in modeling growth rates influenced by NPK, MLP excelled with the highest R-squared (0.52), lowest MAE (1.3499), MSE (2.7220), RMSE (1.6498), and an exceptionally low RMSLE (0.0024). Overall, KNN was the top performer for yield prediction, followed by RF and MLP, whereas MLP was superior for growth rate predictions. This highlights the potential of IoT and machine learning in advancing agricultural intelligence.

Enzymatic pretreatment optimizes rice husk decomposition by black soldier fly, Hermetia illucens L. (Diptera: Stratiomyidae) larvae

BackgroundImproper disposal of rice husk, a major agricultural byproduct, can cause severe environmental issues, including air pollution and habitat destruction. Black soldier fly larvae (BSFLs) offer a promising solution for organic waste management by converting waste into valuable biomass and nutrient-rich frass. However, the complex lignocellulosic structure of rice husk limits its digestibility by BSFL, necessitating effective pretreatment methods.MethodsThis study evaluated the effectiveness of enzymatic pretreatment with cellulase and pectinase, applied alone or in combination with heating and fermentation, on the bioconversion efficiency of BSFL to rice husk. Four pretreatment strategies were tested: direct enzyme addition, enzyme treatment followed by fermentation, heat treatment prior to enzyme addition, and heat treatment combined with enzyme addition and fermentation. A preliminary economic assessment was also conducted to estimate the costs and potential revenues associated with the most effective pretreatment process.ResultsThe combination of heat and cellulase treatment, followed by fermentation, significantly enhanced larval growth and achieved the highest bioconversion efficiency (34.8%). Direct cellulase addition also improved larval biomass production and substrate reduction. Some pretreatment combinations, particularly those involving pectinase, negatively affect larval survival. The economic assessment indicated that, under current conditions, the process is not yet profitable at a small scale, with a net loss of $0.24 per kilogram of rice husk processed. Sensitivity analysis revealed that improvements in BSFL market prices, reductions in enzyme or labor costs, or process scale-up could improve economic viability.ConclusionsOptimizing substrate conditions through enzymatic pretreatment, especially with heat and cellulase, can substantially improve BSFL-based rice husk decomposition. While the technical results are promising, further optimization and cost reduction are needed for commercial implementation. Future research should focus on mechanistic studies and larger-scale economic analyses to support the development of sustainable and economically feasible BSFL-based rice husk management systems.

Emerging Insights into the Durability of 3D-Printed Concrete: Recent Advances in Mix Design Parameters and Testing

Although 3D-printed concrete (3DPC) offers advantages such as faster construction, reduced labour costs, and minimized material waste, concerns remain about its long-term durability. This review examines these challenges by assessing how the unique layer-by-layer manufacturing process of 3DPC influences key material properties and overall durability. The formation of interfacial porosity and anisotropic microstructures can compromise structural integrity over time, increasing susceptibility to environmental degradation. Increased porosity at layer interfaces and the presence of shrinkage-induced cracking, including both plastic and autogenous shrinkage, contribute to reduced durability. Studies on freeze–thaw performance indicate that 3DPC can achieve durability comparable to cast concrete when proper mix designs and air-entraining agents are used. Chemical resistance, particularly under sulfuric acid exposure, remains a challenge, but improvements have been observed with the inclusion of supplementary cementitious materials such as silica fume. In addition, tests for chloride ingress and carbonation reveal that permeability and resistance are highly sensitive to printing parameters, material composition, and curing conditions. Carbonation resistance, in particular, appears to be lower in 3DPC than in traditional concrete. This review highlights the need for further research and emphasizes that optimizing mix designs and printing processes is critical to improving the long-term performance of 3D-printed concrete structures.

Does fiscal devaluation always work? A sectoral and business cycle perspective

Abstract The study verifies effects of fiscal devaluation on a broad set of sector and firm-level macroeconomic variables among European economies in years 1995–2019. Using local projection and panel data models it finds that fiscal devaluation impact on employment is stronger in services and small businesses, where production is most labour intensive. In turn, in manufacturing, where production is capital intensive, firms may substitute capital for labour in response to fiscal devaluation. Lastly, fiscal devaluation favours employment in recessions more than in times of economic expansions, as only in recessions it reduces labour costs in a more lasting way.

Labor Cost Stickiness in Response to Environmental Taxation Policy: A Dual Perspective Analysis in Chinese Firms

ABSTRACT This study finds that China’s 2018 Environmental Protection Tax Law (EPTL) increases corporate labor cost stickiness—that is, after EPTL implementation, firms become more reluctant to reduce labor costs when sales fall. The effect is particularly pronounced in heavy-polluting sectors and among firms receiving government subsidies. Moreover, this heightened stickiness is primarily driven by firms maintaining employment levels rather than adjusting average wages. These findings reflect firms’ strategic emphasis on employment stability to maintain political legitimacy. Unlike prior studies that highlight profitability concerns under environmental regulation, our findings underscore how legitimacy considerations shape labor cost decisions. Overall, the evidence of legitimacy-driven stickiness provides a previously overlooked perspective on how environmental policies influence employment strategies in emerging market firms.

Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian orchards

Durian is a valuable tropical fruit whose pollination heavily relies on bats and nocturnal insects. However, environmental degradation and pesticide usage have reduced insect populations, leading to inefficient natural pollination. This study proposes an AI-powered drone-based pollination method for complex durian orchards, integrating improved object detection and optimized path planning. Specifically, we enhance the YOLO-v8 algorithm using the GhostNet lightweight network to reduce computational complexity while boosting detection precision. For path planning, we develop an Enhanced TSP (EN-TSP) algorithm based on a branch and bound strategy with least-cost optimization. Experimental results demonstrate that the proposed method improves detection accuracy by 5.85% and path efficiency by 26.89% compared to baseline algorithms. The novel use of GhostNet with YOLO-v8 enables superior detection of durian flowers under low-light and occluded conditions, while EN-TSP ensures globally optimal drone routes, reducing travel distance and improving operational reliability. This integrated solution advances smart agriculture by enabling scalable, efficient, and precise pollination, reducing labor costs and increasing durian yield and quality.

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

Investigating the genetic background of novel behavioral indicators of robotic milking efficiency in North American Holstein cattle.

Investigating the genetic background of novel behavioral indicators of robotic milking efficiency in North American Holstein cattle.

Development and evaluation of an automated classification and counting system for rice planthoppers captured on survey boards

Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surveys to examine their abundance and composition in paddy fields involve human visual inspection, which requires considerable time and effort by expert entomologists. We previously developed a deep learning-based object detection system which can detect rice planthopper individuals from scanned images of survey boards, categorize, and count planthopper individuals by 18 categories, based on species, developmental stages, adult sexes, and adult wing-forms, with a mean average precision (mAP) of 79%. In this study, we modified the system by reconsidering the categories of planthopper individuals to be counted and by additional supervised training. The modified system can count rice planthopper individuals captured on survey boards across 17 categories with a mAP of 91%. We also showed that by using the system developed here, classification and counting of rice planthopper individuals can be completed in approximately six minutes per survey board, which can take more than an hour for human experts. Thus, this high-performance system can greatly save time and reduce labor costs for monitoring the occurrence, reproduction, and population growth of the rice planthoppers in paddy fields, which could increase the efficiency of their management.

Review Paper on: SPM Milling Machine

In the modern manufacturing industry, automation plays a crucial role in increasing production efficiency, reducing labour costs, and ensuring consistent product quality. There is high requirement for such automated solutions in industries, that can deal with manufacturing of high-volume and small precise keys. Square keys and round keys made up of materials like Brass or Alloy Steel, are used in various mechanical applications. Which needs to be produced with good precision and high scale. These keys generally involve with special operation requirement like Saddle milling and cutting which cannot be performed on the single conventional machine without manual intervention. The traditional methods of saddle milling and cutting keys can be labour-intensive and prone to errors, leading to inefficiencies and reduced throughput. The outcome of this review is to Identify market gap and share general idea or understanding of SPM machine which can produce kind of key which has saddle milling and cutting operation requirement in mass production layout.

Design and Development of an Automated Seed Metering Bot for Precision Agriculture

Precision agriculture aims to increase agricultural productivity while reducing resource use and environmental impact. A critical component of this approach is seed metering—the accurate placement of seeds to optimize crop growth and uniformity. Traditional seed metering methods are often labor-intensive and imprecise, leading to inconsistent seed spacing, resource waste, and potential yield reduction. The Automatic Seed Metering Bot addresses these challenges by providing a fully autonomous solution that integrates advanced sensors, GPS, and real-time data processing to ensure precise seed placement across variable field conditions. This bot is designed to adapt to different terrains, soil types, and planting requirements, optimizing seed distribution to enhance crop uniformity and yield potential. Capable of self-adjustment based on real-time field conditions, it requires minimal human intervention, reducing labor costs and allowing for more efficient large- scale farming operations. By integrating with other precision farming technologies, such as aerial drones and soil analysis systems, the bot enables data-driven decision-making and improves planting accuracy. The projected design helps to improve productivity benefits, the Automatic Seed Metering Bot supports sustainable farming practices by minimizing seed waste and optimizing resource use, thus contributing to both economic and environmental goals in agriculture

Solar Powered Autonomous Multipurpose Agricultural Robot Using Bluetooth/Android App

This review paper presents the design and implementation of a solar-powered autonomous multipurpose agricultural robot that can be controlled via Bluetooth or an Android app. The primary objective is to create an efficient and environmentally friendly robotic solution for modern agriculture, capable of performing a variety of tasks such as planting, irrigation, and crop monitoring. The robot is powered by a solar energy system, ensuring sustainability and reducing dependency on external power sources. It is equipped with various sensors for navigation, obstacle detection, and environmental monitoring. The Bluetooth and Android app integration allows for remote control and monitoring, providing users with a seamless and user-friendly interface. Autonomous operation is achieved through advanced algorithms for task scheduling, navigation, and system control. This project highlights the potential of combining renewable energy sources with smart agricultural technologies to improve farming practices, reduce labor costs, and contribute to sustainable agriculture. The development of a solar-powered autonomous multipurpose agricultural robot, designed to perform various farming tasks efficiently and sustainably. The robot is powered by a solar energy system, reducing the reliance on conventional energy sources and contributing to environmentally friendly farming practices. The core functionality of the robot includes tasks such as soil preparation, irrigation, seeding, crop monitoring, and weed detection, all of which are essential for precision agriculture. The system utilizes an advanced autonomous control algorithm to navigate the agricultural field, using a combination of sensors for obstacle detection, terrain analysis, and positioning. The robot’s mobility is enabled through a robust drivetrain, which ensures stability across various field conditions. A Bluetooth module integrated with an Android app provides a user-friendly interface for real-time control and monitoring, allowing the user to track the robot's performance, adjust its operations, and receive sensor data remotely

What drives employer engagement with apprenticeships in Scotland?

This study explored employer engagement with apprenticeships in Scotland, using Bredgaard's typology to categorize employers into four types: committed, sceptical, passive, and dismissive. Committed employers, driven by workforce development or corporate social responsibility, actively engaged with apprenticeships but their primary purpose often determined how these were managed and experienced. Sceptical employers, motivated by industry norms, financial incentives, or short-term recruitment needs, exhibited inconsistent engagement and were prone to practices like rebadging or substitution. Passive employers often lacked understanding of apprenticeship frameworks and feared value misalignment, leading to non-engagement. Dismissive employers prioritized flexible labor practices and cost reduction, viewing apprenticeships as incompatible with broader operational strategy. By advancing theoretical and practical insights, this work contributes to efforts to enhance the quantity and quality of apprenticeship opportunities in Scotland. It underscores the need for tailored strategies to address the diverse factors impacting employer engagement.

Применение семантических технологий искусственного интеллекта в управлении архитектурно-строительным проектированием

The article explores the possibilities of using artificial intelligence (AI) and semantic technologies to automate the processing of construction data and the integration of cost estimates (GESN) into building information modeling (BIM) processes. The authors propose an innovative approach that combines natural language processing (NLP), ontological modeling and machine learning to structure informal technical specifications and automatically generate cost indicators for building structures. The study consists of two interrelated stages, including the analysis of relevant publications in the field under consideration and the confirmation of the hypothesis put forward based on the analysis of the collected samples. As a result of the research, it was found that the use of NLP tools such as spaCy and BERT allows for the efficient extraction of key parameters from text technical specifications, ensuring the transition from a free form of description to a structured presentation of information. The creation of ontologies based on regulatory databases (including GESN) in RDF format opens up the possibility of semantic integration of design solutions with the regulatory framework, increasing the accuracy and consistency of estimate calculations. The use of "clear rules" (rule-based) and machine learning methods for comparing building structures with the corresponding estimate standards contributes to the formation of a more adaptive and intelligent pricing system. Thus, the conducted study reflects the potential of AI to reduce labor costs at the early stages of design and improve the accuracy of estimate calculations. Prospects for the work are associated with the development of digital twins and end-to-end automation of the life cycle of construction projects.

Uniform Design and Regression Analysis Methods for Optimal Operational Parameter Design of High-pressure Waterjet Machine

Currently, removing scales from harvested fish is a time- and labor-intensive process. To reduce labor costs and improve productivity, this study uses a uniform design and regression analysis methods to design optimal operational parameters of a high-pressure waterjet machine for removing tilapia scales. A uniform layout of U10*(103) was used to arrange three operational parameters for experiments. The removal rate is the quality index defined by the fish scale removal area divided by full surface area covered by scales. Experimental results show a removal rate of 85.39%.

Design And Development Of Arecanut Harvesting System

- Arecanut harvesting is a labor-intensive process that poses significant challenges in terms of efficiency and productivity. This paper presents an innovative arecanut harvesting machine designed to streamline the harvesting process, reduce labor costs, and enhance yield quality. The machine incorporates advanced technology, Field tests demonstrate a marked increase in harvesting speed and a reduction in damage to the nuts compared to traditional methods. The machine features a mechanical pulley system integrated with a precision-engineered cutting mechanism mounted on a lightweight, telescopic pole. The harvesting operation is fully ground-based, significantly eliminating the risks associated with tree climbing. In field trials, the machine demonstrated an average reduction in harvesting time by 40–50% compared to manual methods, while nut damage was reduced to less than 3%. In addition, the machine requires minimal operator training, is portable, low-maintenance, and affordable, making it particularly suitable for small and medium-scale farmers. This study concludes that the adoption of mechanization in arecanut harvesting is essential for meeting the growing global demand and improving the livelihoods of farmers in arecanut-producing regions. Key Words: optics, photonics, light, lasers, templates, journals

Tree fruit harvesting: Recent developments and future challenges for robotic harvesting

Tree fruit harvesting is a vital process in agriculture, involving the collection of ripe fruits from trees. This review examines manual, mechanical and automated harvesting methods, focusing on their benefits, challenges and potential advancements. Manual methods, such as hand-picking and using ladders, are highly labour-intensive and time-consuming. To address these challenges, mechanical systems like trunk shakers, canopy shakers, limb shakers and catch-and-frame methods have been introduced. These systems use vibrations and capturing mechanisms to improve efficiency and reduce labour costs. However, mechanical methods are not commonly used for fresh fruit harvesting due to the risk of damaging soft fruits and trees. To solve this issue, precise shake-and-catch systems with controlled vibration frequency and amplitude are being developed, achieving fruit removal rates of up to 93.3 %. Harvesting speeds vary, with manual pickers managing 0.5 th-1, trunk shakers 10 th-1 and canopy shakers 25 th-1. Automated harvesting offers even greater efficiency by using robots equipped with advanced technologies, such as deep learning, image processing and specialized grippers, to detect and pick fruits. These systems can complete harvesting in just 4 seconds per fruit. This review highlights the strengths and weaknesses of current methods and explores strategies to enhance fruit harvesting technologies.