Reduce Labor Costs Research Articles

‘Single-Seed-SpeedBulks:’ a protocol that combines ‘speed breeding’ with a cost-efficient modified single-seed descent method for rapid-generation-advancement in oat (Avena sativa L.)

BackgroundIn plant breeding, one of the most cost-effective and efficient ways to increase genetic gain is to reduce the breeding cycle time. In general, modern breeding methods for self-pollinated crops should strive to develop fixed lines at the lowest possible cost and in the minimum possible amount of time. Previous studies on spring oat (Avena sativa L.) showed that combining high plant density with limited soil fertility and moisture levels in a growth media like sand effectively decreases the time and cost of generating fixed single-seed descent lines. More recently, ‘speed breeding,’ or the exposure to prolonged photoperiod regimes of 22 h, has been shown to decrease flowering time in oat significantly. The goal of this study was to combine ‘speed breeding’ with high-density planting in a limited soil fertility media to reduce further the costs and time required to develop oat single-seed-descent lines.ResultsWe grew oat plants at low density in potting-mix (control), high density in potting-mix (HD-soil), and high density in sand (HD-sand) under 16 and 22 h of day length. We observed that oat plants grown in HD-sand and exposed to 22 h day length reduced their flowering time by around 20 and 5 days on average compared to those grown in control conditions at 16 and 22 h, respectively. We also observed that 85% of plants grown at high density in sand produced a single seed when grown in bulk conditions. In contrast, only 40% of plants grown at high density in potting-mix produced a single seed.ConclusionsOur novel protocol showed that oat plants grown in high-density bulks, using sand media and 22-hour day length, reduced their flowering time by 20 days compared to control conditions and produced plants with single seeds, following closely single-seed descent assumptions while significantly reducing labor costs and greenhouse space. This methodology can be deployed in oat breeding programs to help them accelerate their rate of genetic grain for multiple traits.

Vaccine microarray patch self-administration: An innovative approach to improve pandemic and routine vaccination rates

The high-density microprojection array patch (HD-MAP) is a novel vaccine delivery system with potential for self-administered vaccination. HD-MAPs provide an alternative to needle and syringe (N&S) vaccination. Additional advantages could include reduced cold-chain requirements, reduced vaccine dose, reduced vaccine wastage, an alternative for needle phobic patients and elimination of needlestick injuries. The drivers and potential benefits of vaccination by self-administering HD-MAPs are high patient acceptance and preference, higher vaccination rates, speed of roll-out, cost-savings, and reduced sharps and environmental waste. The HD-MAP presents a unique approach in pandemic preparedness and routine vaccination of adults. It could alleviate strain on the healthcare workforce and allows vaccine administration by minimally-trained workers, guardian or subjects themselves. Self-vaccination using HD-MAPs could occur in vaccination hubs with supervision, at home after purchasing at the pharmacy, or direct distribution to in-home settings. As a result, it has the potential to increase vaccine coverage and expand the reach of vaccines, while also reducing labor costs associated with vaccination. Key challenges remain around shifting the paradigm from medical professionals administrating vaccines using N&S to a future of self-administration using HD-MAPs. Greater awareness of HD-MAP technology and improving our understanding of the implementation processes required for adopting this technology, are critical factors underpinning HD-MAP uptake by the public.

PERSPECTIVES AND FEATURES OF BRANCH UTILIZATION TECHNOLOGIES IN INTENSIVE GARDENS AND PARKS

In most countries of Europe and North America, wood chips obtained in the process of cutting brushwood and branches are widely used in intensive gardens and parks as a mulching agent. Technologies that involve the disposal of branches have a wide list of technological operations. One of the most common involves manual pruning with the help of simple technical tools such as a chainsaw and cordless saws. The cut wood is transported and piled into piles, which are burned when there is enough accumulation. This technology is primarily environmentally harmful, as it pollutes the environment. Therefore, the property of using chopped branches as mulch between rows in gardens and park alleys is quite an important factor. The goal is to study the application of the technology of cutting branches into chips with a high-tech unit, which will make it possible to reduce labor costs by approximately 1.5 times, fuel costs by 1.4 times, eliminate environmental pollution, replenish the soil with organic and mineral additives and use additional areas of land. which are now set aside for wood burning sites. Technologies that involve the disposal of branches after trimming the crown of trees have the most diverse directions of use of the pruning product and a wide list of technological operations. Each of them is interesting in its own way, has advantages and disadvantages, and therefore requires more detailed coverage. One of the oldest and most primitive technologies involves manually loading cut wood onto vehicles, taking the branches to storage sites, where they are burned. This technology is poorly mechanized and requires extensive use of unproductive manual labor. The coefficient of utilization of the carrying capacity of vehicles is very low, as it is impossible to place the collected wood tightly under load. Currently, it is used in small farms.

Delayed pay and employee turnover: The buffering role of pay‐for‐performance

AbstractIn response to a financial crisis, many organizations adjust their pay systems to reduce labor costs. In this research, we focus on the use of delayed pay (the postponement of employees' contractual compensation) and examine its effect on employee voluntary turnover outcomes. In a field study (Study 1) with data collected from the executive managers of 129 Chinese hotels experiencing a financial crisis caused by coronavirus disease 2019 (COVID‐19), we find a positive relationship between delayed pay and the voluntary turnover rate at the organizational level. In addition, this relationship becomes weaker when delayed pay consists of a larger proportion of pay‐for‐performance (PFP). We then conduct an experiment (Study 2) to extend our theoretical framework to the individual level, establish causality, and examine the underlying mechanism. The results show that the perceived negative instrumentality of stay (i.e., expected economic losses associated with staying in an organization) explains why individuals intend to leave organizations adopting delayed base pay but not delayed PFP. We further replicate these findings in a critical incident technique study (Study 3). We also discuss the theoretical and practical implications of these findings.

Improving shape formation under conditions of plane tensile stress

Thin-walled axisymmetric truncated parts made of sheet billets are actively used in rocket and aerospace engineering. Improvement to their shape formation, based on directed material thickness change will ensure the production of parts with minimum thickness variation. This will also enable aviation and space industry enterprises to attain leading positions, as well as reduce labor costs. This work studies the possibility of obtaining thin-walled axisymmetric parts of truncated tapered shape using one of the methods of sheet metal stamping under flat tensile stress conditions (flanging). The mechanism was identified and the analysis of the stress-strain state of the billet during deformation was carried out. This takes into account the minimizing of the difference between the specified and technologically possible thicknesses. A mathematical model was developed to consider the shaping method based on the process of flanging. Theoretical studies were based on the principles of the plastic deformation theory of sheet materials. This was achieved by the following factors: approximate differential equations of force equilibrium; equations of constraint; plasticity conditions; and fundamental constitutive relations under given initial and boundary conditions. The process of flanging was simulated using the LS-DYNA software package with the following initial data of a conical billet made of 12Kh18N10T steel: cone angle 16.4°, thickness Sbillet = 0.3 mm. The aim was to eliminate errors in designing a tool for future implementation of the method on a manufactured die tooling, as well as to confirm the theoretical conclusions on the selection of technological parameters and achieve minimal thickness variation. The steps of computer modeling are presented, indicating the main process parameters such as material model, mechanical characteristics of the workpiece material, type of elements, kinematic loads, conditions of contact interaction of elements with each other, etc.

Calculation of the Timing of the Replacement of Relay Equipment for Railway Automation and Telemechanic

In 2021, at the Obukhovo station of the Oktyabrskaya railway. testing of the technology for counting the number of operations of relay equipment began. The results of testing the technology at the Obukhovo station showed that relay equipment with a high intensity of operation generates faster the standard number of operations specified in the technical specifications for the equipment. Relay equipment with a high intensity of operation needs to be replaced earlier than the scheduled period, and equipment with a low intensity of operation needs to be replaced less often than the scheduled period. The routine method of replacing relay equipment does not take into account the intensity of its work. The article proposes a scientific approach that allows you to manage the resource of the relay equipment of railway automation and telemechanic at the stage of its operation based on the calculation of the timing of its replacement. To calculate the timing of the replacement of relay equipment, objective data on the values of their actual number of operations are used. The article proposes the calculation of the timing of the replacement of relay equipment based on its characteristics, service life under technical conditions, scheduled terms in the instructions, and the intensity of its work. The calculation of the cost of failure and maintenance of relay equipment over the entire service life is given to determine the optimal replacement period for relay equipment, for which the replacement period occurs earlier than the scheduled period. The calculation of the timing of the replacement of relay equipment makes it possible to switch from the scheduled maintenance method to maintenance based on the actual state and ensure the transition to address planning. This will reduce labor costs for maintenance and the risk of relay equipment failures.

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Artificial intelligence promotes the development of mineral/rock automatic recognition technology to reduce labor costs and reliance on personal experience. With instrument digitization, image recognition has played an increasingly important role. This review focuses on the image sensing system applied to mineral/rock identification and redivides this system into a sensing module, an imaging module, and an information processing module. The sensing and imaging mechanisms are summarized via technologies of sensing, imaging, and display. The information processing is classified into single‐modal or multimodal recognition. This review analyzes the limitations of single‐modal recognition and discusses the advantages and significance of multimodal recognition. The challenges of current mineral/rock identification are concluded, such as public database, multimodal learning, and portable sensing system. The interdisciplinary sensing technologies are discussed, and application feasibility is explored. This review aims to provide a reference for the research community in automatic mineral/rock identification and sensing system optimization.

Cultivation Strategies of Kenaf (Hibiscus cannabinus L.) as a Future Approach in Malaysian Agriculture Industry

In recent years, kenaf has gained significant global attention as a more cost-effective, adaptable, and manageable alternative to other fibre crops. India and China, with nearly 70% of the global kenaf production, have emerged as the leading producers of kenaf plants. While kenaf was traditionally valued for its paper production, it has evolved into a multipurpose crop with diverse industrial applications over the past two decades. Conventional soil-based cultivation methods for kenaf require up to six months for plant maturity. However, in vitro propagation techniques offer a promising alternative that enables faster growth and reduced labour costs. In vitro propagation can be achieved using solid and liquid media, with limited research available on the pure liquid culture method for kenaf. This review aims to introduce and compare the production of kenaf using solid and liquid media, with a specific focus on the emerging country of Malaysia, which seeks to harness the potential of kenaf cultivation for the 15th Sustainable Development Goal, “life on land”, and its contribution to the economy.

Design and Build of a Multifunctional Portable Fertilizer

Pangestu Farmer Group is a farming group in Pandanrejo Village, Batu Regency, that has high-quality sweet corn as a strategic asset with often fluctuating yields. Consequently, the market demand cannot be met. The main problem of fluctuations in corn yields is due to improper fertilization methods. Applying liquid fertilizer during the rainy season can result in nutrients being washed away by rainwater, but when granular fertilization is used, this results in increased costs and labour. A solution is needed to solve this problem so that the fertilization process is correct, and the timing of fertilization and labour costs can be reduced. Therefore, a portable fertilizing technology based on spring valves is needed. The goal is to develop and manufacture a valve spring-based portable fertilizer that can increase the effectiveness and efficiency of the fertilization process. The comparison of type 1 and type 2 fertilizers lies in the raw material used. Commercially available hand sprayer for comparison in design. Ergonomic analyses are also considered in the design of the fertilizer, both in terms of user safety and comfort. The method in this study is to design tools with power and ergonomic calculations, manufacture tools at Sahabat Teknik Workshop, and conduct tests at Brawijaya University's Agricultural Power and Machinery Laboratory. The test results for portable fertilizer types 1 and 2 show great advantages, namely, increase in working capacity 20 times to 1.2 ha/hour, reduce the number of workers from 7 people/ha to 5 people/ha, reduce operating time, reduce labour costs and increase farmers' income by up to 57, 72%/year.

Coupling multifactor dominated the biochemical response and the alterations of intestinal microflora of earthworm Pheretima guillelmi due to typical herbicides.

The excessive application of herbicides on farmlands can substantially reduce labor costs and increase crop yields, but can also have undesirable effects on terrestrial ecosystems. To evaluate the ecological toxicity of herbicides, metolachlor and fomesafen, two typical herbicides that are extensively used worldwide were chosen as target pollutants, and the endogeic earthworm Pheretima guillelmi, which is widely distributed in China, was selected as the test organism. A laboratory-scale microcosmic experiment was set, and energy resources, enzymes, and the composition and connections of intestinal microorganisms in earthworms were determined. Both herbicides depleted the energy resources of the earthworms, especially glycogen contents; increased the levels of antioxidant enzymes; and inhibited acetylcholinesterase. Moreover, the richness and diversity of the intestinal bacterial community of the earthworms were suppressed. Additionally, the bacterial composition at the genus level changed greatly and the connections between dominant bacteria increased dramatically. Most interactions among the bacterial genera belonging to the same and different phyla showed mutualism and competition, respectively. Importantly, metolachlor with higher toxicity had a transitory effect on these indicators in earthworms, whereas fomesafen, with lower toxicity but stronger bioaccumulation potential, exerted a sustaining impact on earthworms. Collectively, these results indicate that the toxic effects of herbicides on terrestrial organisms should be comprehensively considered in combination with biological toxicity, persistence, bioaccumulation potential, and other factors.

Asymmetric new Keynesian Phillips curve for Mexico, 2005Q1–2022Q4

PurposeThe labor market responds in a differentiated manner during recessions and expansions, and it is of vital importance to know the magnitude asymmetries. The purpose of this paper is to evaluate the effects of the disinflationary monetary policy (2005Q1–2022Q4) through the sacrifice rate measured in terms of unemployment and rate of critical labor conditions (RCLC) with nonlinear auto regressive distributed lag (NLARDL; Shin et al., 2014), which allows to efficiently estimate asymmetric effects in short and long terms in the presence of variables of different integration orders.Design/methodology/approachThe authors estimate an asymmetric accelerationist Phillips curve, augmented with labor precariousness for Mexico (2005Q1–2022Q4) following the NLARDL approach (Shin et al., 2014).FindingsThe authors prove that the increase in the unemployment gap has greater disinflationary effects than the RCLC in both the short and the long term; the expansionary phases of the business cycle, which reduce UGap, do not have inflationary effects either in the short or in the long run, but improvements in the labor market do, when RCLC is reduced; raising RCLC appears to have been the companies’ main survival strategy since 2015; and these asymmetries can generate a low unemployment trap with high and growing precariousness, with huge dynamic costs for well-being, economic growth, inequality and poverty.Social implicationsAs labor precariousness grows, the implications are several both in the short and long run. In the short run, the most notorious example of the effects on workers has to do with unstable and insecure situations, that disrupt all their life planning options, and health issues. Bohle et al. (2004) found in the Organization for Economic Cooperation and Development countries that casual employees had less desirable and predictable working hours, greater work–life conflict and more associated health complaints than people with permanent jobs.Originality/valueThe approach includes the labor precariousness variable, which describes a new phenomenon in the labor market. Nowadays, workers are facing a new threat since firms are employing a new labor cost reduction strategy in which they do not lay off workers but rather paying them less, working them more hours, or reducing benefits. The asymmetries between the effects of precarity and unemployment can generate a poverty trap in the long run. This problem is, once again, of great relevance in the context of global high inflation.

Feasibility analysis of automation for commercial construction project

The use of automation technology in the construction sector may cover a wide variety of operations, from the early design stage through on-site building construction, building maintenance, building control, and finally building disassembly or demolition. The extent of automation adoption varies greatly from one construction phase to the next. These days, as opposed to using automation for on-site operations, the use of automation in the construction sector has several important advantages, including increased worker safety at work, improved quality and cost-effectiveness above that of trained labor, and increased productivity and job efficiency. Research gaps need to be filled to address automated manufacturing process innovation. This study has been carried out most promising areas where automation is required with the help of scientific analysis through a questionnaire survey. This study identifies critical areas for improvement in automation in the work process in commercial construction projects. The respondents comprised site engineers, project managers, and supervisors of construction firms. The relative importance index is used in this study. Using the results of this study, categorized the primary variables for improvement like workers' safety alert procedures, reducing labor cost and construction delay, core test team under multiple projects, impropriate construction method, and better quality of life of the structure.

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

With the development of the e-commerce industry, using UAVs (unmanned aerial vehicles) to deliver goods has become more popular in transportation systems. This delivery method can reduce labor costs and improve the distribution efficiency, and UAVs can reach places that are difficult for humans to reach. Because some goods are perishable, the quality of the delivery will have an impact on the customer satisfaction. At the same time, the delivery time should also meet the needs of customers as much as possible. Therefore, this paper takes the distribution distance and customer satisfaction as the objective functions, establishes a bi-objective dynamic programming model, and proposes an improved SPEA2 (strength Pareto evolutionary algorithm 2). The improved algorithm introduces the local search strategy, on the basis of the original algorithm. It conducts a local search for the better non-dominated solutions obtained in each iteration. The new dominated solutions and non-dominated solutions are determined, and the crossover operator is improved, so that the local search ability is improved, on the basis of ensuring its global search ability. The numerical experiment results show that the improved algorithm achieves an excellent performance in three aspects: the Pareto front, generation distance, and spacing, and would have a high application value in UAV cargo delivery and other MOPs (multi-objective optimization problems). The average spacing value of the improved algorithm is more than 20% smaller than SPEA2 + SDE (strength Pareto evolution algorithm 2–shift-based density estimation), which is the second-best algorithm. In the comparison of the average generation distance value, this number reaches 30%.

Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

AbstractThis paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods.

Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials

Wood material is the foundation of wood structure architecture, and its production technology and equipment technology decide the development and upgrading of modern wood structure architecture. Aiming at the problems of low automation degree, low material utilization rate, low production efficiency and high labor costs in the process of traditional wood processing, we explore the integration and innovation of the traditional wood processing industry and modern industrial Internet information technology on the basis of studying the properties of Betula (Betula costata) solid wood materials, wood comprehensive utilization rate, wood structure component development and processing technology requirements, and form an intelligent, automatic and industrial production mode for building materials. Through technology and methods such as mechanical design, automation technology, machine vision, deep learning, optimization algorithm, electronic design automation, computer aided manufacturing, etc., the key technologies of intelligent automatic optimization of wood materials were studied, and intelligent automatic production lines of Betula species identification, log optimization sawing, solid timber longitudinal multiblade sawing, sawn timber quality detection and solid timber optimizing cross-cuts are built. Based on the machine vision method, features are extracted; a tree species, defect classification and recognition model database is established; an image processing algorithm with high recognition accuracy, as well as fast processing speed and high robustness are studied; non-destructive testing and classification methods of machine vision are optimized; key problems of online rapid classification, detection and optimization of sawing are solved and production quality and processing efficiency are improved. Finally, the timber defect detection accuracy and Betula timber yield are analyzed, and the comprehensive utilization value of optimized sawing timber is compared with the comprehensive utilization value of manually marking sawing timber. The processing cost and efficiency of Betula sawing timber with an intelligent automatic production line are calculated. The test results show that the average detection accuracy of timber defect type, size and location is 89.69%, 89.69%, 92.25% and 82.29%, respectively, and the detection stability is high. By adopting intelligent automatic detection, classification and optimization sawing production line of wood, the comprehensive utilization value of optimized sawing timber is 14.13% higher than that of manual marking sawing timber, and 16,089.29 m3 more building materials can be processed annually. In the process of intelligent automatic wood processing, the intelligent detection system is used to detect defects, improve production performance and production efficiency and reduce labor costs. Compared with the traditional wood processing process, the method studied in this paper is improved to optimize the production line processing performance and processing technology. The research and development of an intelligent automatic production system for solid wood can promote the application and development of an automatic industrial production mode for sawn timber for the wood structure construction industry, deepen the integration of artificial intelligence technology, Internet technology and the whole wood processing industry and lead the upgrading of building materials for wood structures to an intelligent manufacturing production mode.

A high-performance speech BioHashing retrieval algorithm based on audio segmentation

As one of the research hotspots in the field of speech recognition, content-based speech retrieval algorithms can detect speech information with the same content features, which improves computer intelligence while reducing labor costs, and thus have been widely used. Although most of the current speech content retrieval algorithms can guarantee excellent retrieval performance for small-scale speech retrieval work, the performance of the above algorithms is greatly reduced under the constraints of large speech data storage space and high content redundancy. In order to solve the above problems, a high-performance speech BioHashing retrieval algorithm based on audio segmentation is proposed in this paper. The algorithm is divided into an offline pre-processing phase and an online retrieval phase, The offline pre-processing stage converts the speech data into BioHashing sequences with speech content characteristics. In this process, first of all, the Power-Normalized Cepstral Coefficients (PNCC) features of the speech data are extracted and biometric templates with single mapping keys are constructed according to the PNCC features, obtaining BioHashing sequences. Then, slice the original speeches into short-time audio segments according to the proposed audio segmentation algorithm, and the hash reconstruction operation is performed on the BioHashing sequences to obtain the reconstructed Hashing sequences for online retrieval. The online search phase responds to the users' query requests, just find the hash index that matches the query hash sequence from the BioHashing index table, and will the standardized editing distance (SED) to the closest 1 value corresponding to the hash index as the retrieval result back to the user. The experimental results show that the reconstructed hash sequences obtained after removing the silent redundant segments have better robustness and discrimination. Moreover, the algorithm achieves 100% retrieval accuracy for the original speech clips, and the average retrieval time is only 0.0157 s, which shows that the algorithm has good retrieval performance and can meet the needs of speech retrieval in various environments.

Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning

Image recognition of plant growth states provides technical support for crop monitoring; this reduces labor costs and promotes efficient planting. However, difficulties in data collection, the required high levels of algorithm efficiency, and the lack of computing power resources create challenges to the development of intelligent agriculture. As a result, a deep transfer learning algorithm is proposed in this paper. The main motivation for this study was the lack of a dataset of plant growth stages. The key idea was to collect radish growth stage images in an experimental field using standardized equipment and to generate more images using DCGAN. By improving the deep transfer learning model, radish growth stages can be identified much more accurately. In this study, five different deep migration models were selected, namely, Inception-v3, MobileNet, Xception, VGG-16, and VGG-19. Our experiment demonstrated that Inception-v3 was the most suitable model for the recognition of plant growth states. Based on Inception-v3, we propose three improved models. The test accuracies for the radish and Oxford Flower datasets were 99.5% and 99.3%, respectively. Additionally, the accuracy of the pest and disease dataset also achieved excellent performance, with an accuracy of 94.7%, 2.4% higher than previously. These results demonstrate the wide applicability of our model and the rationality of constructing a radish growth stage dataset.

Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks

Intelligent robotics, as a frontier field of widespread attention, is increasingly applied to realize cyber-physical-social system (CPSS). Taking Agriculture 4.0 as one use case, using various agricultural robot applications can reduce labor costs and improve operation efficiency, but limited by the onboard resources, these mobile robots need to make full use of network and computation resources provided by infrastructures in the smart farm to enhance their capabilities.On the one hand, the heterogeneity of networks brings more options for access selection; however, on the other hand, the mobility of robots and the Quality of Service (QoS) requirements of different agricultural applications pose great challenges to the current offloading work. Therefore in this paper, we introduce a stochastic game framework to address these challenges faced by smart agriculture, and propose a multi-agent task offloading and network selection (MATONS) scheme, which utilizes reinforcement learning to offload tasks from robots to central cloud or edge servers. The robots cooperatively optimize the QoS perceived and the energy usage by adopting a joint Nash equilibrium strategy, which requires the decision of offloading along with the optimal transmission technology and corresponding access nodes, i.e., WiFi or cellular network. Simulation results verify that MATONS has great convergence and stability under different scales of robots in the farm, and can achieve superior performance than mainstream algorithms in various scenarios.

ACCOUNTING FOR CHOCOLATE IN THE DOMINICAN REPUBLIC: A CASE STUDY IN VOLUNTEER WORKING TOURISM

This paper focuses on the Chocal Cooperative in the Dominican Republic. The cooperative is of interest because paying visitors to the facility act as “volunteer working tourists.” Specifically, tourists participate in activities that add value to the process of transforming cocoa beans into the products ultimately sold to customers. This raises the question of how to properly account for the visitor fees: are they revenue or a reduction of direct labor cost? The case study includes a discussion of how US Generally Accepted Accounting Principles and International Financial Reporting Standards (IFRS) might apply.