Reduce Labor Costs Research Articles

An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration

The agricultural sector plays a significant role in the economy of many countries, and irrigation is a critical component of successful agriculture. However, traditional irrigation methods can be time-consuming and labor-intensive, and often result in the over or under-watering of crops, which can negatively impact crop yields. To overcome these challenges, smart irrigation systems have been developed to assist farmers in managing their crops and increasing their yield. This research article presents an IoT-based smart irrigation system that uses four sensors - moisture content, temperature, humidity, and ultrasonic - to collect data from the irrigation area and transmit it to a central control system. The central control system uses the data to automatically turn the irrigation pump on and off, based on the moisture level of the soil. The system also includes a mobile application that allows farmers to monitor the system remotely and control the motor pump from their smartphones. The proposed system has several advantages, including reducing the hard work of farmers, providing essential strength to crops, and ensuring that plants receive the adequate amount of water at the required time. Additionally, the system's remote monitoring capabilities allow farmers to monitor the atmospheric temperature, humidity, and moisture content from anywhere at any time, and make adjustments as necessary. Overall, the findings of this research will help farmers to control their irrigation systems remotely, reduce labor costs, and increase crop yields. By improving the efficiency of irrigation and reducing water waste, this IoT-based smart irrigation system has the potential to significantly impact the agriculture sector and promote sustainable farming practices.

Analysis of implementation of industry 4.0 principles in furniture production

It is substantiated that the implementation of the principles of Industry 4.0 in the furniture industry will be able to significantly improve the competitiveness and efficiency of the enterprise, in particular, the replacement of workers with automated equipment in the manufacture of furniture will be able to increase the productivity and quality of production. It was found that the use of industrial robots and automated equipment can significantly reduce labor costs and increase production speed, and the introduction of sensors and their connection to the Internet can help monitor the condition of equipment, measure productivity and forecast maintenance needs. It was analyzed that the obtained data on production and interaction with clients will help in improving production processes and forecasting the demand for furniture products, and the use of virtual and augmented reality can significantly simplify the furniture design process and allow ideas to be implemented faster. It was found that the introduction of flexible production systems will allow to quickly switch to production between different types of furniture and respond to changes in demand. Also, the use of technological robots will be able to help reduce labor costs, especially in the face of growing demands for wages and labor shortages. It was found that the combination of physical and virtual systems will allow control and optimization of production processes in real time, and the use of machine learning algorithms for the analysis of large volumes of data can help identify and eliminate problems in furniture production, automate most production processes and reduce costs on the labor force. It is suggested to train personnel in advance for the introduction of new technologies - processing centers, technological robots, as this will help to ensure the successful and rapid implementation of the principles of Industry 4.0. Thus, robotics is a key element for the implementation of Industry 4.0 principles in furniture production, as it allows to increase the efficiency, quality and competitiveness of production.

Research on structural innovation of intelligent tree planting robot

This paper introduces an intelligent tree-planting robot, which belongs to the technical field of agricultural and forestry machinery and equipment, and the innovative features of its structure are hollow rotary drill and seedling storage and delivery device. This tree planting robot realizes the integration of autonomous movement, drilling, seedling delivery, mulching, compaction and watering functions, with a coherent workflow, which greatly improves the efficiency of tree planting. This robot will replace the traditional manual tree planting, greatly improve the survival rate of seedling planting and reduce labor cost.

Satellite systems and digital technologies in agriculture: state, problems, professional competencies

The goal is to study the possibilities of using digital technologies in agriculture, economic efficiency on the example of digital product "Cropio" – a satellite monitoring system. Methods – analysis, synthesis, comparison and analogy, selection of data and their detailing. The article shows the conditions for the implementation of information processes in agro-industrial complex, considers the current state, problems, substantiates the need for the use of smart technologies, clarifies the concept of "precision farming". The authors analyze the benefits of the Cropio program, which is used in agricultural sector of developed countries. Its functionality allows not only to control the sowing work, but also to determine soil moisture, its saturation with nutrients. It is noted that the program generates information on climatic and weather conditions, calculates the savings in material resources. The software components, their functions, installation process, acquisition mechanisms, and information are presented. Results – calculations of the expected economic effect from the use of this automation system were carried out, profitability and expediency of applying the methodology for evaluating investment were determined. Conclusions – technical and information solutions of the Cropio precision farming project are becoming more and more popular every year, confirming their promise, they can reduce labor costs, increase productivity of farmland, and therefore reduce cost of finished products. Observation of objects based on satellite navigation, computer technology and digital maps makes it possible to assess rationality and timeliness of financial resources, take into account a number of factors that affect market value and profitability of land, minimize various risks, optimize production activities through construction of new business processes.

ТЕХНОЛОГИЯ РАЗДАЧИ ЖИДКОГО КОРМА НА ОСНОВЕ ШТАНГОВОГО РАЗДАТЧИКА

Dairy farming is the main responsibility of the agro-industrial complex, which widely uses priority technologies for keeping and servicing animals. To feed the animals of modern farms, productive mechanized equipment for distributing feed is used. The most interesting is the organization of feeding young animals. An emergency method has been developed for the mechanized distribution of liquid feed for calves in loose housing conditions. The limited mobile feeder is designed for use on farms, including keeping in boxes. The device of the liquid feed dispenser consists of a bar and is a conveyor with buckets for feeding fixed on it. The bar moves on rollers with the help of an electric drive, moves and transports buckets to use group cages with calf boxes. The length of the feeding front is 60 meters at one consumption by the operator. The liquid feed dispenser uses a milk pump NMU-6 with a capacity of 6.5 m 3 /h. An outlet valve with an outlet diameter of 20 mm is installed, which ensures the supply of liquid feed to buckets that are stationary. The speed of movement of the distributor rod is 0.07-0.13 m/s. In the event of a rapid dispensing of liquid feed, the feeder can move with a stop-stop to fill the buckets. The developed design of a limited-mobile rod distributor allows the most efficient use of the mechanized distribution of liquid feed to calves in their loose housing. The technology of liquid feed distribution based on a rod spreader reduces labor costs by 50%, eliminates heavy and monotonous work.

UTOMATING THE PROCESS OF APPLYING POLYMER COATING ON PARTS OF THE LIGHT INDUSTRY

In today’s rapidly evolving manufacturing landscape, automation has become a driving force behind increased productivity, efficiency, and quality. One area where automation is making significant strides is the application of polymer coatings on parts of the light industry. By automating this process, manufacturers can achieve consistent and precise coating application, reduce labor costs, and improve overall production output. In this article, we explore the benefits and advancements of automating the polymer coating application process in the light industry. Automation in a small shop working with leather parts, several conditions and equipment considerations are essential was showed. Automation line with Automated Spray Systems significantly enhances efficiency and productivity in coating applications. These systems can operate continuously, 24/7, minimizing downtime and maximizing throughput. With faster application speeds and reduced cycle times, manufacturers can achieve higher production volumes within the same time frame. The automated process also eliminates the need for manual labor-intensive tasks, allowing workers to focus on more skilled and value-added activities. Overall, the integration of Robotic Arms and Automated Spray Systems leads to streamlined processes and improved manufacturing output. The automation of the process of applying polymer coatings on parts of the light industry offers numerous benefits ranging from increased precision and consistency to enhanced efficiency and productivity. By embracing automation, manufacturers can achieve superior coating quality, reduce labor costs, improve safety, and leverage Industry 4.0 technologies. As the manufacturing landscape continues to evolve, the adoption of automated systems in the light industry becomes increasingly crucial for staying competitive and meeting the demands of the modern market.

Cost-Benefit Analysis of Sesame Production under Improved and Traditional/ Farmer’s Practice/ in Kafta-humera, Tigray Region, Ethiopia

This study aimed to investigate the cost-benefit of sesame production per hectare under improved and (traditional sesame production) farmer's practice. The primary data were collected from the sesame farmers who cultivate both improved and traditional sesame production (plot with improved sesame agronomic technologies on one side and farmers practice on the other side) were selected randomly from the participants of households on the cluster-based large scale demonstration of sesame production during 2019 in kafta-humera district, Western Zone of Tigray, Ethiopia. The study found that farmers were able to generate an average gross income of Ethiopian Birr (ETB) 28997 per hectare (ha) from the improved sesame production, whereas, ETB 19215.21 per ha from their practice, respectively. The average cost of cultivation for improved sesame production was ETB 11857.89 ha and ETB 9259.63 per ha under (traditional sesame production) farmers practice. Therefore, the net profit of sesame production under improved and traditional sesame production was ETB 17139.69 and ETB 9955.58 respectively. This implied that farmers could earn an additional net income of 7184.11 per ha by practicing the recommended sesame technologies. This shows that although the net profit under the improved production system was higher than traditional/farmers practice, higher costs of production were also recorded in improved sesame production than traditional /farmers practice as farmers spend a lot of money on sesame cultivation especially weeding and harvesting. Hence, the government and private sectors should give more emphasis to introduce mechanized row planter, captivators, and combined harvester machines to reduce labor costs especially when the cost of labor is high. Moreover, the price of sesame seed was lower than the previous three-four years. Hence, agricultural economists should consider setting up a sesame price policy to help farmers to gain more profit from selling sesame.

The concept of lean manufacturing in the management of a medical clinic. How to beat the competition through lean manufacturing

The introduction of the concept of lean manufacturing in a medical clinic allows you to quickly achieve a strong competitive advantage by increasing the efficiency of resource management, including reducing labor costs, the efficiency of space and capacity, downtime to normalized KPIs, increase overall customer satisfaction and loyalty (NPS). The introduction of the lean manufacturing concept at the first stage does not require significant organizational efforts or investments. But it allows you to achieve results due to industry novelty: so far, SMEs and large clinics in Russia are not seriously engaged in studying and debugging life cycles, limiting themselves to individual sections, for example, promotion.

Category-Guided Visual Question Generation (Student Abstract)

Visual question generation aims to generate high-quality questions related to images. Generating questions based only on images can better reduce labor costs and thus be easily applied. However, their methods tend to generate similar general questions that fail to ask questions about the specific content of each image scene. In this paper, we propose a category-guided visual question generation model that can generate questions with multiple categories that focus on different objects in an image. Specifically, our model first selects the appropriate question category based on the objects in the image and the relationships among objects. Then, we generate corresponding questions based on the selected question categories. Experiments conducted on the TDIUC dataset show that our proposed model outperforms existing models in terms of diversity and quality.

Smart Table Food Ordering in Restaurant

This project report presents the development of a smart table food ordering system for restaurants. The system aims to enhance the customer experience by providing an intuitive interface for customers to view the menu, place orders, and make payments directly from their table using a touch screen interface. One potential solution is to incorporate technology to enhance customer service and streamline operations. One such innovation is the use of tables fixed with one LED display to enable customers to order directly from their tables. By incorporating this technology, restaurants can reduce their labor costs while improving customer experience. With this system, customers simply input their table number into the LED display, and they can browse the menu and place their order directly. The order is then sent directly to the kitchen or bar, reducing the need for servers to take orders manually. The use of this technology also allows for more efficient tracking of orders and can help reduce errors. When customers place their orders through the LED display, the information is sent directly to the kitchen, reducing the chance of miscommunication between the server and kitchen staff. Additionally, the system can track the time it takes to prepare each order, helping to identify any bottlenecks in the kitchen and improve overall efficiency. This technology can also improve the overall dining experience for customers. With the ability to place orders directly from their tables, customers can take their time browsing the menu and selecting items without feeling rushed. The system can also make recommendations based on previous orders, allowing customers to discover new dishes they may enjoy. In addition to the benefits for customers, this technology can also benefit hotels by reducing labor costs. With the ability to place orders directly from the table, the need for servers to take orders manually is reduced. This can free up staff to focus on other aspects of customer service, such as ensuring guests have a pleasant dining experience. Another benefit of this technology is its potential for integration with other hotel systems.

Intercomparison of Mechanical Transplanted Rice and Direct Seeded Rice in Climate Change Resilient for Improving Crop-Water Productivity and Soil Health in North West IGP: A Review

Climate change is a major issue facing humanity, and the most common method for growing rice is manual puddled transplanted rice (PTR). Direct-seeded rice (DSR) is becoming increasingly popular due to its reduced methane emissions and reduced labour costs. However, there are drawbacks to this transition, such as an increase in weeds, herbicide resistance, nitrous oxide emissions, nutritional disorders, and soil-borne diseases. To reduce these issues, appropriate weed, water, and fertilizer management practices should be applied. Chemical and biotechnological methods, such as herbicide-resistant and more competitive allelopathic variants, will be required for sustainable rice production. The development of site- and soil-specific integrated packages will increase the adoption of DSR and decrease the negative effects of PTR on the environment.

ENHANCING PHOTOVOLTAIC PANEL EFFICIENCY WITH AN IOT-ENABLED ROBOTIC CLEANING SYSTEM – A COMPREHENSIVE REVIEW

The maintenance and cleaning of photovoltaic panels is critical to ensure maximum energy output and prolong their lifespan. However, manual cleaning of large-scale solar farms is time-consuming, labor-intensive, and expensive. To address this challenge, this paper proposes an IoT-based robotic cleaner for efficient monitoring and cleaning of photovoltaic panels. The proposed robotic cleaner is equipped with a camera, sensors, and a cleaning mechanism. The camera captures real-time images of the solar panels, which are analyzed by an AI-based algorithm to detect and locate dirt, debris, and other obstructions. The sensors measure environmental factors such as temperature, humidity, and light intensity, which are used to optimize the cleaning schedule and ensure the safety of the cleaning operation. The cleaning mechanism of the robotic cleaner is based on a high-pressure water jet that removes dirt and debris without damaging the solar panels. The water is supplied by an onboard tank and can be heated or cooled as required. The cleaning process is automated and can be controlled remotely through a web-based interface. The proposed IoT-based robotic cleaner offers several benefits compared to manual cleaning. It reduces labor costs, minimizes the risk of injury to workers, and improves the efficiency of cleaning operations. Moreover, it ensures consistent and high-quality cleaning, which leads to increased energy output and prolongs the lifespan of the solar panels. Overall, this paper presents a novel approach to automate the cleaning of photovoltaic panels using IoT and robotics technologies. The proposed solution has the potential to revolutionize the maintenance of solar farms and contribute to the development of sustainable and clean energy systems.

Automatic Fruit Quality Inspection System Using Image Processing

The demand for high-quality fruits has increased significantly, driven by consumers' growing emphasis on health and nutrition. To ensure consistent quality control and efficient fruit grading processes, an automatic fruit quality inspection system using image processing techniques has been developed. This system leverages Convolutional Neural Network (CNN) algorithms to achieve an impressive accuracy rate of 99% in fruit quality assessment. The proposed system involves a multi-step process starting with the acquisition of high-resolution fruit images. These images undergo pre-processing to enhance clarity and eliminate noise or artifacts. Subsequently, the pre-processed images are fed into a trained CNN model for feature extraction and classification based on learned patterns. The CNN model has been trained on a large dataset of labeled fruit images, enabling it to recognize quality attributes such as colour, size, shape, and defects. The system's evaluation involved a diverse range of fruits, encompassing various species, varieties, and maturity stages. The output demonstrate the system's exceptional accuracy, with a 99% success rate in correctly identifying and grading fruit quality attributes. This automatic fruit quality inspection system offers several advantages, including real-time processing, high efficiency, and reduced labor costs, making it suitable for integration into fruit processing facilities and supply chains. [1]

Performance of a new family of modular, bed-supported, chromatography devices.

Prepacked chromatography columns and cassette filtration units offer many advantages in bioprocessing. These include reduced labor costs and processing times, ease of storage, and enhanced process flexibility. Rectangular formats are particularly attractive as they can be easily stacked and multiplexed together for continuous processing. Cylindrical chromatography beds have dominated bioprocessing even though their bed support and pressure-flow performance vary with bed dimensions. This work presents the performance of novel, rhombohedral chromatography devices with internally supported beds. They are compatible with existing chromatography workstations and can be packed with any standard commercial resin. The devices offer pressure-flow characteristics independent of container-volume, simple multiplexing, and separation performance comparable to cylindrical columns. Their bi-planar, internal bed support allows mechanically less-rigid resins to be used at up to four times higher maximal linear velocities, and productivities approaching 200 g/L/h for affinity resins, compared to the 20 g/L/h typical of many column-based devices. Three 5 L devices should allow processing of up to 3 kg of monoclonal antibody per hour.

Research of the impact of artificial intelligence in marketing

The impact of artificial intelligence on the business processes of Ukrainian companies is studied. The experience of some leading Ukrainian companies that are already successfully implementing artificial intelligence, which results in increased productivity and reduced labor costs, is analyzed. The article highlights the advantages and disadvantages of using artificial intelligence in marketing researches, and also the challenges and obstacles that may occur when using artificial intelligence in marketing. Software platforms and libraries are presented according to the type of artificial intelligence.

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete.

The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m3), cement (Kg/m3), silica fume (Kg/m3), fly ash (Kg/m3), coarse aggregate (Kg/m3 & mm for diameter), fine aggregate (Kg/m3 & mm for diameter), viscosity modifying agent (Kg/m3), fibers (Kg/m3), fiber properties (mm for diameter and MPa for strength), print speed (mm/sec), and nozzle area (mm2), while target properties were the flexural and tensile strength of concrete (MPa data from 25 literature studies were collected. The water/binder ratio used in the dataset ranged from 0.27 to 0.67. Different types of sands and fibers have been used, with fibers having a maximum length of 23 mm. Based upon the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) for casted and printed concrete, the SVM model performed better than other models. All models' cast and printed flexural strength values were also correlated. The model's performance has also been checked on six different mix proportions from the dataset to show its accuracy. It is worth noting that the lack of ML-based predictive models for the flexural and tensile properties of 3D-printed concrete in the literature makes this study a novel innovation in the field. This model could reduce the computational and experimental effort required to formulate the mixed design of printed concrete.

Reflow profiling with the aid of machine learning models

PurposeThis paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.Design/methodology/approachA highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.FindingsML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.Practical implicationsThe methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.Originality/valueIn this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

Method and apparatus for automatic high-throughput synthesis of energetic salts

Energetic materials can instantly combust and release a large amount of energy in responding external impulses. Due to its energy characteristics, it has a wide range of practical applications: military and civilian. Designing high-energy-density materials with better performance, lower sensitivity, and more environmental friendliness has become a hotspot in the field of energetic materials in recent years. Energetic salts are formed by acid-base or metathesis reactions of different types of anions and cations. Energetic salts are a new class of energetic materials which were developed in recent years. Usually, they have high nitrogen content, high enthalpy of formation, high detonation performance, are relative stable to external stimuli, and have a wide range of application prospects in the fields of new explosives: low characteristic signal propellants, gas generators, low smoke or smokeless pyrotechnics. The synthesis method of energetic salts is simple, and the types of energetic materials are greatly enriched. This research takes energetic salts as the synthesis target and adopts multi-channel automation technology for high-throughput synthesis, which can improve the efficiency, reduce labor costs, and greatly increase the synthesis speed, which is of great significance for accelerating the synthesis of new energetic salts.

A Bin-Picking Benchmark for Systematic Evaluation of Robotic-Assisted Food Handling for Line Production

Robotic manipulation and automation have gained increasing popularity in the food manufacturing industry due to their potential benefits for enhancing hygiene standards, enforcing quality consistency, promoting product traceability, and reducing labor costs. As a majority of robotic manipulation, the pick-and-place operation plays a crucial role in food handling applications. However, the reproducibility and comparability of results have put a dilemma that hinders further advancement in this field, especially for those unstructured scenarios. To tackle such thorny issues, this article proposes a benchmarking framework for system-level evaluation of robotic-assisted food handling under the line production environment. A typical food handling scenario, including a pick-and-place operation and a packing operation, is presented as the benchmark task, where food items are supposed to be picked from the tray and placed in the serving dish. A robotic system incorporating a high-speed Delta robot, vision system, conveyor belt, and end-effector is developed as the testbed for the benchmarking implementation. Finally, five variants of the robotic system with different end-effectors are evaluated using the proposed benchmarking framework. Comparative studies illustrate the performance of various benchmarked systems and validate the applicability of the benchmarking strategy for the food handling context. Videos of our experiments are available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/SBAOoswnjWM</i> .

Fresh properties and flexural strength of 3D printing sustainable concrete containing GGBS as partial cement replacement

The most extensively used construction materials in the world are cement, mortar, and concrete. GGBS is an alternative to Portland cement that can reduce carbon dioxide emissions significantly and has been recognized as an environmental-friendly construction material. It can also be used as a substitute or as a supplementary to cement, which can help promote sustainable development globally. On the other hand, 3D printing concrete is a revolutionary concrete building process that is developed by many research institutes and companies across the world. Some of the benefits of 3D printing concrete are the use of materials is optimized, reduce labour cost, fast construction and able to construct customized buildings. However the study about 3D printing concrete materials needs more extensive works in order for the materials to fulfill the criteria of 3D printing concrete properties. Therefore, this study tends to explore some of the materials properties of 3D printing concrete. The objective of this study is to investigate the fresh properties and flexural strength of 3D printing concrete containing GGBS as partial cement replacement and to evaluate the optimum percentage of GGBS as partial cement replacement in 3D printing concrete. The percentage replacement of GGBS that was carried out in this study was 0%, 20%, 30%, 40%, and 50% with a 0.5 w/c ratio. Extrudability, flowability, buildability, and flexural strength tests were performed. From the experimental results, it shows that 30% of GGBS replacement recorded the highest flexural strength. The mix also performs well in extrudability and buildability test.