Reduce Labor Costs Research Articles

BIOSTATISTICAL SOFTWARE: MICROSOFT EXCEL, SPSS AND MINITAB MAKES FINDING CENTRAL TENDENCY AND DISPERSION EASY AND EFFECTIVE

When we were in school, we were taught how to use a note book and a pen to determine central tendency and dispersion. The entire process took too long and required too much time. These days, we can accomplish this using a programme called biostatistical software. These programmes reduce labour costs as well as time. India consistently ranks as one of the top five pharmaceutical producing nations in the world. The use of software, which allows a work to be completed with less effort and more accuracy, is growing steadily. In research, the use of computers and innovative software is becoming more and more frequent, particularly in the field of biostatistics, which deals with the application of mathematics to biological systems (living things). Due to its usage in both experimental and observational research, biostatistics is advantageous not only to the pharmaceutical business but also to medical and paramedical schools (such as nursing and pharmacy schools). Pen and paper calculations and measurements take an enormous amount of time and are inefficient. Currently, a variety of statistical software is available for the fulfilment of tasks that can quickly produce results. Software programmes used for statistical analysis include Minitab, SPSS, R studio and Microsoft Excel. This article will go over various biostatistical software programmes and how to utilise them to calculate central tendency and dispersion. Because it is free, versatile and simple to use MS Excel is the biostatistical programme that is utilised the most. Cons and benefits can be found in every piece of software. Microsoft Excel is free, whereas other programmes require a monthly or annual fee. There are available demo versions of these that can be used for educational purposes

Introducing technology of planting methods to increase rice production for sustainable farmers’ income

Background: Rice self-sufficiency is still a great concern in Indonesia; in order to feed the population of 270 million people, farmers must implement new technology to boost rice production. This study aimed to provide information on the differences between direct seed planting and its conventional counterpart in terms of productivity and farmer income. Secondly, we aimed to characterize the productivity and farmer income for two legowo planting systems (legowo 4:1 and legowo 2:1). Finally, farmers’ perceptions of the legowo implementation were evaluated. Methods: This study was carried out in Konawe District at the farm level in Karandu Village, Anggotoa Sub District of Southeast Sulawesi Province, Indonesia. The research design was implemented to allow comparison between legowo 4:1 and legowo 2:1. 60 farmers who implemented legowo 2:1 and legowo 4:1 respectively, were selected. Moreover, 30 farmers who did not implement both 4:1 and 2:1 were selected as the conventional planting method. This research design was implemented in order to compare between legowo and non legowo planting systems, as mentioned above, 60 respondents will compare with 30 farmers in terms of yield and economic advantage. Lastly, in order to determine technological perceptions, a total of 150 farmers were interviewed. Results: The study case results showed that direct seed planting is much more profitable than its conventional counterpart. The introduction of legowo 4:1 was highly significant in boosting rice production compared to legowo 2:1. In terms of perception, we found that the low cost and the availability of legowo are the most important parameters for the adoption of this technology. Conclusions: In summary, we recommend the implementation of the legowo 4:1 planting system to boost rice productivity. By reducing labor costs, the direct seeding method increased farmers’ income.

Identification of Embryonic Chicken Proteases Activating Newcastle Disease Virus and Their Roles in the Pathogenicity of Virus Used as In Ovo Vaccine.

In ovo vaccination is an attractive immunization approach for chickens. However, most live Newcastle disease virus (NDV) vaccine strains used safely after hatching are unsafe as in ovo vaccines due to their high pathogenicity for chicken embryos. The mechanism for viral pathogenicity in chicken embryos is poorly understood. Our previous studies reported that NDV strain TS09-C was a safe in ovo vaccine, and the F protein cleavage site (FCS) containing three basic amino acids (3B-FCS) was the crucial determinant of the attenuation of TS09-C in chicken embryos. Here, five trypsin-like proteases that activated NDV in chicken embryos were identified. The F protein with 3B-FCS was sensitive to the proteases Tmprss4, Tmprss9, and F7, was present in fewer tissue cells of chicken embryos, which limited the viral tropism, and was responsible for the attenuation of NDV with 3B-FCS, while the F protein with FCS containing two basic amino acids could be cleaved not only by Tmprss4, Tmprss9, and F7 but also by Prss23 and Cfd, was present in most tissue cells, and thereby was responsible for broad tissue tropism and high pathogenicity of virus in chicken embryos. Furthermore, when mixed with the protease inhibitors aprotinin and camostat, NDV with 2B-FCS exhibited greatly weakened pathogenicity in chicken embryos. Thus, our results extend the understanding of the molecular mechanism of NDV pathogenicity in chicken embryos and provide a novel molecular target for the rational design of in ovo vaccines, ensuring uniform and effective vaccine delivery and earlier induction of immune protection by the time of hatching. IMPORTANCE As an attractive immunization approach for chickens, in ovo vaccination can induce a considerable degree of protection by the time of hatching, provide support in closing the window in which birds are susceptible to infection, facilitate fast and uniform vaccine delivery, and reduce labor costs by the use of mechanized injectors. The commercial live Newcastle disease virus (NDV) vaccine strains are not safe for in ovo vaccination and cause the death of chicken embryos. The mechanism for viral pathogenicity in chicken embryos is poorly understood. In the present study, we identified five trypsin-like proteases that activate NDV in chicken embryos and elucidated their roles in the tissue tropism and pathogenicity of NDV used as in ovo vaccine. Finally, we revealed the molecular basis for the pathogenicity of NDV in chicken embryos and provided a novel strategy for the rational design of in ovo ND vaccines.

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixel-wise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

Child and family outcomes of the Safe Babies Court Team – A scoping review

Children under the age of three are frequently removed from their home and placed in foster care, resulting in critical impediments to their development and well-being. These placements result in significant short and long-term economic costs. The Safe Babies Court TeamTM (SBCT) intervention was created to provide an alternative to the traditional welfare system by serving the needs of parents and children with a multidisciplinary team led by a judge and child welfare leader trained in trauma-informed practice and infant mental health. The SBCT intervention has been implemented across states and counties within the United States to various extents: certain locations have only implemented partial elements of the intervention or have extended it to children up to five years of age. Many of the SBCT implementations have published reports which discuss the methods and outcomes seen in each court. This scoping review aims to comprehensively characterize the effects of SBCT, both child-centered and economic, across the different implementations. We summarize forty articles and reports that document outcomes for children, families, and courts served by SBCT sites across the United States. Overall, SBCT was shown to be promising for children and families who underwent the intervention through positive outcome comparisons. SBCT may have positive economic benefits by reducing labor costs and out-of-home payments in the short-term, though long-term studies are needed for more comprehensive conclusions.

An Automated Irrigation and Monitoring System Using IoT: A Survey

The Internet of Things (IoT) is revolutionizing the agricultural business and assisting farmers to overcome the enormous obstacles they confront. Agriculture is a vast area that needs to be studied. About 80% of India’s surface water is used for agriculture. It can be used for a plethora of applications. Proper irrigation is still a challenge in most agriculture practices. Farming is critical to the development of a nation like Ethiopia. Agriculture-related issues have consistently stymied the country's development. The difficulties that the Internet of Things (IoT) may handle, include regulating cattle, sustainability, and plants and soil. The only solution to this challenging problem is contemporary agriculture and technology for sustainable water use by updating traditional agricultural techniques. By solving issues in farming, having to cut IoT applications boosts agricultural production's efficiency, quality, durability, and cost-effectiveness. This reduces the risk of over or under-watering, resulting in healthier plants and higher yields. Today's enormous and tiny farms may apply IoT to monitor the location that may monitor crop development, identify insects, and assess water content as well as their smart linked cultivators and irrigation. Moreover, the system also reduces the need for manual monitoring and intervention, saving time and reducing labour costs. This project improves the watering operation while monitoring soil parameters including water content, temperatures, and conductivity. Node-MCU is applied to make judgements. If there is a deviation from the impact on succeeding, the user is warned by text message. This guarantees the whole system's health and it enables the system to use an appropriate quantity of water which avoids over/under irrigation.

Problems and countermeasures of accounting informatization construction in colleges and universities under the background of big data and artificial intelligence

As the scale of online socialization continues to expand, the amount of data generated by people’s social networking continues to increase, and data has gradually become the invisible wealth of various industries, and data analysis, as the main method to control data, has become a skill that must be mastered in the development of talents in various industries. Big data technology is a data storage and processing method based on data analysis technology. Prediction of the law of development of a certain thing and analysis of the success rate of a certain decision. Big data technology has played an important role in the informatization of various industries, and its use in accounting informatization can bring more convenience to accounting work. Although artificial intelligence technology has only been born for less than a hundred years, the technical branches it has developed have been widely used in various industries. At present, the types of algorithms corresponding to artificial intelligence technology are still increasing, and the application of AI algorithms to the development of accounting informatization can make Accounting work is more intelligent, and its accuracy can be improved on the basis of greatly reducing labor costs. Putting the construction of accounting informatization in colleges and universities under the technical background of artificial intelligence and big data development can achieve more valuable breakthroughs in data collection and countermeasure optimization. This paper first analyzes the theories of big data, artificial intelligence and accounting informatization, and then obtains various problems existing in the development of accounting informatization construction in colleges and universities through questionnaires. By comparing the newly designed solutions with the traditional accounting informatization construction solutions, it is found that the accounting informatization construction solutions combined with big data and artificial intelligence can be used in accounting personnel training, accounting platform security hidden danger investigation and college accounting. Internal control and other directions are superior to the traditional accounting informatization construction plan.

How smart technology empowers consumers in smart retail stores? The perspective of technology readiness and situational factors.

Smart retail stores have been gaining momentum in smart retailing. Instead of relying on in-store staff like traditional counterparts, smart retail stores provide an unmanned environment purely enabled by various in-store smart technologies that support customers throughout the shopping journey. This unstaffed operating model also enables smart retail stores to provide competitive prices by reducing labor costs. However, studies have overemphasized the unique value offered by smart technology but discounted the common value strengthened in smart retail. This study applies the situational factor framework to identify both unique and common factors empowered by smart technology from a comprehensive perspective; then, technology readiness is incorporated to explore consumer purchase intentions in smart retail stores. A total of 283 survey data were collected and analyzed. The main results indicate that most situational factors have a direct effect on purchase intention, and technology readiness enhances the unique situational factors enabled directly by smart technology.

VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs

Code vulnerability detection has long been a critical issue due to its potential threat to computer systems. It is imperative to detect source code vulnerabilities in software and remediate them to avoid cyber attacks. To automate detection and reduce labor costs, many deep learning-based methods have been proposed. However, these approaches have been found to be either ineffective in detecting multiple classes of vulnerabilities or limited by treating original source code as a natural language sequence without exploiting the structural information of code. In this paper, we propose VDoTR, a model that leverages a new tensor representation of comprehensive code graphs, including AST, CFG, DFG, and NCS, to detect multiple types of vulnerabilities. Firstly, a tensor structure is introduced to represent the structured information of code, which deeply captures code features. Secondly, a new Circle Gated Graph Neural Network (CircleGGNN) is designed based on tensor for hidden state embedding of nodes. CircleGGNN can perform heterogeneous graph information fusion more directly and effectively. Lastly, a 1-D convolution-based output layer is applied to hidden embedding features for classification. The experimental results demonstrate that the detection performance of VDoTR is superior to other approaches with higher accuracy, precision, recall, and F1-measure on multiple datasets for vulnerability detection. Moreover, we illustrate which code graph contributes the most to the performance of VDoTR and which code graph is more sensitive to represent vulnerability features for different types of vulnerabilities through ablation experiments.

Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions.

Measurement Method for Wall Crack Size Based on Pixel Ratio

The identification and measurement of building cracks can reduce the harm caused by accidents to people’s lives. Reducing manual work can improve the efficiency of crack inspection and reduce inspection errors. In this paper, (1) Machine vision with high measurement accuracy and fast response speed is used, the crack contour is extracted by OpenCV splicing, segmentation, morphological processing, and wall cracks can be accurately detected. (2) The crack size is calculated using the pixel ratio algorithm, the image pixel ratio is calculated by the reference object method, and the size of the wall crack is measured. (3) The measurement error is small. In comparing the experimental results and manual measurement, the width measurement error rate is the highest, 0.314%, and the height error rate is the highest, 2.9%. (4) Size information can be displayed to prevent the hazard caused by cracks in advance. So, the system has a good wall crack identification and measurement effect, which reduces labor costs.

Application of AGV in the Production System at the PT. Adhikara Wiyasa Gani

This study aims to analyze the effectiveness and efficiency of applying AGV in the production system at the PT. Adhikara Wiyasa Gani. AGV is implemented as a means of transporting materials from the warehouse to the production line, as well as returning finished goods to the warehouse. The research method used was data collection through field observations, interviews with workers, and analysis of production data before and after using AGV.
 The research results show that the use of AGV can increase the effectiveness and efficiency of the production system. The time required for material delivery from the warehouse to the production line and the return of finished goods to the warehouse can be minimized, thereby speeding up production time. In addition, AGV can also reduce production costs by reducing labor costs and minimizing the risk of human error in shipping goods.
 In conclusion, the application of AGV can have a positive impact on the production system at the PT. Adhikara Wiyasa Gani. However, further research can be conducted to deepen the effectiveness and efficiency of AGV implementation in production systems in general.

321 Automated Development of a Spine Registry (Autoregistry) using an Interpretable Surgeon-Written Regular Expression Classifier

INTRODUCTION: Clinical registries are critical for modern surgery and underpin outcomes research, reimbursement negotiations, device monitoring, and trial development. Existing approaches to registry construction are labor intensive, costly, and prone to user error. Natural language processing (NLP) techniques combined with broad electronic health record (EHR) datasets can theoretically automate the building and maintenance of registries. METHODS: We utilized a Hadoop datalake consisting of the EHR, billing systems, imaging systems, and other data representing all information generated by an AMC. Using this database and SQL queries, we retrieved every operative note written in the department of neurosurgery over the last decade. Operative notes were parsed using regular expressions constructed by the operative surgeons themselves. A random subset of 100 notes was selected for manual review and analysis. RESULTS: 31,139 operative cases were identified and downloaded. An operative note processor was written by the operating surgeons and required three days of development time with approximately two weeks of validation. In 100 random cases, the regex classifier was on average 98.6% accurate at identifying whether a particular procedure was performed and correctly identified the entire list of defined surgical procedures in 88% of patients. Most errors (64.2%) occurred during the identification of the involved vertebral levels. Operative notes from ancillary specialties for assistive approaches or closures also led to false-positive results. CONCLUSIONS: This study demonstrates the feasibility of automatically generating a spine surgery registry (“autoregistry”) using a Hadoop EHR datalake combined with an interpretable and customizable surgeon-written natural language processing algorithm, which may reduce labor costs and other pitfalls associated with manual registry development.

Study on fibre reinforced foam concrete-a review

‘Foamed concrete’ is one of the spectacular material which has an inevitable property in construction field. It provides good insulation from heat and sound. This paper mainly focused on manufacturing of foamed concrete and the material used for the foam concrete-like binders, foaming agents, water quantity, plasticizers, and fibres. Functional, fresh state and hardened state properties of foam concrete including workability, density, tensile strength, flexural strength, compressive strength, ductility, drying shrinkage, sound insulation and thermal properties were also discussed.Among the different types of foamed concrete, Fibre-reinforced foam concrete (FRFC) attracts more researcher’s attention due to its better tensile strength, toughness, compressive strength, and ductility comparing with the normal plain foamed concrete. Foam concrete reduces labor costs and reduces stress resulting in the overall economy of the structure. In FRFC, there are air bubbles captured in the hardened state which makes it contrast with fibre-reinforced concrete and the involvement of fibre helps in the reduction of cracks. Fibre-reinforced foam concrete has high resistance to heat, fire resistance, and thermal insulation; hence it is used during the construction of bridges, roadways, building pillars, etc. This paper presents critical review on properties of FRFC and its applications in the construction field.

Smart Waste Management: Waste Segregation using Machine Learning

In the digitized era, the role of smart mechanisms plays a vital role and one among them is the segregation of waste. To make use of proper disposal and waste management techniques, the segregation of wastes is essential. In the existing systems, drones are used for identifying waste using image processing, and deep learning and use GPS, and GSM methods to identify and send locations to the authorities. The enhancement achieved is to analyze and implement waste segregation with the help of image classification and multi-object detection. Waste management may therefore be done more efficiently with an accuracy of 95% with a mean average of 87.4% which in turn helps significantly to reduce labor costs.

Research Review of Ship Draft Observation Methods

In order to systematically analyze and summarize the research status and development trend of draft observation, co-work cluster analysis base on draft observation was carried out by using knowledge VOS viewer software. Two research hotspots of draft observation were obtained, human visual observation combined with auxiliary equipment and artificial intelligence observation. The object and method of ship draft observation are summarized, and the main research direction is analyzed. The analysis focuses on improving observation accuracy, reducing labor costs, and being able to apply in complex environments. Future research directions of draft observation mainly include how to combine the traditional ship draft observation with artificial intelligence in the future, reduce error of draft observation to millimeters, identify obstacles, explore neural network autonomous learning optimization models, reduce the observation time and labor cost and meet the requirements of diversified application scenarios. At the same time correct the attitude that the accuracy of the current human observation is not high enough or even a negative in the artificial intelligence research area, finally promote the ship, insurance assessment, maritime and other aspects, in order to make the technology which combined artificial observation with intelligent assistance can be applied in practice to solve relevant lawsuits and provide an authoritative legal basis, and be used as the legal basis in the declaration of the ship and subsequent legal disputes.

Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling

Waste recycling is a critical issue for environment pollution management while garbage classification determines the recycling efficiency. In order to reduce labor costs and increase garbage classification capacity, a machine vision system is established based on the deep learning and transfer learning. In this new method, an improved MobileNetV2 deep learning model is proposed for garbage detection and classification, where the attention mechanism is introduced into the first and last convolution layers of the MobileNetV2 model to improve the recognition accuracy and the transfer learning uses a set of pre-trained weight parameters to extend the model generalization ability. In addition, the principal component analysis (PCA) is employed to reduce the dimension of the last fully connected layer to enable real-time operation of the developed model on an edge device. The experimental results demonstrate that the proposed method generates 90.7 % of the garbage classification accuracy on the “Huawei Cloud” datasets, the average inference time is 600 ms on the raspberry Pi 4B microprocessor, and the model volume compression is 30.1 % of the basic MobileNetV2 model. Furthermore, a garbage sorting porotype is designed and manufactured to evaluate the performance of the proposed MobileNetV2 model on the real-world garbage identification, which turns out that the average garbage classification accuracy is 89.26 %. Hence, the developed garbage sorting porotype can be used a effective tool for sustainable waste recycling.

Decentralized Voting System using Blockchain

This article explores the use and impact of voting using blockchain technology. As a result of mounting worries about the honesty and security of conventional voting methods, especially in the digital era, the study tackles the requirement for an open, impenetrable, and robust method of conducting elections. A decentralized application on a blockchain platform is designed and implemented using the technique, which makes use of smart contracts for voter registration, ballot generation, and vote counting. The viability of blockchain technology in guaranteeing voter anonymity, data integrity, and cryptographic security are important discoveries. Online voting can be done securely and digitally using blockchain technology. It has great potential to reduce costs and increase voter turnout. Using Blockchain we can secure any application and work securely for that application. Therefore, cryptocurrency, healthcare, real estate, etc. Many applications use blockchain technology. *Blockchain technology encrypts votes, preventing any votes from being tampered with. It allows voters to vote for a candidate only once. The system reduces labor costs and counting errors by providing quick access to election results. This article explains solutions to solve the above problems. Our solution is to use an online voting system based on the Ethereum blockchain. That's why we connect surveys to blockchain technology

Обработка замечаний экспертизы с использованием Среды Общих Данных (СОД)

The productivity of the construction industry has declined in recent decades compared to the manufacturing industry, which doubled its productivity over the same period.
 The advantages of automation in the manufacturing industry may be the reason for high labor productivity. Moreover, automation has recorded a steady reduction in labor costs and an increase in labor productivity, as well as reducing routine tasks and manual labor.
 These huge advantages have led to the emergence of research into the application of automation of construction processes. In parallel with this, there is a need to automate processes that are vital to ensure the rapid creation of high-quality housing infrastructure.
 As part of the implementation of state programs for the transition to the digital economy, including in the construction industry, the tasks of implementing information modeling technology at all stages of the building life cycle are becoming urgent. Given the large amounts of data and high requirements for the accuracy of information models, the processes of automating the processing of source data and the formation of a shared data environment come to the fore.
 The main purpose of the study is to analyze the existing business process of processing expert comments to form its map and modeling the process based on it using CDE.

Методические подходы к определению затрат труда при проектировании объектов строительства

The life cycle of buildings includes the labor costs of all categories of management and construction personnel involved in construction from the beginning of preparatory work to the commissioning of the facility. Additionally included here are the costs of developing project documentation. Qualitatively developed project documentation leads to a positive result of construction activity. The price of design works is determined based on the following costs: personnel labor costs, insurance payments, expenses for all types of repairs, material costs, maintenance of buildings and/or rent, taxes and fees. A competent assessment of labor costs and their systematization will help to identify promising segments of labor cost optimization that most significantly affect the reduction of total labor costs.