External Two Stage DEA Analysis of Bank Efficiency in West Balkan Countries

Since the beginning of the application of the Data Envelopment Analysis (DEA) model in various areas of the economy, it has found its wide application in the field of finance, more specifically banks, in the last few years. The focus of this research was to determine the sustainability of the intermediate function of banks, especially in recent years when interest rates on deposits have been at a minimum level. The research was divided into two parts, wherein the first part determined the efficiency of the intermediate function of banks in the countries of the Western Balkans in the period from 2015 to 2019. The second part approached the regression analysis in which we determined the influence of the bank size, type of bank, and mergers and acquisitions (M&A) activity on the defined efficiency. In the first stage we applied the output-oriented DEA model using deposits, labor costs, and capital as input variables; on the other side, we used loans and investments as output variables. We used data from the revised financial statements of the banks operating in Serbia, Bosnia and Herzegovina, Montenegro, North Macedonia, and Albania. The results of our study showed that there is a difference in efficiency levels between countries and within countries in the considered time period. Furthermore, Tobit regression analysis showed a significant and negative influence of the bank type and M&A on relative technical efficiency of banks, and a positive and significant relationship between bank size and relative efficiency. These findings suggest that large commercial banks can sustain on the West Balkan market. It is to be expected that less efficient small banks will be taken over by large and more efficient banks.

Automatic Receipt Recognition System Based on Artificial Intelligence Technology

In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.

A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Factors Affecting Construction Sector Safety Performance: Exploratory Factor Analysis Evidence from China

Construction industry is a significant contributor to the Chinese economy. The industry has more than 12 million employers with over 250 million employees and creates almost $1.9 trillion worth of structures yearly. Civil construction remains the main driver of growth in China. Basically, a task is developed to meet market demands or demands in a timely fashion. Different possibilities may be thought about in the conceptual drawing board, and also the technical and also financial feasibility of each alternative will be assessed and compared in order to select the very best feasible job. The construction industry in China is forecast to grow by 7.7% in 2021, driven by strong Y-o-Y growth in the first quarter, reflecting the comparison to the previous year's period when construction work was halted across most of the country. Thereafter, the construction industry is expected to record an average annual growth of 4.2% between 2022 and 2025. The industry's growth over the forecast period is expected to be driven by investments on new infrastructure, including investment in the areas of 5G networks, Artificial Intelligence, the Internet of Things, and data centers. According to the government-backed think tank, the China Electronic Information Industry Development, the country is expected to spend CNY10 trillion (US$1.4 trillion) on new infrastructure projects between 2020 and 2025. This study evaluated factors affecting construction sector performance: explanatory factor analysis evidence from China. From the literature reviewed, it was established that entering the Chinese construction market is still seen as exciting but difficult by many foreign contractors and consultants. The study found out that rising material and labor costs, labor woes, increased competition and shrinking profit margins were some of the challenges construction firms in Chin face. The study concludes that the implementation of construction safety laws and the rate of subcontracting are relevant factors affecting construction sector in China, while neither the extent of using temporary workers, nor the availability of resources, nor the level of per capita GDP has any effects. Keywords. Construction sector, safety performance, construction sector, labor costs, increased competition, shrinking profit margins

Cost behavior in e-commerce firms

We conduct empirical research on the flexibility of operating costs of e-commerce firms. With an international sample of firms from different European countries, we find that e-commerce firms have a different cost structure than traditional retail firms, with a lower share of labor costs and cost of goods sold, but a higher share of other operating costs. While we find no significant different behavior in cost of goods sold and labor costs between the two types of firms, e-commerce firms are more flexible in adjusting other operating costs than traditional retail firms when activity decreases. Results are robust to different models, estimations methods and samples. The higher flexibility of e-commerce firms relies on other operating costs, but e-commerce creates qualified jobs with higher wages than traditional retail, with no additional exposure to labor uncertainty for employees.

Strengthening Taiwan’s Green Building Certification System from Aspects of Productivity and Energy Costs to Provide a Healthier Workplace

This study estimates the relationship between poor indoor environmental quality (IEQ) and the increasing labor costs in green buildings in Taiwan. Specifically, poor performance of IEQ including HVAC, lighting, and indoor air quality, influences the health and well-being of occupants and leads to worse productivity, ultimately causing increased personnel cost. In Taiwan’s green building certification (GBC) system, the energy-savings category is mandatory while the IEQ category is only optional. It means that certified building cases may not reach the expected level in IEQ. Thus, this study reviews the thermal environment, indoor air quality (IAQ), and illumination performances of IEQ-certified and non-IEQ-certified buildings in 20 green buildings. Building energy and IEQ simulations were conducted to analyze the relationships between indoor comfort, energy cost, and personnel cost in green buildings. The results show that IEQ-certified green buildings averagely perform better than non-IEQ-certified ones in the aspects of IEQ and building costs. Besides, 3 of 13 non-IEQ-certified green buildings undertake extremely high additional expenditure for the poor IEQ. The results correspond to some previous findings that green-certified buildings do not necessarily guarantee good building performance. This study further inspects the pros and cons of Taiwan’s GBC system and proposes recommendations against its insufficient IEQ evaluation category. As the trade-off of energy-saving benefits with health and well-being in green buildings has always been a concern, this study aims to stimulate more quantitative research and promote a more comprehensive green building certification system in Taiwan.

Prospects of fish supply-demand and its implications for food and nutrition security in Egypt

Aquaculture plays an increasingly important role in meeting the rising global demand for fish fuelled by economic and demographic growth. However, in many middle income countries, the growth of aquaculture is constrained by rising labor costs, limited input supply, environmental concerns, and infectious diseases. In this paper, we developed a multi species, multi sector equilibrium model and applied it to the fishery sector of Egypt, a leading aquaculture producer in Africa, to examine these barriers. Projection results show that rising wage rates would slow down the growth of labour-intensive aquaculture compared to those that use relatively less labour. The demand for feed, seed inputs and water use for aquaculture would substantially increase. The results also show that disease outbreaks would possibly affect production sectors via output reduction and also consumers via increases in fish price. Our findings suggest that stabilising the prices of feed and seed, investments in disease control and input use efficiency improvement technologies, including water use, are important while the overall effectiveness of tax instruments is modest. Though calibrated to Egypt, our approach can be applied to other middle size national aquaculture industries.

Effectiveness of Public Spending before and during COVID-19 Pandemic

The pandemic of COVID-19 influences all sectors of the economy. It caused decreasing in produced Gross domestic product (GDP) and higher unemployment. As it is known, to overcome this negative tendency, it is possible to put in practice monetary and fiscal instruments. During the pandemic, the government tried to slow down negative economic results through public spending. With them, the government looks to be increased aggregate demand in the economy and as a result-GDP raises and unemployment reduces. The research is based on created original model for testing the impact of total public spending, capital, salary, social insurance and care, for maintenance by a consolidated fiscal program on the value of GDP. The changes of GDP measure the effectiveness of public spending. The period of research is before and during the COVID-19 crisis (2019-2020) in the case of Bulgaria. Before the pandemic the analysis shows coefficient of determination for capital spending is more significant compare with all other types of public expenditure and these cost predetermine economic growth. During the pandemic of COVID-19 public spending has used as the main instrument to overcome the negative results for the economy. For this period it found an extremely strong impact of labor costs and social care expenditure on aggregate demand. They bring more positive results to be solved health issues, but not for faster recovery of the economy.

Some Evaluations on the Technical State of Metal Products and Determination of their Safe Operation Period on the Basis of Criterial Control

Over a long period of operation, under the influence of corrosion and stresses from the acting forces, metal structures lose their strength. There is a need for their periodic non-destructive testing. The development of new methods is relevant in the field of control of building metal structures, such as bridge structures, structures of building cranes and other mechanical engineering products. The applied methods should be reliable and should not require huge material and labor costs. In this work, informational relationships between acoustic characteristics and parameters of metal microstructure are established. The proposed method can be useful for specialists and experts in the field of monitoring the technical condition of metal products requiring non-destructive testing. The safety of the operated objects depends on the accuracy of the applied criteria, as well as the degree of resource saving due to the full use of the product resource.

Farm Parcel Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

Background: Land-use classification schemes typically address both land use and land cover. Vectorized data extracted from farm parcel segmentation provides important cadastral data for the formulation and management of climate change policies. It also provides important basic data for research on pest control in large areas, crop yield forecasts, and crop varieties classification. It can also be used for the assessment of compensation for damages related to extreme weather events by the agricultural insurance department. Firstly, we investigate the effectiveness of an automated image segmentation method based on TransUNet architecture to enable that automate the task of farm parcel delineation that originally relied on high labor costs. Then, post-processing by vectoring binary segmentation image, which the area and regularity parameter to adjust the accuracy of segmentation, can get a more optimized image segmentation result.Results: The results on the existing data show that the automatic segmentation system we proposed is a method that can effectively divide various types of agricultural land. The system was trained and evaluated using 94780 images. The performance parameters obtained showed that the accuracy rate reached 83.31%, the recall rate reached 82.13%, the F1-S rate was 80.37%, the total accuracy rate was 82.23%, and Iou was 80.39%. At the same times, without losing too much accuracy, we train and test the model with 3m resolution image, which has the advantage of processing speed than 0.8m resolution. Therefore, our proposed method can be effectively applied to the task of extraction of agricultural land, which is better and more efficient than most manual annotations.Conclusions: We have demonstrated the effectiveness of strategy using a TransUNet architecture and postprocessing by vectoring binary segmentation for farm parcel extraction in high remote sensing images. The success of our approach is also a demonstration of feasibility of the deep learning to participate in and improve agricultural production activities, which is important for achieving scientific management of agricultural production.