ABSTRACT Objectives The purpose of this study was to estimate the cost of Dental Infection Control (DIC) according to the standard protocol developed by the Korea Dental Association (KDA) and Centers for Disease Control and Prevention (CDC) to compare it with the DIC cost of some real dental practices in Korea. Methods Based on a standard protocol, we calculated the number of consumables, time required, and labor costs for DIC. Additionally, the costs incurred per patient per treatment were calculated for dental practice based on a survey questionnaire. Results The cost of DIC according to the standard protocol was higher than the average cost incurred by dental practices responding to this study. The highest cost of treatment was oral surgery (16,539 KRW, 13.75 USD) and treatments with descending order of costs incurred are, periodontal surgery, endodontic treatment, filling, scaling, examination, and intraoral radiography. For invasive procedures, the calculated cost is higher than that of other treatments, because of the need for surgical protective gear. Conclusion The DIC cost based on the standard protocol was higher than that incurred by the dental practice surveyed. DIC is essential for patients and dental staff, and to reduce the difference between the DIC cost incurred by dental practice and dental infection cost according to standard protocols, it is important for dental staff to have the right perception and an active attitude toward infection control. Additionally, a realistic and systematic policy for DIC should be established to enable dental practices to implement it effectively.

The development and implementation of an artificial intelligence (AI) system in healthcare practice for personalized diagnosis, correction of cognitive impairments and prediction of outcomes in coronary heart disease (CHD), taking into account the presence of anemia syndrome, is a promising area in modern cardiology. The purpose of this review was to analyze publications on the problems of adherence to the treatment of cardiological patients, improving their quality of life, reducing mortality and disability, prolonging work activity, optimizing the labor costs of medical personnel, and the transition to digital healthcare of cognitive training IT-applications with AI. The implementation of the AI system will improve the treatment outcomes of patients with IHD and anemia syndrome, increase the efficiency of using healthcare resources and contribute to the development of personalized medicine in Russia. Further research will be aimed at evaluating the effectiveness of the system's implementation and expanding its functionality through the integration of data and monitoring of patients' physical activity.

This paper proposes an extended approach to assessing software ergonomics, combining the analysis of classical abstractions such as “functions”, “classes”, and “modules” with the peculiarities of microservice architectures. The study develops a methodology based on the “7±2” rule and inconsistency coefficient for abstraction hierarchies, adapting it to distributed systems. The work introduces three new metrics for microservices: API consistency factor, service autonomy index, and efficiency metric of resource utilization, all supported by the structural properties of systems. Problem Statement. In modern programming, with widespread adoption of microservice architecture, maintaining and evolving software become priority tasks. Poor ergonomics of growing code bases can lead to substantial labour and financial costs. Therefore, evaluation tools for software ergonomics in microservice architecture are necessary. The aim of the study is to develop a comprehensive assessment system for software ergonomics tailored to microservice architecture. The study results in addressing the evaluation of software ergonomics, defining ergonomics characteristics of code, and introducing three core metrics for assessing microservice architecture ergonomics. The practical significance of the work lies in the fact that hierarchical design approaches and evaluation methods of code ergonomics must provide standardized means for assessing software ergonomics in microservice architecture. These tools will allow simplifying the process of maintenance and enhancement of software products.

This paper examines the role of artificial intelligence (AI) technologies in reducing labor costs in agricultural production. Successes in increasing yields and reducing production costs are analyzed. Statistics on the implementation of digital technologies in Russian agricultural enterprises are presented, key trends are highlighted, and prospects for further automation and robotics are identified. Specific cases of successful implementation of innovative solutions are considered. It is shown that digitalization of the agricultural sector can significantly increase labor productivity, reduce material and financial costs, improve the accuracy of technological processes, and ensure stable growth in production volumes. Practical examples of the use of AI technologies are provided. Factors hindering the widespread adoption of AI, such as high initial investment, a shortage of trained specialists, and the lack of necessary infrastructure, are examined. Despite this, it is shown that investments in digitalization have a high potential for return on investment and can lead to increased profits. Conclusions are drawn about the large-scale positive impact of automation on the economy of the Russian agricultural sector.

Traditional radar-camera calibration requires manual intervention and excessive computational resources, resulting in high labor costs for maintenance in roadside perception scenarios. Thus, we propose a continuous online calibration method for roadside integrated radar-camera device. The method is based on azimuth angle and multi frame tracking. Firstly, the radar-camera corresponding points are matched by the target azimuth angle and its rate of change, thus achieving coarse calibration. It doesn’t need manual roadside parameter measurement, only need the camera intrinsic parameters obtained in the laboratory. Secondly, the Hungarian tracking algorithm is used to match camera-radar point pairs with over a larger range and the fine calibration matrix is obtained. Additionally, the validation criterion is established, which ensures the fine calibration can operate continuously and timely adjust when the device pose changes. To verify the efficiency of the proposed method, the real roadside experiments are conducted in the traffic-dense scenario. The results show that the purposed method reduces the reprojection error by 25% comparing to manual calibration, by 55% comparing to other automatic calibration method. This approach significantly enhances calibration accuracy and robustness in complex environments, it can provide reliable technical support for intelligent transportation systems.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27309-x.

Purpose This study adopts the multinational operational flexibility perspective to examine how the characteristics of the existing subsidiary portfolio influence the subsequent investment attributes of its subsidiaries. Specifically, we explore how the switching needs and opportunities associated with cross-border labor cost conditions influence a new subsidiary's location, product and ownership. Design/methodology/approach We employed a panel data analysis of Korean overseas manufacturing subsidiaries. We adopt binomial logit regression for three dependent variables, the last two nested within the first regression. Findings We find that higher labor cost uncertainty interacts with higher labor cost correlation among countries of existing subsidiaries, leading to a new subsidiary in a country with a negatively correlated labor cost. We also find that the locational choice is synchronized with a less localized product and higher ownership for global switchability. We additionally observe that this synchronization of investment attributes is more pronounced when managers are highly aware of switching options embedded in their international investments. Originality/value We examine the nested relationships of key investment decisions (location, product, ownership) from the multinational operational flexibility, which provides different explanations from other perspectives.

Due to global warming, temperate regions are increasingly experiencing heat waves, which negatively impact dairy cow welfare, productivity, and farm profitability. Ventilation systems (VSs) are a common heat mitigation strategy, despite their high initial investment cost. This study investigated the effects of heat stress (HS) and VSs on dairy cows’ milk yield and whether VSs guarantee an economic benefit for farmers. The trial involved four dairy farms over 3 years: 2 years before and one after VS installation. We conducted an observational within-farm pre-post study, using two pre-installation years and 0.5–1 post-installation year per farm. The following outcomes were analyzed at the herd level: daily milk yield, biweekly milk quality, and monthly reproductive metrics. The temperature-humidity index (THI) was calculated daily as a measure of HS and categorized as follows: comfort (<72), mild discomfort (72–79), discomfort (80–84), and alert (>84). Economic sustainability was assessed through partial budget analysis, accounting for additional feed, labor, and electricity costs. The presence of VSs was associated with a significant increase in cows’ milk yield across all THI conditions ( p < 0.001). Fat and protein contents decreased following VS installation, consistent with the observed increase in milk yield. However, their values were significantly lower under the most critical THI classes ( p < 0.001). Linear somatic cell count (SCC) scores were higher under the discomfort and alert THI classes in the absence of VSs, whereas they decreased slightly across THI classes when VSs were used ( p < 0.001). The duration of lactation, days open, and number of services per pregnancy reached their highest values under THI alert conditions in the absence of VSs and were significantly reduced following the implementation of VS ( p < 0.001). Increased milk income with VS use was €12.39/day/cow under mild discomfort, €12.23 under discomfort, and €12.08 under alert conditions. The results showed wide variability in economic outcomes across farms and THI classes. Although differences in VS management prevented a definitive conclusion regarding return on investment (ROI), the findings suggest positive effects on cows’ productivity and farm profitability. However, a definitive ROI could not be stated due to heterogeneity in fan size/spacing, cows-per-fan coverage, and farm-specific post-installation durations. Therefore, future cost–benefit analyses should consider additional factors to fully evaluate VS investments.

Large-scale farming is crucial to improving agricultural competitiveness and increasing farmers’ income. With the rapid development of the digital economy, applying digital technology in agricultural production is regarded as a new source of enhancing agricultural competitiveness. However, few studies have discussed the potential link between the application of digital production technology and large-scale farming from a micro-perspective. Based on survey data from rice farmers in Jiangsu Province, China, in 2023, this study uses an endogenous switching model to analyze the impact of digital production technology on large-scale farming and its potential mechanisms. The results show that applying digital production technology significantly promotes large-scale farming at both the farm and plot levels. These findings remain robust after a series of robustness tests, including changing estimation methods and redefining dependent variables. Mechanism analysis reveals that digital production technology promotes large-scale farming through three pathways: reducing labor costs, mitigating disaster risks, and enhancing managerial capabilities. The heterogeneity analysis indicates that the impact of digital production technology on large-scale farming is more pronounced among farmers with high digital literacy or those in regions with advanced digital finance. This study emphasizes that promoting digital production technology is feasible for achieving large-scale farming.

Despite the proliferation of artificially intelligent systems capable of social interaction, how and why social interaction influences users over time remains poorly understood. We draw on theories of technology adoption and research in affective computing, social psychology, and management to introduce the concept of human-AI relationships involving interdependence, temporality, and intensity. We develop the Relational Tradeoff Model, extending current theorizing on technology adoption by accounting for a critical third factor in addition to cognitive acceptance and behavioral use: human subjective well-being. The model reveals an important unexplored tradeoff in relationships with socially interactive AI: short-term acceptance and use gains but long-term subjective well-being costs for trust, psychological safety, and emotional labor, depending on AI social function and exacerbating and mitigating individual and relational factors. We discuss implications and suggestions for future exploration, including intrapersonal, interpersonal, and team relational dynamics and evolving expectations of AI in organizations.

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

ABSTRACT The issue of talent supply-demand mismatch remains challenging for the development of enterprise competitiveness, how to improve the quality of talent supply and optimize labour investment decisions has become a key issue. This paper explores the impact of industry-education integration (IEI) pilot on the labour investment efficiency of enterprises. The study finds that IEI significantly reduces inefficient labour investment, particularly by curbing over-employment. The underlying mechanisms include alleviating resource constraints, strengthening supervision and management, and promoting human capital upgrading. The cross-sectional analysis shows that IEI is more influential in state-owned enterprises, non-labour-intensive enterprises, enterprises with higher customer concentration and less market competition. Furthermore, IEI can reduce labour cost stickiness and optimize the labour income share within firms, contributing to national objectives of common prosperity. This paper not only enriches the existing literature but also provides practical guidance for advancing IEI initiatives, overcoming barriers to industry-education collaboration, and enhancing enterprise labour investment decisions.

ABSTRACT On the back of steadily rising labour costs, driving the servitisation of China's manufacturing industry holds significant importance. This study calculates highly refined indicators of enterprises' investment in servitisation and, in conjunction with data on minimum wages in counties and districts, examines how increasing labour costs affect enterprise investments in servitisation. The research reveals that the significant rise in labour costs notably boosts the level of enterprises' investment in servitisation. By decomposing the level of servitisation of enterprises' inputs into domestic and foreign parts, it is found that the increase in labour costs has mainly and significantly promoted the domestic level of servitisation of enterprises' inputs. This influence exhibits heterogeneity in terms of enterprise ownership, trade modes, regions, skilled labour, and factor intensity. From a mechanism perspective, the increase in labour costs primarily impacts enterprises' investment in servitisation by the endogenous development mechanism of enterprises and the collaborative mechanism of industrial chains among enterprises. Further studies indicate that the rise in labour costs drives various features in the transformation of enterprises' investment in servitisation, including substitution effects, strengthening effects of legal systems, and the impact of automation replacing human labour.

Over the past couple of decades, technology has become an integral part of our lives, with widespread digitalization and access to knowledge making our lives easier every day. What seemed like science fiction in the 20th century is now perceived as completely ordinary and mundane. One such example is unmanned aerial vehicles (UAVs). Today, UAVs are used in many areas: agriculture, construction, energy, and military applications. UAVs are a very promising field in geodesy and cartography. In vast and hard-to-reach areas, UAVs reduce time, money, and labor costs. Using specialized software, it is possible to create both highly accurate terrain maps and 3D models of buildings and structures. This article presents the experience of creating a 3D model of a residential building in the city of Yakutsk. Currently, with the possible introduction of a 3D real estate cadastre and information modeling technologies in construction, this research topic is highly relevant.

This paper addresses the challenges of dynamic environments and multimodal data fusion in multimodal transport path optimization for smart ports by proposing a GL-SSL Model that integrates Graph Neural Networks (GCN), Long Short-Term Memory (LSTM), and Self-Supervised Learning (SSL). The model fully exploits the graph-structured information of port transport networks and their temporal variations, while SSL enhances feature representation, enabling efficient optimization of path planning. Experiments were conducted on multiple public datasets, including AIS data from the Port of Rotterdam, global shipping data, and port net revenue data. Results show that the GL-SSL Model achieved significant improvements in key performance metrics. Specifically, the optimized path length reached 80 km, the transport cost was reduced to 200 cost-units (a composite metric reflecting fuel consumption, equipment wear, and labor cost), and the delay rate was maintained at 0.05 (5%), all of which are substantially better than traditional algorithms and other deep learning models. Furthermore, the model demonstrated stable performance under complex scenarios such as peak traffic, adverse weather, and equipment failures, with rapid convergence of training loss and strong robustness. These findings highlight the model’s adaptability and practical application potential. Overall, this work provides effective technical support for multimodal transport path optimization in smart ports and carries important theoretical significance and broad application prospects.

The study investigates the determinants of export performance in Pakistan's textile industry by analysing: (1) the direct effects of organizational ambidexterity, production performance and competitive intensity on export performance and (2) the mediating role of export strategy in the relationships between competitive intensity and export performance, as well as between production performance and export performance. Pakistan’s textile industry has remained stagnant on exports in the last five years and has not recorded positive growth as regional competitors like China, Bangladesh, Vietnam, and India continue to sustain and thrive on their exports. This paradox in relation to Pakistan’s advantage in cotton production and labour costs, along with an established industrial base signals strategic flaws impeding firms in the translation of manufacturing capabilities into export performance in a competitive global textile market. The study uses survey data on 87 mid-level and senior managers of export-oriented Pakistani textile manufacturing companies, analysed using PLS-SEM. Confirmatory factor analysis and path analysis were used to test hypothesized relationships. Production performance was found to be the most consistent predictor of export performance, followed by organizational ambidexterity. The export results were negatively affected by high levels of competition. Export strategy served as a powerful mediator in the competitive intensity-export performance and production performance-export performance relationships. The study finds that the difficulties with exports are not in production, but in strategic adaptation to competition. Companies require both operational excellence and adaptable export strategies and policymakers should nurture strategic capabilities rather than simply production. Export strategy is the key link that couples what firms are capable of doing internally with their response to competition in the external environment.

The health for hearts united dissemination Trial: Implementation costs to reduce cardiovascular risk in African Americans.

‘Columbia Star’ is a high-quality, thornless, trailing blackberry ( Rubus L. subgenus Rubus Watson) grown for the machine-harvested processed market in Oregon and Washington, USA. Because it is a relatively new cultivar, little is known about how to manage it best. Therefore, a new planting was established in Fall 2019 to study the impact of plant spacing (0.75 vs. 1.5 m), chemical primocane suppression (with or without), and primocane training [August training vs. “new-over-old” (unpruned)] on its growth, yield, fruit quality, machine-harvest efficiency, and labor requirements. In each treatment, primocanes were trained to a vertical two-wire trellis system the first year after planting. By the second year and each year afterward, a chemical suppressant was sprayed on the primocanes when they reached ∼0.3 m in length in late April or early May, and the planting was machine-harvested on four to six dates from late June to late July. When averaged across plant spacings, the combination of August training and primocane suppression resulted in more primocanes (15 canes/plant) than any combination of new-over-old training and/or no primocane suppression (averaged 12 canes/plant). New-over-old training, on the other hand, increased the number of fruit per lateral, depending on the year and plant spacing, and resulted in higher yields than August training in the final 2 years of the study (2023 and 2024). The plants also produced a higher yield when they were spaced at 0.75 m than at 1.5 m, but only during the first 2 years of fruit production. Primocane suppression had no effect on yield in any year, likely because it resulted in more canes per plant, which compensated for shorter cane lengths. Any treatment differences in fruit weight were small and were <0.2 g/fruit. However, total soluble solids in the fruit were nearly 1% less on average with new-over-old training, perhaps because a portion of the fruit were knocked off earlier during harvest than with August training. In terms of labor, new-over-old training required 87% less time than August training when the plants were spaced at 0.75 m, and 79% less time when they were spaced at 1.5 m. Primocane suppression reduced the time by an average of 7% and 13% with new-over-old and August training, respectively. After the first 3 years of fruit production, new-over-old training resulted in a cumulative net return of $52,875 and $49,006/ha when the plants were spaced at 0.75 and 1.5 m, respectively, when averaged across treatments with and without primocane suppression. August training, in comparison, resulted in a loss of $11,606/ha at 0.75 m and a return of only $7218/ha at 1.5 m. Across plant spacings, primocane suppression increased cumulative net returns by $7217/ha when the primocanes were trained in August, but only by $1067/ha when the primocanes were trained using the new-over-old method. Given economic and labor constraints and positive impact on yields, the new-over-old method is expected to become the industry standard very quickly .

Labor costs and domestic value added: Evidence based on the China’s new Labor Contract law

‘Mini Blues’ highbush blueberry (Vaccinium sp.) was released in 2016 as a high-quality, machine-harvestable cultivar for processed markets. A planting was established in Oct 2015 in western Oregon and an initial study, published in 2022, included the following pruning treatments, which began in Winter 2017–18: 1) conventional highbush pruning (HB); 2) removing one or two of the oldest canes per bush (Speed); 3) leaving plants to grow from 2017–21 (Unpruned); and 4) hedging after fruit harvest in 2018 (Hedge) and then unpruned afterward. Hedge plants were considered identical to Unpruned at the end of the initial study. In Winter 2021–22, this study began when Unpruned and Hedge treatment plants were renovated by cutting all canes back to a height of ∼30 cm and thinning to the strongest 8 to 10 canes per plant. In Winter 2022–23, half of the renovated plants were thinned (“cane thinning”), leaving 10 to 12 strong canes, and the other half were left unthinned. Pruning time, yield, berry weight, and total soluble solids (TSS) were measured from 2022 to 2024 to determine how quickly plants recovered from renovation and what the economic considerations are for this practice given the loss of yield in 2022 after renovation. Cane thinning in Winter 2022–23 reduced yield compared with all other treatments in 2023, but all treatments had similar yield by the third season after renovation (2024). Berry weight and TSS were similar among treatments, except Speed had smaller berries in 2023. Cane thinning required 331 h/ha compared with 44 h/ha for Unthinned (removing low growth) and 376 h/ha for HB in 2022–23. The highest post-renovation economic returns, as well as cumulative returns (2018–24) came from Speed pruning, followed by unpruned with renovation and no cane thinning. Cane thinning reduced yield for 1 year, cost more for labor, and showed no significant benefit in the 2 years since it was performed. ‘Mini Blues’ continues to perform well with reduced labor practices and does not need to be pruned using conventional techniques.

An observational study of the relationships between time from calving to pregnancy, body condition score, and lameness among dairy cows, using video data.