Real Area Research Articles

Characterization of contact evolution on sand grain surfaces and their time-dependent contact properties based on Greenwood and Williamson model

This paper focuses on the microscopic roughness of silica sand grain, and its morphological evolution (contact maturing) under a constant load. The surface topology with rich texture is obtained by Atomic Force Microscopy and various roughness parameters are investigated by statistical approaches. The surface profiles in the same contact region before and after being subjected to sustained loading are both measured to identify time-dependent morphological changes on the contact surface. The two congruent surface topologies, initial and mature surfaces, not only show the delayed fracturing of contact asperities, but also present quantitative differences in the spatial roughness parameters over time. These differences are intensified with consideration of upper profiles only, indicating that asperities with higher elevations are susceptible to the contact maturing process. With the geometric information obtained from the same region at two different measurement times, the Greenwood and Williamson asperity model of rough surface is used to estimate contact proximity, real area of the load-bearing contacts, and the number of contact spots. The model results of the post-loaded surface incorporated with true surface profiles show an increased number of contact points as well as the true contact area under constant loading. It is expected that subcritical fracturing of contact asperities leads to closer proximity of contact entities when loaded by prolonged loading, resulting in firmer and stiffer intergranular contact. This may very well be the reason for time effects in sand assemblies.

Scaling up laser polishing using a polygon scanner

AbstractSurface roughness has a significant influence on the performance and appearance of components. By combining a boost laser with ultrafast beam deflection using a polygon scanner, very high processing speeds can be achieved. A 65 % improvement in surface roughness has been achieved on stainless steel, with Sa < 0.1 μm, while the gloss has increased from 130 to 366 GU. As a result, the previously matt surface became reflective, improving the appearance of the material. A real area rate of 156 cm2/s was achieved, comparable with industrial process times.

Assessing the impact of urban expansion on carbon emission

Urban expansion is not only a driving force for economic growth and development but also a perilous element for anthropogenic carbon emission coinciding with population growth, urban expansion, land use pattern, urban sprawl. The study compares urban expansion and its impact on the carbon emission pattern for Rajshahi, a city of Bangladesh where land cover data is an important determinant. At first, the study aims to evaluate the pattern of urban expansion and the second step quantifies the carbon emission trend. The satellite images are used for land cover detection of urban expansion and Support Vector Machine (SVM) algorithm is applied for its classification. Similar carbon emission data are examined to relate carbon emission quantification rate by remote sensing algorithm. It indicates that urban expansion significantly explains carbon emission by comparing with similar secondary data to extract carbon emission and energy consumption. After accentuating urban expansion and carbon emission rate, the spatiotemporal pattern is analyzed in 16 different directions revealing that the urban expansion and emission trend expands to north-western and north-eastern directions of Rajshahi, turning it into a low carbon absorbed city. The responsible landmarks are real estate housing complex, educational institutions, student dormitories, industrial areas, brickfields and mango wholesale business. In order to understand the effects of uncontrolled urban expansion, this study will assist policymakers and urban planners in developing a comprehensive and well-organized strategy.

The Effect of Green Stormwater Infrastructures on Urban-Tier Human Thermal Comfort—A Case Study in High-Density Urban Blocks

Green stormwater infrastructure (GSI) is a key approach to greening and cooling high-density blocks. Previous studies have focused on the impact of a single GSI on thermal comfort on sunny days, ignoring rainwater’s role and GSI combinations. Therefore, based on measured data of a real urban area in Nanjing, China, this study utilized 45 single-GSI and combination simulation scenarios, as well as three local climate zone (LCZ) baseline scenarios to compare and analyze three high-density blocks within the city. Among the 32 simulations specifically conducted in LCZ1 and LCZ2, 2 of them were dedicated to baseline scenario simulations, whereas the remaining 30 simulations were evenly distributed across LCZ1 and LCZ2, with 15 simulations allocated to each zone. The physiological equivalent temperature (PET) was calculated using the ENVI-met specification to evaluate outdoor thermal comfort. The objective of this research was to determine the optimal GSI combinations for different LCZs, their impact on pedestrian thermal comfort, GSI response to rainwater, and the effect of GSI on pedestrian recreation areas. Results showed that GSI combinations are crucial for improving thermal comfort in compact high-rise and mid-rise areas, while a single GSI suffices in low-rise areas. In extreme heat, rainfall is vital for GSI’s effectiveness, and complex GSI can extend the thermal comfort improvement time following rainfall by more than 1 h. Adding shading and trees to GSI combinations maximizes thermal comfort in potential crowd activity areas, achieving up to 54.23% improvement. Future GSI construction in high-density blocks should focus on different combinations of GSI based on different LCZs, offering insights for GSI planning in Southeast Asia.

중국 청년들을 둘러싼 저층화 공간 서사의 전환과 재맥락화

This research examines how impoverished youth's perceptions of space, both real dwelling spaces and abstract social progress areas, are evolving. It examines in particular how the problems of'snail homes' and 'ant tribes', which were big difficulties connected to living spaces in the early 2010s, are continuing in what social context, and how disadvantaged youth are generating new narratives within this. They deploy narratives of 'disgust and exclusion' and'sympathy and empathy' towards poverty at the same time, eventually demonstrating new attitudes and actions in reaction to the state's narrative that has backed the development logic. Of course, their living conditions have not changed significantly, but the young are now learning to collect and recontextualize their own experiences and narratives in limited living spaces. Furthermore, in terms of social progress, they choose employment that provide stability in their lives rather than a single direction of travel focused in the city or the quest of tremendous achievement. Even people with strong academic credentials opt to go to the competitive perimeter, advance their jobs, and then return to the core city. The author believes that their psychological state is not entirely free of the stress of a competitive society and the narrative of competition, but that they are becoming psychologically stronger through lightness rather than previous heaviness, and cynicism and joy rather than sadness or despair. When reading impoverished youth's actions as signals of social transformation, it is assumed that the spectrum of interpretations will expand as we approach closer to future China.

The Environmental and Economic Importance of Mixed and Boundary Friction

One route to reducing global CO2 emissions is to improve the energy efficiency of machines. Even small improvements in efficiency can be valuable, especially in cases where an efficiency improvement can be realized over many millions of newly produced machines. For example, conventional passenger car combustion engines are being downsized (and also downspeeded). Increasingly, they are running on lower-viscosity engine lubricants (such as SAE 0W-20 or lower viscosity grades) and often also have stop–start systems fitted (to prevent engine idling when the vehicle is stopped). Some of these changes result in higher levels of mixed and boundary friction, and so accurate estimation of mixed/boundary friction losses is becoming of increased importance, for both estimating friction losses and wear volumes. Traditional approaches to estimating mixed/boundary friction, which employ real area of contact modelling, and assumptions such as the elastic deformation of asperities, are widely used, but recent experimental data suggest that some of these approaches underestimate mixed/boundary friction losses. In this paper, a discussion of the issues involved in reliably estimating mixed/boundary friction losses in machine elements is undertaken, highlighting where the key uncertainties lie. Mixed/boundary lubrication losses in passenger car and heavy-duty internal combustion engines are then estimated and compared with published data, and a detailed description of how friction is related to fuel consumption in these vehicles, on standard fuel economy driving cycles, is given. Knowing the amount of fuel needed to overcome mixed/boundary friction in these vehicles enables reliable estimates to be made of both the financial costs of mixed/boundary lubrication for today’s vehicles and their associated CO2 emissions, and annual estimates are reported to be approximately USD 290 billion with CO2 emissions of 480 million tonnes.

Comparisson between generalized geometric triangulation and odometry

The localization in mobile robotics is essential for carrying out autonomous tasks. For this reason, different algorithms have been developed to estimate the robot's pose, either relatively or absolutely. One of the best known is Wheel-based Odometry, which is easy to implement but the error tends to increase with respect to time producing an unreliable estimation. In contrast, absolute localization algorithms such as Generalized Geometric Triangulation (GGT) offer higher accuracy, although their implementation may require more advanced measurement systems, and pose estimation can be slow. This work compares these two algorithms and shows what happens with the pose estimation when used for period of time. The presented results were obtained in a real testing area equipped with a Turtlebot3 model burger robot.

Fine-grained spatiotemporal estimation of tourism flows leveraging cross-video collaborative perception

Microscale research on tourism flows is crucial for controlling such flows, analyzing resource-carrying capacity, and promoting sustainable development in the tourism industry. Current fine-grained monitoring of tourism flows using location-based big data faces challenges, such as inadequate user representation, data acquisition difficulties and spatiotemporal uncertainty. This study presents a method for the spatiotemporal modeling and estimation of regional tourism flows based on the collaborative perception of discrete surveillance videos. The method employed bridges the gap between physical and video image scenes by establishing collaborative perception relationships among multiple devices, thereby enabling the precise modeling and estimation of the dynamic spatiotemporal processes of population movement in the region. Empirical studies in real scenic areas confirm the adaptability of this technology to diverse geographical scenes and ensure the accuracy of the spatiotemporal flow estimation. This study addresses the challenges of high sampling costs and low spatiotemporal accuracy in regional fine-grained crowd estimation and offers technical support for near real-time dynamic crowd modeling and monitoring. The experimental results have the potential to assist in applications, such as tourism flow management, dynamic regulation and the risk analysis of group activities in scenic areas.

Clinical Observation of 10-mm Endoscopic Minimally Invasive Interlaminar Decompression in the Treatment of Ossified Lumbar Spinal Stenosis

This study aims to observe the safety and effectiveness of 10-mm endoscopic minimally invasive interlaminar decompression in the treatment of ossified lumbar spinal stenosis. The clinical data of 50 consecutive patients with ossified lumbar spinal stenosis were retrospectively analyzed. All patients underwent minimally invasive interlaminar decompression with 10-mm endoscope. Patient demographics, perioperative data, and clinical outcomes were recorded. Visual analog scale scores, Oswestry disability index scores, and modified Macnab criteria were used to assess clinical outcomes. The lateral recess angle, real spinal canal area, and effective intervertebral foramen area were used to assess the effect of decompression. The mean age of all patients was 59.0±12.3years. The mean operative time and intraoperative blood loss were 43.7±8.7minutes and <20ml, respectively. Two years after surgery, the leg pain Visual analog scale score decreased from 7.4±1.0 to 1.6±0.6 (P<0.05) and the Oswestry disability index score decreased from 63.8±7.6 to 21.7±3.4 (P<0.05). The lateral recess angle, real spinal canal area and effective intervertebral foramen area were significantly larger than before surgery (P<0.05). The overall excellent and good rate at the last follow-up was 92.0% according to the modified Macnab criteria. The 10-mm endoscopic minimally invasive interlaminar decompression can safely and effectively remove the ossification in the spinal canal and achieve adequate decompression in patients with ossified lumbar spinal stenosis.

ON THE PROBLEM OF EASTERN BORDER OF SAKA CULTURE OF ALAI

Introduction. The article is devoted to the problem of establishing the eastern border of the Saka culture of Alai. An important role in understanding of this problem is played by the Touyun burial ground, located in the southwestern part of Xinjiang, close to Kyrgyzstan. Materials and methods. This burial ground has specific funeral rites, which demonstrate the characteristic features peculiar to the Saka culture of Alai. In particular, these are burials in stone boxes, crypt-like stone structures and at the level of the ancient surface, constructed under low stone-earth burial mounds. Burials in them were both single and multi-act. The buried people in these burials were in an elongated position with orientation of the sculls to the west and northwest direction. The funeral inventory from Touyun was relatively poor and represented by a couple of ceramic vessels, jewelry items (earring, bracelet, and beads), a belt garment (belt buckle), household items (sumac, awls) and clothing items (plaques). Results and discussion. These funeral objects, based on analogies, allowed us to establish the time of existence of the Touyun burial ground, which fits into the chronological framework of the end of the 5th - 3rd centuries BC. It is evident, that this necropolis appears as a result of the migration of the Saka population from the eastern part of the Alai Valley. The occupying this territory group of Alai Saka was appeared in the contact zone with the population of the Saka culture of Tien Shan region, with which, apparently, they established marital ties. Currently, the Touyun burial ground is the easternmost site of the Saka culture of Alai, which makes it possible to expand the border of this culture by more than 130 km to the east than previously thought. It also made it possible to significantly clarify the real areas of the main archaeological cultures in the southwestern part of Xinjiang such as the Saka cultures of Alai, Pamir and Tien Shan region.

Integrated Modeling of Coastal Processes Driven by an Advanced Mild Slope Wave Model

Numerical modeling of wave transformation, hydrodynamics, and morphodynamics in coastal regions holds paramount significance for combating coastal erosion by evaluating and optimizing various coastal protection structures. This study aims to present an integration of numerical models to accurately simulate the coastal processes with the presence of coastal and harbor structures. Specifically, integrated modeling employs an advanced mild slope model as the main driver, which is capable of describing all the wave transformation phenomena, including wave reflection. This model provides radiation stresses as inputs to a hydrodynamic model based on Reynolds-averaged Navier–Stokes equations to simulate nearshore currents. Ultimately, these models feed an additional model that can simulate longshore sediment transport and bed level changes. The models are validated against experimental measurements, including energy dissipation due to bottom friction and wave breaking; combined refraction, diffraction, and breaking over a submerged shoal; wave transformation and wave-generated currents over submerged breakwaters; and wave, currents, and sediment transport fields over a varying bathymetry. The models exhibit satisfactory performance in simulating all considered cases, establishing them as efficient and reliable integrated tools for engineering applications in real coastal areas. Moreover, leveraging the validated models, a numerical investigation is undertaken to assess the effects of wave reflection on a seawall on coastal processes for two ideal beach configurations—one with a steeper slope of 1:10 and another with a milder slope of 1:50. The numerical investigation reveals that the presence of reflected waves, particularly in milder bed slopes, significantly influences sediment transport, emphasizing the importance of employing a wave model that takes into account wave reflection as the primary driver for integrated modeling of coastal processes.

AFDet: alignment and focusing for aerial object detection

ABSTRACT Object detection in aerial images has become a focus in recent years due to the expansion of its application fields. Aerial objects have the characteristics of multi-scale, arbitrary angle and dense arrangement, which brings considerable challenges to the task. Based on the efficient one-stage detectors, most methods design modules to adaptively align the feature receptive field with the anchors to improve the accuracy of the bounding box regression and calculate loss according to parameter bias. Current methods are not adequately data-driven in feature optimization and the angle parameter regression faces boundary problems. To handle these problems, in this letter, we propose an Align-Focus detector (AFDet) for aerial image object detection. We introduce the ideology of deformable multi-head self-attention to feature optimization and design a new Align-Focus Module (AFM) which can sensitively focus feature encoding response to the real texture area of the objects. In addition, we apply KF-IOU Loss to solve the boundary problem. We conduct necessary experiments on DOTA and HRSC2016 datasets, and AFDet achieves the highest mAP performance compared to the existing methods.

Analysis of the Factors Influencing Housing Prices in Yunnan and Guizhou Based on Linear Regression

The real estate industry contributes significantly to the national economy. In reality, it has a significant impact on the lives of residents. It is of great significance to study the factors that affect the housing price for the development of the real estate industry. Based on the real estate data of Yunnan Province and Guizhou Province from January 2012 to December 2022, this study will analyze the factors influencing the housing prices of Yunnan Province and Guizhou Province. In addition to SPSS research, multiple regression analysis will be used in this paper. According to the analysis, housing prices in Yunnan and Guizhou provinces will rise with the decline of long-term loan interest rates. Besides, the residential consumer price index negatively affects housing prices in Yunnan and Guizhou provinces. Moreover, an increase in the amount of real estate investment will lead to an increase in housing prices. On this basis, the completed real estate area has a negative relationship with house prices.

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

The Shear wave (S-wave) velocity is an essential parameter in reservoir characterization and evaluation, fluid identification, and prestack inversion. However, the cost of obtaining S-wave velocities directly from dipole acoustic logging is relatively high. At the same time, conventional data-driven S-wave velocity prediction methods exhibit several limitations, such as poor accuracy and generalization of empirical formulas, inadequate exploration of logging curve patterns of traditional fully connected neural networks, and gradient explosion and gradient vanishing problems of recurrent neural networks (RNNs). In this study, we present a reliable and low-cost deep learning (DL) approach for S-wave velocity prediction from real logging data to facilitate the solution of these problems. We designed a new network sensitive to depth sequence logging data using conventional neural networks. The new network is composed of one-dimensional (1D) convolutional, bidirectional long short-term memory (BiLSTM), attention, and fully connected layers. First, the network extracts the local features of the logging curves using a 1D convolutional layer, and then extracts the long-term sequence features of the logging curves using the BiLSTM layer, while adding an attention layer behind the BiLSTM network to further highlight the features that are more significant for S-wave velocity prediction and minimize the influence of other features to improve the accuracy of S-wave velocity prediction. Afterward, the nonlinear mapping relationship between logging data and S-wave velocity is established using several fully connected layers. We applied the new network to real field data and compared its performance with three traditional methods, including a long short-term memory (LSTM) network, a back-propagation neural network (BPNN), and an empirical formula. The performance of the four methods was quantified in terms of their coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The new network exhibited better performance and generalization ability, with R2 greater than 0.95 (0.9546, 0.9752, and 0.9680, respectively), RMSE less than 57 m/s (56.29, 23.18, and 30.17 m/s, respectively), and MAE less than 35 m/s (34.68, 16.49, and 21.47 m/s, respectively) for the three wells. The test results demonstrate the efficacy of the proposed approach, which has the potential to be widely applied in real areas where S-wave velocity logging data are not available. Furthermore, the findings of this study can help for a better understanding of the superiority of deep learning schemes and attention mechanisms for logging parameter prediction.

New Progress in Intelligent Picking: Online Detection of Apple Maturity and Fruit Diameter Based on Machine Vision

In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples’ maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model’s multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry.

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

Spider-Net: High-resolution multi-scale attention network with full-attention decoder for tumor segmentation in kidney, liver and pancreas

The abdominal tumor is a general term for tumors in kidney, liver and pancreas. Accurate segmentation of abdominal tumors is essential for their treatment. However, the varying shapes and sizes of abdominal organs result in significant differences in tumor regions. Existing convolution neural networks (CNNs) can only accurately segment individual abdominal tumors, lacking sufficient generalizability. We aim to design a network that can achieve good segmentation results for different abdominal tumors. To this end, a Spider-net to segment tumors is presented in this paper, which consists of a high-resolution multi-scale attention encoder and a full-attention decoder. Additionally, scale attention that integrates channel attention and spatial attention is designed for generating output. We have also designed a classification branch to distinguish whether the segmented region is a real tumor area or another benign lesion. We train and evaluate the Spider-net on three different organs: the kidney, pancreas, and liver. Spider-net achieves state-of-the-art results compared to methods that only use CNNs or transformers. Code are available at https://github.com/h2440222798/HRMA.

Construction of knowledge constraints: a case study of 3D structural modeling

The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human–machine collaboration and combines structural interpretation to form a new model of complex structural modeling guided by knowledge. Specifically, we focus on utilizing knowledge rule reasoning technology to extract topological semantic knowledge from interpretive data and employ knowledge inference to derive structural constraint information from complex geological structure models, thus effectively constraining the 3D geological structure modeling process. To achieve this, we develop a rule-based knowledge inference system that derives theoretical models consistent with expert cognition from interpretive data and prior knowledge. Additionally, we represent the extracted knowledge as a topological semantic knowledge graph, which facilitates computer recognition and allows estimation of intersection lines during 3D geological modeling, resulting in the creation of accurate models. The applicability of our proposed method to various complex geological structures is validated through application tests using real-world data. Furthermore, our method effectively supports the realization of intelligent structure modeling in real working area.

Modeling NO2 concentrations in real urban areas using computational fluid dynamics: A comparative analysis of methods to assess NO2 concentrations from NOx dispersion results

Major cities worldwide constantly deal with health hazards caused by air pollution. Modeling this pollution on an urban scale is essential for assessing the impact of local policies and promoting sustainable urban development. However, there are practical difficulties when using microscale modeling in applied context, and particularly for nitrogen dioxide modeling (NO2). In this study, a Computational Fluid Dynamics (CFD) model was employed to assess monthly NO2 concentrations in Antwerp, Belgium, and the results were compared to a one-month measurement campaign using 73 passive samplers. The result showed that using CFD with conventional assumption – such as neutral atmospheric stability consideration and using a turbulent Schmidt number (Sct) set to 0.7 – yield satisfying results according to air quality model acceptance criteria. Optimal outcomes were achieved by considering NO2 background concentration instead of NOx and employing Bachlin et al.’s empirical function to convert modeled NOx concentrations to NO2, dismissing the need for straightforward chemical mechanisms – such as photostationary steady-state equilibrium (PSS) –, or more expensive models in terms of computing resources. This approach yielded an overall error of less than 15 % and a correlation coefficient R of 0.78, affirming its effectiveness in modeling NO2 air quality in applied context.

Perceived effects of key audit matters reporting on audit efforts, audit fees, audit quality, and audit report transparency: stakeholders’ perspectives

PurposeThis paper aims to examine the perspectives of auditors, regulators and financial report preparers on the effects of key audit matters (KAMs) reporting on audit effort, fees, quality and report transparency.Design/methodology/approachThe authors conducted 21 semi-structured interviews with stakeholders (13 Audit Partners, 5 Chief Financial Officers and 3 regulators) and thematically analysed the interviews. They use the frame of “Paradox of Transparency” to explain the findings.FindingsAuditors perceive that the overall quality control of their audits has improved both in the planning and execution stages, and such improvement can mostly be attributed to the coercive pressures from professional bodies and regulators. Nevertheless, audit fee remains unchanged. Auditors disclose industry generic items and descriptions of KAMs, sometimes masking the real problem areas of the clients. Even after improving the performative audit quality, transparency of audit reporting has not improved. Issues that warrant going concern qualifications or audit report modifications are now reported as KAMs. Hence, KAMs reporting might make the audit report less transparent.Practical implicationsLocalised audit environments and institutions affect the transparency of KAMs reporting. Without attention to corporate governance and auditors’ independence issues, paradoxically, performative improvement in audit quality (due to the KAMs reporting requirement) does not enhance the transparency of audit reports.Originality/valueTo the best of the authors’ knowledge, this study is the first to provide field-level evidence in Bangladesh and other developing countries about the perceptions of auditors, financial report preparers and regulators on the effects of KAMs reporting on audit efforts, fees, quality and report transparency.