Branch Network Research Articles

Three Decades of Failed Bank Acquisitions

Using more than 30 years of data, we document that the acquisition of failed US commercial banks through FDIC-managed Purchase and Assumption (P&A) transactions leads to long-term improvements in the profitability and loan risk of the combined entity and has no detrimental effects on its capital adequacy. These results are generally stronger for transactions with greater potential for economies of scale and efficiency gains. Furthermore, geographic similarity in the branch network of the acquirer and the target marginally improves the profitability of the combined entity, while a greater business similarity between the merged banks has no effect on deal outcomes. Additional tests show that the presence of regulatory subsidies also improves the profitability of the combined entity. Finally, we find no support for theoretical predictions about the misallocation of failed bank assets in the presence of widespread failures in local markets. Our findings are important for the understanding of the consequences of bank resolution through assisted M&As.

Exploring best practices in smart public library services: a survey

Purpose This paper aims to the integration of smart cities and technologies has had a profound impact on public library services globally. However, debates continue about how public libraries are incorporating smart technologies and tools, and what this means for their role in smart city development. This paper explores how public libraries are evolving to meet community needs by providing both physical and digital services. Design/methodology/approach The purpose of this study is to assess the role of public libraries in the smart cities and how they are aligning toward development of smart concepts, technologies, services such as contributions to smart city objectives, community impact, the digital divide, unique projects and readiness for future smart city advancements. So, the authors explored 11 public library systems of cities across the world implementing smart city projects. Findings The authors found that all the library systems offer extensive print and digital collections, sustainable buildings and a well-connected network of branches. They provide self-checkout services, makerspaces equipped with 3D printing and coding workshops and advanced recording and video editing technologies. Furthermore, these libraries function as community centers, serving diverse groups including children, women, the elderly and marginalized communities. They function as third spaces that foster lifelong learning and digital literacy. Originality/value Although smart cities face substantial debate regarding their features, tools and feasibility, public libraries have adapted and redefined their roles to become influential public institutions. They now play a crucial part in supporting vibrant and dynamic communities and neighborhoods within smart cities.

Optimizing flotation froth image segmentation via parallel branch network and hybrid loss supervision

Flotation is a crucial technology for fine coal separation, and accurately acquiring bubble size information during the flotation process is essential for monitoring flotation conditions and achieving intelligent control. However, existing semantic segmentation models encountered issues with boundary disconnection when segmenting flotation bubbles, resulting in deviations between the extracted bubble sizes and their true values. To address the aforementioned challenges, a semantic segmentation model was proposed to maintain high-resolution feature maps throughout the network by designing a parallel branch network structure. Additionally, a ConvTranspose module was proposed to preserve the detailed feature information of images while gradually enhancing the resolution of feature maps. In the model training phase, a hybrid loss function combining pixel classification loss with shape similarity loss was proposed to alleviate the sample imbalance problem caused by the substantial difference in the number of pixels between bubble boundaries and the interior of bubbles. Moreover, since traditional semantic segmentation evaluation metrics, such as MIoU, lack a mechanism for measuring bubble boundary continuity and cannot effectively penalize the problem of boundary disconnection, this paper proposed a new evaluation method for assessing the segmentation performance of flotation froth images. To comprehensively evaluate the effectiveness of the proposed method, this paper conducted tests using flotation froth images collected from actual production processes. Compared with existing methods, the segmentation model proposed in this paper exhibited clear superiority in mitigating the problem of bubble boundary disconnection. The prediction error for the number of bubbles was 6.38 %, which is significantly better than other methods.

The sale of failed banks: The importance of their branch networks and of the acquirers’ financial strength

This paper investigates the pricing of insolvent banks in the U.S. that are sold under the purchase and assumption resolution method of the Federal Deposit Insurance Corporation (FDIC). We consider quarterly data for 444 acquisitions of insolvent U.S. banks between 2009 and 2016. We find that acquirers not only pay higher prices for insolvent banks with larger core deposits, as has been highlighted by the literature (and is consistent with the FDIC’s beliefs), but also for those banks with larger branch networks that are less dispersed geographically. When the acquirers bid (separately) for the assets of the insolvent banks, they place a positive value on the number of branches of the insolvent bank, but appear to be insensitive to geographic dispersion. Acquirers also pay more for banks with a national charter. The results additionally show that failed banks are most likely to be acquired by relatively large and highly capitalized banks whose organic growth is not affected in the years following the acquisition. Overall, our findings contribute to a better understanding of the implications of the purchase and assumption resolution method for the FDIC and for the banking industry.

Calculation of Distribution Network PV Hosting Capacity Considering Source–Load Uncertainty and Active Management

The access of a high proportion of photovoltaic (PV) will change the energy structure of the distribution network (DN), resulting in a series of safety operation risks. This paper proposes a two-stage PV hosting capacity (PVHC) calculation model to assess the maximum PVHC, considering the uncertainty and active management (AM). Firstly, we employ a robust optimization model to characterize the uncertainty of sources and loads in DN with PV and analyze the worst-case scenarios for PVHC. Subsequently, we construct a PVHC calculation model that takes into account AM, and convert the model into a mixed-integer second-order cone (MISOC) model using linearization techniques. Finally, we apply “heuristic optimization + CPLEX solver” to solve the model and introduce overvoltage and overcurrent indices to analyze the safety of the DN under PV limit access. Case studies are carried out on the IEEE 33-bus system and a practical case. Results show that (1) only the uncertainty that reduces the load or increases the output efficiency will affect PVHC; (2) for DN limited by overvoltage, AM can better improve PVHC; however, for DN limited by maximum transmission power, the effect of AM is low; (3) for most DN, SVC can improve PVHC, but the effect is modest. And network reconfiguration can significantly increase PVHC on the system with poor branch network, even reaching 150% of the original PVHC.

Physics-informed deep convolutional network for combined sea ice concentration and velocity prediction

Accurate prediction of short-term fluctuations in Arctic sea ice is important for safe use of Arctic shipping routes. While deep-learning models can improve the accuracy of sea-ice predictions, the accuracy and predictability of short-term sea-ice conditions are limited in most purely data-driven deep-learning models that consider a single aspect of sea ice, without considering the physical variation law between several sea ice factors. We propose dual-task prediction models for sea ice concentration (SIC) and sea ice velocity (SIV), and incorporate a loss function that simultaneously addresses dynamic constraints of SIC and SIV into each model, based on dynamics terms in the sea ice control equation. Comparative experiments are performed to determine which model structure best performs at predicting SIC and SIV. We report a dual-task branching structure to be more suitable for predicting SIC and SIV, and a post-decoder branch network structure to best predict SIC and SIV. Adding a sea ice dynamics equation to the loss function improves the model fit with dynamics law, improving the predictability and prediction accuracy of SIC and SIV.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

A multimodal multistream multilevel fusion network for finger joint angle estimation with hybrid sEMG and FMG sensing

Finger joint angle (FJA) estimation, as a dynamic and fine-grained decoding mode, can support intuitive and natural human–machine interactions. This study is pioneering work dedicated to achieving accurate and clinically friendly FJA estimation. In this context, a novel surface electromyography and force myography-based multimodal multistream multilevel fusion network (MMMFNet) is proposed. MMMFNet utilises gate-recurrent units (GRU) networks to capture spatial dependencies among features and adopts a multilevel fusion strategy to extract more comprehensive representations from information sources than a single-level fusion. A cross-stream interaction (CSI) block is added between the GRU networks to focus on important information in the prediction and optimise the learning process and representation capability by facilitating information interaction among branch networks. The experimental results suggest that the MMMFNet has higher estimation accuracy (RMSE: 6.225±0.276 vs. 6.953±0.282, R2: 0.879±0.019 vs 0.846±0.021), shorter training time (4.146±0.074 min vs. 16.536±0.430 min), and lower computational complexity (5.4×106 FLOPs vs 8.8×106 FLOPs) than state-of-the-art fusion methods. Moreover, MMMFNet, which quantifies feature importance using attention weights, is the first explainable multimodal model for hand movement intention decoding. In summary, compared with state-of-the-art methods, MMMFNet can not only estimate FJAs more accurately but is also more clinically friendly.

DEDBNet: DoG-enhanced dual-branch object detection network for remote sensing object detection

With the improvement of spatial resolution of remote sensing images, object detection of remote sensing images has gradually become a difficult task. Extracted object features are usually hidden in a large amount of interference information in the background due to the complexity and large area of backgrounds, as well as the multi-scale nature of objects in remote sensing images. Still, many existing background weakening methods face difficulties in practical applications and are prone to high rates of false positives and false negatives. Therefore, remote sensing object detection has become increasingly challenging. To address these challenges, a novel background weakening method called Difference of Gaussian (DoG) to weaken background (DWB) module is proposed. Then, we develop a dual-branch network, named DoG-Enhanced Dual-Branch Object Detection Network (DEDBNet) for Remote Sensing Object Detection. The base branch network is responsible for detecting objects, while the DWB's branch network corrects the detected objects using feature-level attention. To combine the features of these branches, we propose two new methods Self-Mutual-Correcter with Detect heads (SMCD) for corrective learning and Map Channel Attention (MCA) for channel attention. Self-Corrector (SC) enables modification and integration of features, while the Mutual-Corrector (MC) enhances the features and further fuses them. We evaluate our proposed network, DEDBNet, through extensive experiments on four public datasets (DOTA with an mAP of 0.836, DIOR with an mAP of 0.871, NWPU VHR-10 with an mAP of 0.973, and RSOD with an mAP of 0.975). The results demonstrate that our method outperforms other state-of-the-art object detection methods significantly for remote sensing images.

SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM).

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet's two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model's superior recognition precision and rapid convergence capabilities in complex fault environments.

A dual branch network combining detail information and color feature for remote sensing image dehazing

A dual branch network combining detail information and color feature for remote sensing image dehazing

An investigation of the predictors of credit risk among Ghanaian rural banks

This study investigated the predictors of credit risk in rural and community banks (RCBs), and the effects of rural banking in the Ashanti Region of Ghana. The study examined the correlation between credit risk as the dependent factor and a set of 8 systemic and unsystemic factors of credit risk as independent variables. The systemic variables were GDP growth rate, interest rate, inflation rate and unemployment rate. The unsystemic factors analyzed were branch network, total assets, return on assets and capital adequacy ratio. Secondary data was collected from 15 RCBs which were selected through purposive sampling, stratified random sampling and simple random sampling. Data collected was analyzed using Microsoft Excel and STATA software applications. Dataset ranged from the year 2008 to 2017. Descriptive statistical analysis, correlational analysis and regression analysis were undertaken. The study found that, return on assets, branch network and size were the significant unsystemic predictors of credit risk whilst interest rate, inflation and unemployment were the significant systemic predictors of credit risk in RCBs in Ghana. Furthermore, the study revealed that RCBs have had considerable positive impact on the communities where they operated. Managers of RCBs must consider pursuing a strategy of branch expansion and improving efficiency of staff because of the moderating effects of these factors on credit risk. This study contributed to the understanding of the relationship between unsystemic predictors of credit risk and systemic predictors of credit risk in RCBs, as intervening variables.

Key Determinants to Drive Motor Insurance Customer Acquisition in Western Province, Sri Lanka: A Focus on the Marketing Mix

Purpose: There is a significant need for a comprehensive insurance policy for all vehicle owners, especially considering the growing emphasis on health and safety, and public protection. The substantial preference for 3rd party insurance over comprehensive policy covers has created a hurdle for insurance companies, both in terms of acquiring new customers and retaining existing ones. This prevailing trend has presented a significant challenge to the growth potential of general insurance firms in the market. The main purpose of this study is to investigate how factors such as product, price, promotion, and distribution network influence the customer purchase intention towards motor insurance. Design/methodology/approach: This was a cross-sectional study using a deductive research approach. Data were gathered from 412 policy holders in Sri Lanka using a self-administered structured questionnaire. Multiple regression analysis was used to determine the most influencing factor among product, price, promotion and distribution network influencing customer purchase intention towards motor insurance. Findings: Based on the analysis, the insurance service [product], insurance premium [price], promotion and distribution network have a significant relationship with the customer purchase intention towards motor insurance policies in Sri Lanka. Among the factors tested, distribution network and insurance service have the most significant impact on customer purchase intention with reference to motor insurance segment. Originality: This study provides a unique contribution by highlighting the critical role of the distribution network, particularly the combination of Place and People elements, in driving motor insurance customer acquisition in Sri Lanka. Unlike prior studies that emphasize price and promotional strategies, this research focuses on the integrated influence of accessibility and personalized service. The findings demonstrate that a well-established distribution network outweighs traditional marketing factors in influencing purchase decisions, offering a new perspective on consumer behavior in the Sri Lankan motor insurance industry. Implications: The findings of this study will pave the way for a paradigm shift where, insurance firms could understand and develop relevant strategies to improve services provided to the existing and potential motor insurance customers. By analyzing what truly matters to Sri Lankan drivers, both insurance service providers as well as regulatory authorities can ally and develop a holistic insurance policy that comprehensively covers all aspects of vehicle ownership, filling the void in the current market. According to the findings, it can be advised that the insurance corporations should focus more on providing a product with more unique features and invest in creating a solid network of branches across the country which are fortified with skilled and service-oriented personnel who could persuade the customers in buying comprehensive insurance covers. Future researchers can also focus on income and educational level as moderators since both of which have an impact on consumer behavior as suggested in prior literature. Also, there is a possibility of widening their scopes by considering other types of insurance products and expand the geographical area as well.

ORGANIZATIONAL CULTURE - THE PATH TO A SUCCESSFUL CORPORATION

The essence of every successful business organization is reflected in a good organizational culture, particularly for organizations that operate through a network of branches and subsidiaries. The modern business environment is characterized by dynamism and uncertainty, making organizational culture one of the ways to develop and strengthen the uniqueness of successful operations. Through norms, customs, and an ingrained value system, an organization shapes the thinking and collaboration of its employees. This paper presents an analysis of the organizational culture of a bank branch, utilizing the Organizational Culture Assessment Instrument (OCAI) model for this purpose. Its mission is to provide an overview of the existing and preferred type of organizational culture. The goal is to identify and analyze the characteristics of the organizational culture within the bank's branch using the OCAI model and highlight elements that require further consideration or potential corrective measures.

Natural Processes in the Area of the Former Kakhovske Reservoir After the Destruction of the Kakhovka HPP

Abstract The results of the studies devoted to the consequences of the Kakhovka dam destruction on June 6, 2023 in the course of the Russia-Ukraine war are presented. After the accident, the Kakhovske reservoir, which was the largest by volume on the Dnipro River, practically disappeared. It has turned into the network of river branches and lakes that do not compare with the former reservoir. Currently, the conditions on the territory of the former reservoir have approached those observed in natural conditions. To some extent, they depend on the water discharge of the Dnipro River, more exactly, on the water discharge of the Dnipro HPP located upstream. In the conditions of large water runoff, the large part of the former Kakhovske reservoir is covered with water. An unusual natural phenomenon of the area of the former reservoir is very rapid overgrowth, primarily by willow. A year after the accident, its height in places which is rich in organic sediments exceeded 4 meters. At the same time, the size of the trees on the bottom covered by sands is much smaller. Today, a large area of the former reservoir looks like a dense forest.

The Impact of the Kakhovka Dam Destruction on the Water Temperature in the Lower Reaches of the Dnipro River and the Former Kakhovske Reservoir

Abstract The results of the studies devoted to the consequences of the Kakhovka dam destruction and the empting of the Kakhovske reservoir are presented. The water regime of the lower reaches of the Dnipro River was studied, in particular, the water temperature. The remote sensing data on spatial features of water temperature are given. Significant changes in water temperature are shown both in the lower reaches of the Dnipro River and in the former Kakhovske reservoir. In the first days after the destruction of the dam, due to the mixing, the surface water temperature at the mouth of the Dnipro River dropped significantly. Then the water temperature in the lower reaches of the river approached to that one observed before the destruction. In turn, the former Kakhovske reservoir became a network of river branches and lakes that cannot be compared with the former reservoir. The Dniprovske Reservoir, located upstream, affects the water temperature of this territory, especially the largest branch.

Adaptive Deep Neural Network for Click-Through Rate estimation

Over the past few years, deep learning networks have been applied in the estimation of the Click-Through Rate (CTR). The main task of the CTR model lies in predicting a user’s positive/negative response to an item. Most of existing CTR models are constituted of two parts: a deep neural network (DNN) and a wide model (e.g., deep cross network). The wide models are generally designed to learn the feature interaction and contain much more parameters than the DNN. As a consequence, it is quite difficult for existing CTR models to work on the resource-limited devices. Blindly removing the wide model will significantly encumber the predictive performance of the algorithm. In this paper, we adopt the knowledge distillation to train a single multi-branch network and assemble all branches as a teacher network. Each branch network is a simple DNN which not only matches the ground-truth label distribution but also aligns the prediction distribution of the teacher network. One of branch networks, termed as Adaptive Deep Neural Network (ADNN), is trained independently and further combined with a wide model to learn feature interactions. Our method does not require pre-training any high-capacity teacher models, which endows our method with higher efficiency compared with existing ones. The experimental results tested on Criteo and Avazu datasets show that the hybrid model outperforms state-of-the-art methods, and the light model ADNN also has a considerable performance accuracy over certain modern complex models, demonstrating the superiority of the methodology.

Research on microseismic signal identification through data fusion

The present study proposes a double-branch classification network, DPNet (Double Path Net), for the classification and identification of microseismic and blasting signals based on multimodal feature extraction. The vibration signals’ one-dimensional spectrogram and two-dimensional wavelet time–frequency graph are inputted into the double branch network. Subsequently, convolutional neural networks and ResNet are employed to extract the one-dimensional frequency features and two-dimensional time–frequency features of the vibration signals, respectively. Experimental results demonstrate that our proposed method achieves outstanding classification performance with an accuracy of 97.34% for microseismic signals and blasting signals. This research not only provides innovative solutions to practical problems but also introduces a novel idea of multimodal feature extraction at a theoretical level. By successfully applying it to efficiently classify complex signals in mining engineering, we offer a feasible solution that holds promising prospects for practical applications in this field.

Regional analysis of agricultural bank liquidity

PurposeThe purpose of this article is to determine the regional economic factors and bank characteristics that significantly contribute to changes in bank liquidity. We also seek to identify regions that may be most susceptible to liquidity tightening.Design/methodology/approachFor this article we use data on deposits from commercial banks, Federal Reserve survey data and indicators of regional and agricultural economic conditions. We specify a panel regression with fixed effects to model how liquidity at agricultural banks has changed and to identify the most significant drivers.FindingsOur results suggest that small banks and banks with branch networks located in areas more concentrated in agricultural production bear the greatest risk of reduced liquidity.Practical implicationsPrior to the pandemic and more recently, lower deposit growth, combined with strong demand for agricultural loans, has led to reductions in liquidity at agricultural banks. Lower liquidity could reduce credit availability for farm borrowers and increase risks for banks that must rely on alternative sources of funding to meet loan demand.Originality/valuePrevious research has shown that exogenous shocks from other economic sectors, such as energy, can significantly affect bank liquidity, but research is limited on how agricultural bank liquidity is affected by downturns in the agricultural economy and other regional economic factors. Another contribution is this paper’s analysis of regional disparities in bank liquidity.

Branch Networks, Client Networks, and Knowledge Sharing in Audit Firms

SYNOPSIS In large audit firms, interoffice knowledge sharing is embedded in two networks: the branch network, comprising all branch offices, and the client network, formed by similar engagements. Proximity between branch offices may enhance knowledge accessibility, whereas similarity between engagements could impact the relevance of that knowledge. I investigate how the two networks jointly influence the effectiveness of knowledge sharing. I construct client networks based on the similarity of clients’ product offerings and find that clients with greater centrality in the neighboring client network benefit more from knowledge sharing in terms of improved audit quality. I further show that this effect only exists when similar clients in neighboring offices are larger in size and is more salient when there is a gap in expertise between the current auditing office and neighboring offices. By enriching our understanding of interoffice knowledge sharing, these results offer practical insights to auditors, regulators, and audit-report users. Data Availability: Data are available from the data sources indicated in the paper. JEL Classifications: M42.