Internal Information Research Articles

Corporate marketing in modern hospitals and satisfaction level of corporate clients in Bangladesh

The study's abstract emphasizes the Marketing Department's dual responsibility in creating external awareness and disseminating internal information in modern hospitals. To improve service quality and management decision-making, the department keeps an eye on patient happiness and oversees communication, advertising, corporate agreements and feedback methods. Key research findings show that while corporate patients generally value the department's cutting-edge technology and the expertise of its staff, they do have reservations about the department's responsiveness and punctuality, particularly with regard to inpatient care. Patients in the Outpatient Department (OPD) expressed greater levels of satisfaction than those in the Inpatient Department (IPD), where patients expressed greater dissatisfaction with personalized care and error correction. To increase general patient happiness, recommendations include enhancing service speed, providing personnel training, and attending to particular patient requirements.

Analyzing existing approaches to measuring intellectual capital

Intellectual capital is an invisible but important resource for all companies in the turbulent digital environment, which is the driver of innovation development. The issues of assessing and analyzing intellectual capital are extremely relevant and have long occupied the minds of domestic scientists and top managers. Today there are no generally accepted methods of intellectual capital assessment, which would allow to give a more objective assessment of its constituent elements. The article presents the results of analyzing the existing approaches to measuring intellectual capital. The author concludes that companies need to carefully monitor their intellectual capital and focus on providing internal information about it in order to gain a competitive advantage in the market.

Information Stratification: An Understanding of Rural Governance in the New Media Era

Current research on media governance emphasizes the technological aspect of information as a tool but lacks sufficient attention to the digital transformation of information subjects. This paper aims to focus on the internal information characteristics of rural governance in the digital age, thus understanding the changes of rural governance subjects in the information society under new media contexts. Based on more than a decade of fieldwork in rural areas of Beijing conducted by the research team, this study employs in-depth interviews and participant observation in a multi-case study, exploring the structural representation of rural governance from an information perspective. The research reveals that in the new media era, rural society, under multiple governance bodies, generates differentiated information stratification, specifically manifesting as instrumental rational information in the formal governance layer, proxy implementation information in the semi-formal governance layer, and livelihood-adaptive information in the informal governance layer. Among these, the authority-driven governance information in the formal layer is the core part of the rural governance system. The subjects and structures of information under multiple governance bodies are interconnected and coexist within the internal field of rural governance, subsequently driving the centralization of formal governance information, the collaboration of semi-formal governance information, and the alienation of informal governance information. This leads to the emergence of orderly information stratification within the multi-layered governance structure. Information stratification deepens the explanatory pathway of multi-layered governance in rural social studies and proposes new perspectives for understanding rural governance issues in the new media era.

Cost and resource comparison analysis for THA in Switzerland and Austria.

Total hip arthroplasty (THA) is an orthopedic intervention that generates substantial costs to national healthcare systems due to the number of interventions and the cost per intervention. We performed a cost comparison analysis in Austria and Switzerland. Data from the national joint arthroplasty register in Switzerland and internal information from the national healthcare services in Austria and Switzerland were compared for patient demographics, interventional characteristics, and costs adjusted for inflation and purchasing power from 2015 to 2021. The average age for primary THA in Austria was from 67.4 to 67.8years with 55.9-57.2 percent female patients and from 68.5 to 69.3years with 52.4-53.8 percent female patients in Switzerland. The annual incidence rate for primary THA rose from 210.28/100k to 216.6/100k in Austria and from 212/100k to 250/100k in Switzerland. After correction for inflation, costs were -1.91 percent lower in Austria in 2021 than in 2015 and -2.57 percent lower in Switzerland. After correction for purchasing power, costs were higher in Austria. The average hospital stay after THA in Austria was reduced by 20 percent (11.7days/2015 vs. 9.4days/2021) and 25 percent in Switzerland (8.4days/2015 vs. 6.4days/2021). Revision rate was 2.5-3.2 percent in Austria and 2.8-3.2 percent in Switzerland. The patient population was comparable while patients undergoing primary THA in Austria stay longer in hospital and have relatively higher costs when adjusted for currency, purchasing power, and inflation. The use of standardized registers would be helpful to compare outcomes and costs.

Why hasty adoption of telework stick? Evidence from firms’ digitalization

ABSTRACT The pandemic, from 2020, was expected to be a catalyst for telework to permeate. Our study empirically examines the relationship between a firm’s digitalization and telework stickiness, utilizing the unique data from our firm survey conducted in Osaka, Japan. Our results show amongst the four digital technologies, online sharing of internal information impacts telework stickiness. This tendency is also true for firms that hastily adopted telework after the pandemic.

Ultraviolet radiation inhibits mitochondrial bioenergetics activity.

Mitochondria play an important role in cellular function, not only as a major site of adenosine triphosphate (ATP) production but also by regulating energy expenditure, apoptosis signaling, control of the cell cycle, cellular growth, cell differentiation, transportation of metabolites, and production of reactive oxygen species. Interaction with electromagnetic waves can lead to dysregulation or alterations in the patterns of energy activities in the mitochondria. Ultraviolet light (UV) can be found in sunlight and artificial sources, such as lamps. UV radiation can cause damage to DNA, proteins, and lipids. Besides that, UV radiation is largely used in microorganism disinfection. To establish possible alterations in mitochondrial bioenergetics, this study proposes to investigate the UV (at two distinct intervals) effects on isolated mitochondria from mice liver to obtain direct responses and selective permeability of the internal membrane information. UVA-371 and UVC-255 nm lamps were used to irradiate, at different doses varying from 22.5 to 756 mJ/cm2, isolated mitochondria samples. Mitochondrial respiration pathways were investigated by high-resolution respirometry, and possible mitochondrial membrane damages were evaluated by mitochondrial swelling by spectrophotometer analysis. UVC irradiation results (in the higher dose) indicate decrease in 75% of mitochondrial bioenergetics capacity, such as limitation of oxidative phosphorylation in 60% and increased energy dissipation in 30%. Mitochondrial swelling experiments (spectrophotometer) indicated inner membrane damage, and consequently a loss of selective permeability. Direct correlation between irradiation and effect responses was observed, mitochondrial bioenergetics is severely affected by UVC radiation, but (UVA) radiation did not present bioenergetic alterations. These alterations can contribute to improving the knowledge behind the cell death mechanism in disinfection UV light and UV therapy such as phototherapy.

Image segmentation of tunnel water leakage defects in complex environments using an improved Unet model

Computer vision technology provides an intelligent means for detecting tunnel water leakage areas. However, the accuracy of defect feature extraction and segmentation is limited by factors such as insufficient lighting and environmental interference inside tunnels. To address the problem, this paper proposes a tunnel water leakage area segmentation network model called Customized Side Guided-Unet (CSG-Unet), using Unet as the baseline model. The main contributions are: (1) To improve the accuracy of water leakage area extraction, a customized side guided term is introduced to direct the net’s attention to the changes in light and shade within the image. A parallel attention network module is designed to extract internal information from the guided term. Subsequently, a strengthened channel attention module aggregates the guided term and the original information to achieve accurate segmentation of water leakage areas; (2) To address the scarcity of tunnel water leakage area datasets, a basic dataset is constructed by collecting data from open-source datasets and manually gathered data in tunnels. On this basis, perspective transformation is used to change the camera viewpoint, gaussian noise is randomly added to the images in the dataset to simulate images taken in dimly lit scenes, thereby expanding the dataset and enhancing the network’s generalization. The CSG-Unet network was trained using the constructed training set, achieving a mean Intersection over Union (mi IoU) of 85.54%, a mean Dice coefficient (mi Dice) of 85.26%, and a mean Pixel Accuracy (mi PA) of 90.85%. Compared to its baseline network, U-Net (tiny), these metrics show an improvement of over 3.2% in each indicator. Finally, a visual comparison between the improved network and the baseline network further confirms that the proposed model can effectively adapt to the segmentation of water leakage areas in complex environments.

Do training and development, internal communication and information sharing, and job design boost the innovation of SMEs?

Do training and development, internal communication and information sharing, and job design boost the innovation of SMEs?

Giant magneto-optical Kerr effects governed by the quasi-bound states in the continuum

The magneto-optical Kerr effect (MOKE), as one of the magneto-optical effects, exhibits polarization change upon reflection that can be used to explore the internal information of magnetic materials with broad applications in modern information technology. However, typically, MOKE is quite weak due to the lower magneto-optical interaction. To tremendously enhance the MOKE, quasi-bound states in the continuum in a one-dimensional Ce- doped Y3Fe5O12 (CeYIG) film photonic crystal slabs (PCS) are proposed to improve the magneto-optical interaction in this work. A giant enhancement in the rotation angle and ellipticity of the longitudinal MOKE, which is about 93.4 and 136.8 times stronger than a pure uniform CeYIG, can be realized. Almost circularly polarized reflected beams with different chiralities are obtained with the CeYIG film. By tuning the geometric parameters of the PCS and the applied external magnetic field, dynamic control of polarization states of the reflected beams with different wavelengths can be realized. This magneto-optical metasurface provides a convenient way for the implementation of magneto-optical devices such as information memory devices, sensors, polarizers, and chiral devices.

Positive and Negative Retinotopic Codes in the Human Hippocampus.

The hippocampus is thought to coordinate sensory-mnemonic information streams in the brain, representing both the apex of the visual processing hierarchy and the central hub of mnemonic processing. Yet, the mechanisms underlying sensory-mnemonic interactions in the hippocampus are poorly understood. Recent work in cortex suggests that a retinotopic code - typically thought to be exclusive to visual areas - may help organize internal and external information at the cortical apex via opponent interactions. Here, we leverage high-resolution 7T functional MRI to test whether a bivalent retinotopic code structures activity within the human hippocampus and mediates hippocampal-cortical interactions. In seven densely-sampled individuals, we defined the retinotopic preferences of individual voxels within the hippocampus and cortex during a visual mapping task, as well as their functional connectivity during independent runs of resting-state fixation. Our findings reveal a robust retinotopic code in the hippocampus, characterized by stable population receptive fields (pRFs) with consistent preferred visual field locations across experimental runs. Notably, this retinotopic code is comprised of roughly equal proportions of positive and negative pRFs, aligning with the hypothesized role of negative pRFs in mnemonic processing. Finally, the signed amplitude of hippocampal pRFs predicts functional connectivity between retinotopic hippocampal and cortical voxels. Taken together, these results suggest that retinotopic coding may scaffold internal mnemonic and external sensory information processing within the hippocampus, and across hippocampal-cortical interactions.

Characterization Model Research on Deformation of Arch Dam Based on Correlation Analysis Using Monitoring Data

Deformation is the most direct indicator of structural state changes in arch dams. Therefore, numerous deformation monitoring points are typically arranged on arch dams to obtain deformation data from each point. Considering the complex relationships between the deformation at each monitoring point, this study focuses on the internal structural relationships and information fusion within the dam. The Pearson correlation coefficient is used as a similarity index to determine significant linear correlations between the measuring points. Ward’s cluster analysis method is then applied to group these points based on their similarities. To identify measuring points with strong nonlinear correlations, the Maximum Information Coefficient (MIC) method is employed. By integrating these linear and nonlinear correlations, a model is constructed to characterize the deformation at specific measurement points using data from strongly correlated points. The effectiveness of this model is verified through a concrete engineering case study, offering a novel approach for analyzing arch dam deformations.

SWOT Analysis of the Marketing Mix Strategy of Integrated Heart Services of Pku Muhammadiyah Gamping Hospital in the Era of National Health Insurance

The referral system in the National Health Insurance policy is a challenge for class B hospitals. This research aims to analyze the marketing mix strategy for Cardiac Vascular Center carried out by the PKU Muhammadiyah Gamping hospital in the National Health Insurance era. The research approach used is qualitative with a case study type of research. The subjects of this research are internal hospital informants and were selected based on purposive sampling criteria, namely those related to the marketing strategy of the hospital's superior services. Based on the results of the SWOT analysis, an alternative strategy was obtained, namely a growth oriented strategy. This strategy maximizes the strengths you have to take advantage of existing opportunities or can also be called an SO (strength-opportunity) strategy. The strategies resulting from the SWOT analysis are improving human resources, adding services, developing facilities and infrastructure, increasing promotions, collaborating, improving IT systems, providing HR training and studies, and providing appropriate remuneration. It is hoped that the results of this research can be used as a consideration for strategic decisions for Cardiac Vascular Center at PKU Muhammadiyah Gamping Hospital and for other researchers to make strategic priorities according to hospital needs.

The conductor model of consciousness, our neuromorphic twins, and the human-AI deal

AbstractCritics of Artificial Intelligence (AI) posit that artificial agents cannot achieve consciousness even in principle, because they lack certain necessary pre-conditions present in biological agents. Here we highlight arguments from a neuroscientific and neuromorphic engineering perspective as to why such a strict denial of consciousness in artificial agents is not compelling. Based on the construction of a co-evolving neuromorphic twin, we argue that the differences between a developing biological and artificial brain are not fundamental and are vanishing with progress in neuromorphic architecture designs mimicking the human blueprint. To characterise this blueprint, we propose the Conductor Model of Consciousness (CMoC) that builds on neuronal implementations of an external and internal world model, while gating and labelling information flows. An extended turing test lists functional and neuronal correlates of biological consciousness that are captured by the CMoC. These correlates provide the grounding for how biological or artificial agents learn to distinguish between sensory activity generated from outside or inside of the brain, how the perception of these activities can itself be learned, and how the information flow for learning an internal world model is orchestrated by a cortical meta-instance, which we call the conductor. Perception comes with the distinction of sensory and affective components, with the affective component linking to ethical questions that are inherent in our multidimensional model of consciousness. Recognizing the existence of a blueprint for a possible artificial consciousness encompasses functional, neuronal and ethical dimensions, begging the question: How should we behave towards agents that are akin to us in the inner workings of their brains? We sketch a human-AI deal, balancing the growing cognitive abilities of artificial agents, and the possibility to relieve them from suffering of negative affects, with a protection for the rights of humans.

Individual differences in emotion prediction and implications for social success.

The social world requires people to predict others' thoughts, feelings, and actions. People who successfully predict others' emotions experience significant social advantages. What makes a person good at predicting emotions? To predict others' future emotional states, a person must know how one emotion transitions to the next. People learn how emotions transition from at least two sources: (a) internal information, or one's own emotion experiences, and (b) external information, such as the social cues detected in a person's face. Across five studies collected between 2018 and 2020, we find evidence that both sources of information are related to accurate emotion prediction: individuals with atypical personal emotion transitions, difficulty understanding their own emotional experiences, and impaired emotion perception displayed impaired emotion prediction. This ability to predict others' emotions has real-world social implications. Individuals who make accurate emotion predictions have better relationships with their friends and communities and experience less loneliness. In contrast, disruptions in both internal and external information sources explain prediction inaccuracy in individuals with social difficulties, specifically with social communication difficulties common in autism spectrum disorder. These findings provide evidence that successful emotion prediction, which relies on the perception of accurate internal and external data about how emotions transition, may be key to social success. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Internal Information Quality and Corporate Social Responsibility Performance: An Analysis of CSR Strengths and CSR Concerns

ABSTRACT Our study investigates whether the quality of information available within firms promotes corporate social responsibility (CSR) performance. Using different measures of internal information quality, we find that internal information quality is positively associated with CSR strengths, which are discretionary in nature, and negatively associated with CSR concerns, which are compliance oriented. We find that the positive association between internal information quality and CSR strengths is stronger in settings with internal information asymmetries due to greater cash flow volatility, a larger number of employees, or a broader geographic footprint. We also find that the negative association between internal information quality and CSR concerns is stronger in compliance settings where there are internal information asymmetries linked to year-over-year increases in concerns. Our results suggest that organizational practices that improve a firm’s internal information environment will likely enhance CSR performance by promoting CSR strengths and diminishing CSR concerns. Data Sources: Data are available from the public sources cited in the text. JEL Classifications: M14; Q56; M41; D8.

TeKo: Text-Rich Graph Neural Networks With External Knowledge.

Graph neural networks (GNNs) have gained great prevalence in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants adopt a message-passing principle that obtains network representations by the attribute propagates along network topology, which however ignores the rich textual semantics (e.g., local word-sequence) that exist in numerous real-world networks. Existing methods for text-rich networks integrate textual semantics by mainly using internal information such as topics or phrases/words, which often suffer from an inability to comprehensively mine the textual semantics, limiting the reciprocal guidance between network structure and textual semantics. To address these problems, we present a novel text-rich GNN with external knowledge (TeKo), in order to make full use of both structural and textual information within text-rich networks. Specifically, we first present a flexible heterogeneous semantic network that integrates high-quality entities as well as interactions among documents and entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity descriptions, to gain a deeper insight into textual semantics. Furthermore, we devise a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn high-level network representations. Extensive experiments illustrate that TeKo achieves state-of-the-art performance on a variety of text-rich networks as well as a large-scale e-commerce searching dataset.

Internal Connectedness and Accrual Quality: Evidence from Employee LinkedIn Connections

SYNOPSIS We investigate the impact of employee connectedness on a firm’s accrual quality. Using data from the largest professional network, LinkedIn, we measure the degree to which employees are connected—which we label “internal connectedness”—and are therefore likely to interact and share information. We hypothesize and find that a firm’s internal connectedness increases its accrual quality and that the positive effect is stronger when external monitoring or internal formal monitoring is weaker or when accrual estimation is more difficult. That is, the effects of internal connectedness are stronger when greater benefits can be expected from enhanced informal internal information sharing. Our results provide insights for academics and practitioners in understanding the determinants of financial reporting quality and the crucial yet often neglected role of the internal information environment. Data Availability: Data are available from the sources cited in the text.

Who Am I? How Public Library Staff Manage Conflicting Information about the Self‐Concept

ABSTRACTThis poster describes how public library staff manage conflicting information about their identity. Public library staff completed audio diaries about times throughout the workday that made them feel frustrated or uncomfortable, revealing times when external and internal information about their self‐concept conflicted. Their responses were coded according to established conflict styles, revealing a dynamic approach to identity construction.

Drive sustainable business performance with humane entrepreneurship, knowledge management: the mediation of sustainable business advantage and sustainable business model innovation

PurposeThis study evaluates the important role of humane entrepreneurship (HE) on sustainable business advantage (SBA) and sustainable business performance (SBP) by exploring the mediating role of sustainable business model innovation (SBMI) under the influence of knowledge management (KM) in small and medium-sized enterprises (SMEs). Encouraging businesses to implement a combination of KM and HE facilitates the implementation of SBMI that brings value to businesses, communities and the environment in emerging economies.Design/methodology/approachThis study uses an empirical quantitative research method using primary information based on data collected by questionnaires. The study sample resulted in 424 valid responses (66.77% response rate). The target audience of the study is middle and senior managers in SMEs. This study aims to perform structural modeling analysis with the maximum likelihood estimates method to examine structural relationships.FindingsThis study found a positive relationship between KM and HE to SBMI. At the same time, SBMI plays an important role for businesses to achieve SBA and SBP. Similarly, a positive relationship was also found between HE to SBA and SBP. And SBA strongly promotes SMEs to achieve SBP. In addition, the mediating role of SBMI and SBA is clarified to have a positive impact between HE–SBP and SBMI–SBP. HE plays an important role in motivating businesses to innovate sustainable business models, making it easier to gain sustainable competitive advantages, thereby bringing sustainable business results.Practical implicationsThis research helps SMEs realize the importance of KM and HE in SBMI. At that time, SMEs take advantage of internal and external information sources and innovate their business models based on HE mechanisms. Applying HE mechanisms will allow businesses to innovate their business models by combining concerns between stakeholders and the social environment for sustainable development. Therefore, businesses will achieve their SBA toward SBP.Originality/valueThe present literature shows that there are few empirical studies on the mechanism mediating SBMI in the relationship between HE and SBP; and SBA in the link between SBMI and SBP. In addition, the impact of KM and HE on SBMI is also very important in the current context. The study's findings could have huge implications for entrepreneurs and senior managers in SMEs in emerging economies. Help them realize the importance of KM and HE for SBMI, thereby building strategic perspectives that help businesses achieve sustainable competitive advantages and increase SBP for businesses.

A hybrid deep learning-based short-term forecast model for ionospheric foF2 in East Asia region

The short-term forecast of the critical frequency of the ionospheric F2 layer (foF2) is particularly challenging due to its nonlinear and non-stationary characteristics. To improve the short-term forecast accuracy of foF2, we propose a short-term hybrid deep learning model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and long short-term memory (LSTM) algorithms. The proposed model leverages the ICEEMDAN decomposition algorithm to expand foF2 time series data into multi-dimensional space and utilizes the LSTM algorithm to preserve long-term data information, capturing detailed changes in foF2 parameters and obtaining internal information about foF2. The 2014 and 2017 foF2 data from four observation stations located in Mohe, Beijing, Jeju, and Kokubunji in the mid-latitudes of East Asia were utilized for modeling and 1-hour forecast analysis. The forecast values of the proposed model align closely with the measured data, exhibiting minimal fluctuation in forecast error and effectively tracking the changing trend of foF2. The average root mean square error (RMSE) across the four stations in 2014 is 0.40 MHz, with a relative RMSE (RRMSE) of 7.11 % and a coefficient of determination (R2) of 0.98. The average RMSE in 2017 is 0.43 MHz, RRMSE is 7.68 %, and R2 is 0.92. The proposed model reveals significant improvements in the forecast accuracy of the comparison model compared with the CCIR, URSI, NN, and LSTM models. Especially compared with the CCIR and URSI models in the International Reference Ionosphere (IRI), demonstrating superior real-time tracking capability for foF2 and effectively addressing the challenge of poor short-term forecast accuracy observed in the IRI model.