Central Server Research Articles

Viewing hospitality from an interdisciplinary lens: a systematic review of 30-year research on hospitality in health care

Purpose This study aims to examine the conceptual and empirical operations of hospitality at its intersections with health care, which includes medical and senior care. Design/methodology/approach This study conducts a systematic review of literature on hospitality in health care published in hospitality, tourism and leisure journals spanning from 1990 to 2023. A total of 50 studies meeting the inclusion criteria are reviewed, providing insights into how hospitality is conceptualized, its practical implementation and the proposed outcomes in health-care settings. Findings Hospitality in health care is conceptualized by hospitality scholars in three main ways: as service functions, as a service exchange and as an organizational culture. There is a significant overlap between the notion of hospitality and the concept of person-centered care in gerontology and health-care literature. Also, hospitality contributes positively to patient/resident experiences, organizational performance and societal impacts. Research limitations/implications The study is limited by its focus solely on the theoretical and practical aspects of hospitality in health care within hospitality, tourism and leisure journals, excluding relevant literature from gerontological and health-care journals. Originality/value Interdisciplinary research requires scholars from different disciplines to develop a common language and understanding of key concepts. This study presents the conceptual and practical domains of hospitality and its relevancy to health-care research and offers future directions to strengthen the interdisciplinary research between hospitality, health care and gerontology.

IMPLEMENTATION OF MULTI-NODE SENSOR DATA DELIVERY USING THE MASTER-SLAVE METHOD IN LORA COMMUNICATION

This research explains the application of sending data from various sensor nodes using the master-slave method in Long Range (LoRa) communication. This system was created to increase efficiency and reliability in collecting sensor data spread across several locations. Sensor nodes function as slaves that collect and send data to the master. The master then processes and combines the data before sending it to a central server. Experimental results show that this method is successful in reducing latency and increasing data transmission speed and shows great potential for Internet of Things (IoT) applications that require wide communication range and low power consumption.

FedCCL: Federated dual-clustered feature contrast under domain heterogeneity

Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent research is limited to simply using averaged signals as a form of regularization and only focusing on one aspect of these non-IID challenges. Given these limitations, this paper clarifies these two non-IID challenges and attempts to introduce cluster representation to address them from both local and global perspectives. Specifically, we propose a dual-clustered feature contrast-based FL framework with dual focuses. First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity. Then, we facilitate cross-client knowledge sharing by pulling the local representation closer to clusters shared by clients with similar semantics while pushing them away from clusters with dissimilar semantics. Second, since the sizes of local clusters belonging to the same class may differ for each client, we further utilize clustering on the global side and conduct averaging to create a consistent global signal for guiding each local training in a contrastive manner. Experimental results on multiple datasets demonstrate that our proposal achieves comparable or superior performance gain under intra-domain and inter-domain heterogeneity.

Communication-Efficient Wireless Traffic Prediction with Federated Learning

Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression scheme and an adaptive aggregation strategy for wireless traffic prediction. FedCE achieves gradient compression through top-K sparsification and can largely relieve the communication burdens between local clients and the central server, making it communication-efficient. An adaptive aggregation strategy is designed by quantifying the different contributions of local models to the global model, making FedCE aware of spatial dependencies among various local clients. We validate the effectiveness of FedCE on two real-world datasets. The results demonstrate that FedCE can improve prediction accuracy by approximately 27% with only 20% of communications in the baseline method.

Some aspects of consıderable models of oıl and gas ındustry development

The article examines some assumptions and problems of the world economic theory, it is pointed out that these assumptions are based on the characteristics of the economic structure in different parts of the world where the distribution of world production and the global exchange of goods, services and other results of human activity take place, of course the oil and gas industry is not left out in this matter shown. Moreover, taking into account the importance of its impact on world politics, it has been pointed out that the field is one of the most relevant objects of economic analysis today. The characteristic features of modern globalization are given in the article. In such a situation, it was suggested that since the real scale of the application of methods that increase oil production in Azerbaijan is not able to prevent the deterioration of the structure of reserves, it will not be possible to make a big positive change in the exploitation of extractable reserves. The article also discusses the application of econometric models in economic research on the oil and gas industry. As a means of research and prediction of natural processes, the mathematical modeling method has been shown to be very widespread in oil and gas extraction. The observations made were summarized and it was shown that there is a systematic progress in the dynamics of the world economic system in the conditions of modern globalization, the main characteristic features of modern globalization were classified and given by the author, the creation of optimal conditions for the operation of the oil and gas industry as a result of the administrative and fiscal regulation of the state in the conditions of globalization was noted, and also the oil and gas industry some aspects of the theoretical models of the future development of the field have been studied and shown, taking into account its specific characteristics and in the context of the natural decrease of the volume of production. In the article, it was determined that one of the most important issues is the correct determination of the oil and gas reserves of the field, its calculation by separate reserve categories. Experience shows that it is very important to calculate these resources in different ways, to analyze the indicators of field development in detail, and to compare them with real indicators in order to implement one or another project indicators. The basis of these methods is the determination of reserves and their role in the technical-economic justification of the options for field development. This gives special relevance to such issues in the oil and gas industry and considers their constant improvement in the conditions of the market economy appropriate. The developed article is dedicated to the study of such issues.

CESA: Communication efficient secure aggregation scheme via sparse graph in federated learning

As a distributed learning paradigm, federated learning can be effectively applied to the decentralized system since it can resolve the “data island” problem. However, it is also vulnerable to serious privacy breaches. Although existing secure aggregation technique can address privacy concerns, they also incur significant additional computation and communication costs. To address these challenges, this paper offers a Communication Efficient Secure Aggregation scheme. Firstly, the central server uses the communication delay between terminals as the weight of the fully terminal-connected graph to transform it into a sparse connected graph based on the minimal spanning tree. Secondly, instead of relying on central server for key advertisement, the terminals advertise keys via a neighboring terminal forwarding approach based on sparsely graph. Thirdly, we propose using the central server for auxiliary advertising to address unexpected terminal dropout. Simultaneously, we theoretically demonstrate our scheme’s security and have lower computation and communication costs. Experiments show that CESA can reduce the running time by 28.2% without sacrificing security and model accuracy compared to conventional secure aggregation when there are 10 terminals in the system.

Training-free approach to constructing ensemble of local experts

Conventional machine learning methods assume that a central server holds the entire training dataset for training a global model. However, in many real-world situations, datasets are collected from individual local nodes in distributed environments. Gathering these datasets often poses challenges owing to various practical constraints such as data security and privacy issues, storage limits, and transmission costs. When data distributions differ among local nodes, local experts trained on individual local nodes exhibit poor generalization performance for new instances. In this study, we propose a training-free method for constructing an ensemble of local experts without accessing datasets. For a query instance, we apply uncertainty quantification and out-of-distribution (OOD) detection to calculate the uncertainty and OOD scores of the predictions from individual models. We then determine the weights of the models by assigning a lower weight to those with higher uncertainty and OOD scores. Using these weights, the predictions of the individual models are aggregated to obtain a final prediction. In contrast to existing methods that impose specific application requirements, the proposed method can be applied in situations in which only individual predictions from local models are available for use when making a prediction for a query instance. We evaluate the effectiveness of the proposed method using image classification benchmark datasets.

ATHENA-FL: Avoiding Statistical Heterogeneity with One-versus-All in Federated Learning

Federated learning (FL) is a distributed approach to train machine learning models without disclosing private data from participating clients to a central server. Nevertheless, FL training struggles to converge when clients have distinct data distributions, which leads to an increased training time and model prediction error. We propose ATHENA-FL, a federated learning system that considers clients with heterogeneous data distributions to generate accurate models in fewer training epochs than state-of-the-art approaches. ATHENA-FL reduces communication costs, providing an additional positive aspect for resource-constrained scenarios. ATHENA-FL mitigates data heterogeneity by introducing a preliminary step before training that clusters clients with similar data distribution. To handle that, we use the weights of a locally trained neural network used as a probe. The proposed system also uses the one-versus-all model to train one binary detector for each class in the cluster. Thus, clients can compose complex models combining multiple detectors. These detectors are shared with all participants through the system's database. We evaluate the clustering procedure using different layers from the neural network and verify that the last layer is sufficient to cluster the clients efficiently. The experiments show that using the last layer as input for the clustering algorithm transmits 99.68% fewer bytes to generate clusters compared to using all the neural network weights. Finally, our results show that ATHENA-FL correctly identifies samples, achieving up to 10.9% higher accuracy than traditional training. Furthermore, ATHENA-FL achieves lower training communication costs compared with MobileNet architecture, reducing the number of transmitted bytes between 25% and 97% across evaluated scenarios.

A fuel cell powered rack prototype for data center application with waste heat recovery: design consideration and experimental verification

The combination of data center and PEMFC has been a promising data center zero-carbon emission solution. However, the relevant experimental studies are very rare and most work only solved the simple electrical coupling. This paper proposed a new in-rack data center servers and PEMFC coupling design scheme considering both the electrical coupling and thermal management coupling of the servers and PEMFC, and built a cabinet prototype for the first time. The complete multi-condition continuous experiment on the PEMFC system fully demonstrates the servers power supply stability of the prototype with the fast tested power response capability of 4.9 kW in 0.1 s. The experiments under rated condition (when the servers power is 5 kW) were conducted and the carbon emission level and efficiency performance of the prototype are analyzed. Under the rated condition, the highest prototype CHP efficiency is 64.1 % while the prototype can provide 41.6 ℃ hot water with 3.04 L·min-1 and the lowest prototype CHP efficiency is 59.8 % while the prototype can provide 61.1 ℃ hot water with 1.33 L·min-1. The prototype maintains the zero-carbon (or negative carbon) emission of servers. The average operation carbon emission rate is -35 g·h-1. Compared with the full grid supporting data center, the power average prototype operation carbon reduction rate is 0.13 g·s-1·kW-1. The application potential of the proposed data center and PEMFC coupling scheme is demonstrated with the prototype experiment results from the advantage of carbon emission and heat recovery potential.

Translating potential germline findings from tumour profiling into routine clinical care.

105 Background: Tumor molecular profiling (TMP) provides an opportunity to identify heritable cancer predisposition. Molecular Screening and Therapeutics Study (MoST) (8000 patients, 2016-23) and Cancer Screening Program (CaSP) (23000 patients, 2023-25) are Australian research programs providing TMP for therapy matching in advanced cancer. Matched germline testing is resource-inefficient and is not performed . An integrated approach between TMP and cancer genetics is needed to improve familial cancer outcomes through prevention and early detection. Methods: In MoST, potential germline variants (PGV) in actionable cancer genes were investigated in blood DNA in a research setting. Positive cases were informed, and a genetics review for clinical testing recommended. In CaSP, germline follow-up recommendations are included in the Molecular Oncology Board (MOB) Report based on decision algorithms to inform clinicians of PGV needing clinical assessment. Cancer genetics clinics in Australia were surveyed regarding preferences for the return of genetic information from tumor profiling. A centralised precision care clinic was launched in March 2024 to investigate the implementation of returning PGV information from tumor-only profiling. Results: In MoST, 38% (140/367) of PGVs tested were positive in blood DNA. Of these, 44% were identified in the highest actionability genes: BRCA1, BRCA2, PALB2, Lynch Syndrome. The validation and return of results process was unsatisfactory; it was resource-heavy, timeframes were extended, DNA was sometimes unavailable, and communicating information to non-participating relatives of deceased participants was challenging due to a lack of established pathways. The systems design in CaSP has allowed the study to manage higher volume without a net increase in germline workforce. To date, 2372 patients have completed profiling with 173 MOB recommendations for PGV validations. Fifty-four cancer genetics staff nationally responded to the survey. Many (61%) were concerned about the increased uptake of TMP without a workforce to match germline testing demand (97%) and lack of funding assigned in a universal healthcare system (70%) being main concerns. Most (82%) supported a centralised service anticipating improved efficiency gain (71%), equity of access (77%) and streamlining of processes (71%). The stakeholders voiced a consistently high (>50%) demand for information regarding PGV in lower actionability genes. MOB recommendations were adapted to reflect 3 tiers of actionability The centralised clinic has reviewed 21 patients nationally to test 23 highest actionability variants. The average referral to review time is 2 weeks. Conclusions: Precision oncology can play a role in familial cancer prevention. Deliberate collaboration and integration between precision research and relevant specialised care can lead to resource-optimised, equitable delivery of quality care.

Fault Diagnosis Method for Elevator Carriages Based on Temporal Generative Federated Distillation

Abstract Addressing the "data silo" issue among different elevator operating units and the temporal correlations in elevator vibration signals, a novel small-sample fault diagnosis method for elevator carriages based on temporal generative federated distillation is proposed. This method incorporates a temporal generative adversarial network into Federated Distillation via Generative Learning (FedGen). FedGen combines federated learning, knowledge distillation, and generative models to enhance model aggregation efficiency while mitigating data heterogeneity. However, the original generative model struggles to maintain dynamic correlations between signals when extracting temporal features. Therefore, an improved Time Series Generative Adversarial Networks (TimeGAN) model is introduced, substituting the initial logarithmic loss function with a least squares error function, thereby enhancing training stability and data quality. This approach eliminates the need for proxy datasets in knowledge distillation, avoiding the loss of temporal information during central server feature extraction. Simulation results demonstrate that this method enables data sharing while protecting data privacy, and enhances model generalization capabilities.

Partially shared federated multiview learning

With the diversification of data representations, the field of multiview learning has attracted the attention of many researchers. However, multiview data is stored in different devices in practical applications, and existing multiview learning methods fail to apply directly. Moreover, the data transmission process is often accompanied by the risk of privacy leakage. Therefore, this paper proposes a multiview learning method based on a vertical federated learning framework called partially shared federated multiview learning method (PSFedML) to handle this issue. Our proposed method uses a partial sharing approach that can simultaneously utilize consistent and complementary information between views to learn latent representations. In addition, the transmission of common latent representations between local clients and the central server avoids the risk of privacy leakage. We also give a corresponding iterative optimization algorithm. Experiments on real-world datasets demonstrate the efficiency and superiority of PSFedML.

Dynamic Bandwidth Slicing in Passive Optical Networks to Empower Federated Learning.

Federated Learning (FL) is a decentralized machine learning method in which individual devices compute local models based on their data. In FL, devices periodically share newly trained updates with the central server, rather than submitting their raw data. The key characteristics of FL, including on-device training and aggregation, make it interesting for many communication domains. Moreover, the potential of new systems facilitating FL in sixth generation (6G) enabled Passive Optical Networks (PON), presents a promising opportunity for integration within this domain. This article focuses on the interaction between FL and PON, exploring approaches for effective bandwidth management, particularly in addressing the complexity introduced by FL traffic. In the PON standard, advanced bandwidth management is proposed by allocating multiple upstream grants utilizing the Dynamic Bandwidth Allocation (DBA) algorithm to be allocated for an Optical Network Unit (ONU). However, there is a lack of research on studying the utilization of multiple grant allocation. In this paper, we address this limitation by introducing a novel DBA approach that efficiently allocates PON bandwidth for FL traffic generation and demonstrates how multiple grants can benefit from the enhanced capacity of implementing PON in carrying out FL flows. Simulations conducted in this study show that the proposed solution outperforms state-of-the-art solutions in several network performance metrics, particularly in reducing upstream delay. This improvement holds great promise for enabling real-time data-intensive services that will be key components of 6G environments. Furthermore, our discussion outlines the potential for the integration of FL and PON as an operational reality capable of supporting 6G networking.

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Comparative analysis of federated learning algorithms under non-IID data

With the increasing global attention to data privacy and security issues, how to effectively utilize distributed data while protecting personal privacy has become an important research topic. Federated learning (FL) aims to address data silos and privacy issues by enabling multiple devices or servers to train shared models in collaboration without submitting raw data to a central server. Although a variety of federated learning algorithms have been proposed, there is still a gap in the research on their performance differences under the same model. The goal of this study is to compare and analyze the performance differences of different federated learning algorithms on the same model through experiments. Based on the Fashion-MNIST dataset, this paper compares four commonly used federated learning algorithms in detail: Federal Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Stochastic Controlled Averaging for Federated Learning (SCAFFOLD), and Federated Proximal (FedProx). The experimental results show that FedProx performs best in all evaluation indicators, followed by SCAFFOLD and FedAvg, while FedSGD performs the worst. These insights into algorithm performance with non-IID data inform practical application suitability and guide future research.

A Quick Analysis of E-Commerce

Internet shopping is now a sea change that has implications with both consumers and advertisers. Digital transformation will played a great role as in upcoming easy access to financial divisions and business services in a rising economy. To combat deceptive conduct, the rising use of smart mobile operators and the World Wide Web as a niche product for exchange of goods and services and foreign investment needs a stronger attention on t s security. The advances in internet technology has created havoc in almost every element of a lives. E-commerce has a lot of benefits that boost customer and give conveniences in any place and enabling accompany to get a higher market share. As a consequence of extensive internet usage, e-commerce has risen in popularity, and companies, particularly continue, and are rapidly moving to this option as a differentiating strategic framework. This paper discusses about E-commerce, E-commerce providers, Benefits, and Challenges. This paper can be used by researchers for further study in E-commerce in future

Advanced Survey of Blockchain for the Internet of Things Smart Home

"The Internet of Things (IoT) has many uses, like making homes smarter. But a big problem with IoT is that it relies too much on a central server, which can be risky. Blockchain, a new kind of system where everyone shares control, can fix this issue. Smart homes face lots of security problems, like hackers and privacy concerns. Blockchain is helpful here too, making sure data and transactions are safe. This paper talks about using Blockchain in smart homes, breaking it down into three main parts. It explains how Blockchain can protect data and transactions, and talks about the security of IoT smart homes.".

Increasing trust in AI through privacy preservation and model explainability: Federated Learning of Fuzzy Regression Trees

Federated Learning (FL) lets multiple data owners collaborate in training a global model without any violation of data privacy, which is a crucial requirement for enhancing users’ trust in Artificial Intelligence (AI) systems. Despite the significant momentum recently gained by the FL paradigm, most of the existing approaches in the field neglect another key pillar for the trustworthiness of AI systems, namely explainability. In this paper, we propose a novel approach for FL of fuzzy regression trees (FRTs), which are generally acknowledged as highly interpretable by-design models. The proposed FL procedure is designed for the scenario of horizontally partitioned data and is based on the transmission of aggregated statistics from the clients to a central server for the tree induction procedure. It is shown that the proposed approach faithfully approximates the ideal case in which the tree induction algorithm is applied on the union of all local datasets, while still ensuring privacy preservation. Furthermore, the FL approach brings benefits, in terms of generalization capability, compared to the local learning setting in which each participant learns its own FRT based only on the private, local, dataset. The adoption of linear models in the leaf nodes ensures a competitive level of performance, as assessed by an extensive experimental campaign on benchmark datasets. The analysis of the results covers both the aspects of accuracy and interpretability of FRT. Finally, we discuss the application of the proposed federated FRT to the task of Quality of Experience forecasting in an automotive case-study.

Factors affecting the intention to adopt cloud accounting software in small and medium enterprises in Ho Chi Minh City

In the trend of the digital economy along with the widespread Industrial Revolution 4.0, accounting work organization needs to innovate in applying information technology (IT) in the implementation and management of accounting tasks. Cloud accounting software is built based on the development achievements of Cloud Computing, data is stored on data center servers, helping to process data and accounting work anytime, anywhere. and on every device. This article aims to identify factors influencing the intention of the adoption of cloud accounting software in small and medium enterprises (SMEs) in Ho Chi Minh City. Through data collection using questionnaires from 210 SMEs and using SPSS analysis tools, the research results show that there is an impact of factors such as perceived usefulness, perceived ease of use, manager support, social influences, and application risks that affect the intention to apply cloud accounting software of these businesses. These results provide useful information to help business managers better understand the benefits of the application, the role and importance of IT application in general, and cloud accounting software in particular in business operations, contributing to improving accounting work at the business.

Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning

Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework allows distributed training of GNN models using local user data. Each client trains a GNN using its own user–item graph and uploads gradients to a central server for aggregation. To overcome limited data, we propose expanding local graphs using Software Guard Extension (SGX) and Local Differential Privacy (LDP). SGX computes node intersections for subgraph exchange and expansion, while local differential privacy ensures privacy. Additionally, we introduce a personalized approach with Prototype Networks (PN) and Model-Agnostic Meta-Learning (MAML) to handle data heterogeneity. This enhances the encoding abilities of the federated meta-learner, enabling precise fine-tuning and quick adaptation to diverse client graph data. We leverage SGX and local differential privacy for secure parameter sharing and defense against malicious servers. Comprehensive experiments across six datasets demonstrate our method’s superiority over centralized GNN-based recommendations, while preserving user privacy.