Fair scheduling in wireless ad-hoc networks of location dependent channel errors

The growth of the wireless networks has brought the issue of fair allocation of bandwidth among the users. Besides the issues in wired networks scheduling, wireless network has to take into account the characteristics of wireless channel, such as channel errors, location dependent contention, hidden or exposed stations, spatial channel reuse, and constraints in mobile hosts processing power and battery power. Medium Access Control (MAC) protocols define rules for orderly access to the shared medium and play a crucial role in the efficient and fair sharing of scarce wireless bandwidth. The IEEE standard 802.11 specifies the wireless LAN protocols for both infrastructure-based and ad-hoc networks. The previous studies in ad-hoc networks have assumed error free channel. However, in the wireless domain, a packet flow may experience location-dependent channel error and hence may not be able to complete transmission. The bursty channel errors can render the previous studies inapplicable. This study developed a fairness model for fair scheduling to deal with channel error in wireless ad-hoc networks. The throughput of the network is increased while the fairness is maintained in the model. The model was implemented in a distributed manner by localizing the global information required by the users of the networks. The simulation results showed the scheduling model achieves higher throughput and maintains the fairness at the same time

Determinants of online hotel reservation system use

More and more hoteliers have established Web sites for their hotels. One of their objectives is to set up a more efficient means for customers to make online room reservations. Customers\u27 perceptions of online reservation systems are the crucial determinants of their actual system use. It\u27s important to find out their perceptions. However, no research has been reported on customers\u27 perceptions of online reservation systems. This study developed a conceptual framework based upon previous research. The objective of the study was to determine customers\u27 perceptions of online hotel reservation systems and find out the relationships among the constructs in the conceptual framework. The study developed seven propositions to verify the relationships among the constructs in the conceptual framework. A randomly selected 2,000 faculty and staff members at five 4-year colleges in the Midwest of the US were asked to participate in the study. The reason for selecting them as a sample of the study is that they are Web users, potential and existing hotel customers. An online survey method was used in the study. The results of this study confirmed that there was a significant relationship between perceived usefulness (PU) and attitude toward online hotel reservation system. The results also showed that attitude was significantly correlated with intention to use the system and intention had a significant relationship with actual system use. Other relationships between the constructs were not significant and the according propositions were not supported at the level of .05. Multiple regression analysis showed that attitude accounted for 51% of the variance in the future use intention. PU, perceived ease of use (PEOU), and perceived accessibility (PA) were excluded from the regression model because they were not significant at the level of .05. Limitations and suggestions for future research are presented

Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling

With the increasing number of sequenced species, phylogenetic profiling (PP) has become a powerful method to predict functional genes based on co-evolutionary information. However, its potential in plant genomics has not yet been fully explored. In this context, we combined the power of machine learning and PP to identify salt stress-related genes in a halophytic grass, Spartina alterniflora, using evolutionary information generated from 365 plant species. Our results showed that the genes highly co-evolved with known salt stress-related genes are enriched in biological processes of ion transport, detoxification and metabolic pathways. For ion transport, five identified genes coding two sodium and three potassium transporters were validated to be able to uptake Na+. In addition, we identified two orthologs of trichome-related AtR3-MYB genes, SaCPC1 and SaCPC2, which may be involved in salinity responses. Genes co-evolved with SaCPCs were enriched in functions related to the circadian rhythm and abiotic stress responses. Overall, this work demonstrates the feasibility of mining salt stress-related genes using evolutionary information, highlighting the potential of PP as a valuable tool for plant functional genomics

Mixture extreme learning machine algorithm for robust regression

The extreme learning machine (ELM) is a well-known approach for training single hidden layer feedforward neural networks (SLFNs) in machine learning. However, ELM is most effective when used for regression on datasets with simple Gaussian distributed error because it often employs a squared loss in its objective function. In contrast, real-world data is often collected from unpredictable and diverse contexts, which may contain complex noise that cannot be characterized by a single distribution. To address this challenge, we propose a robust mixture ELM algorithm, called Mixture-ELM, that enhances modeling capability and resilience to both Gaussian and non-Gaussian noise. The Mixture-ELM algorithm uses an adjusted objective function that blends Gaussian and Laplacian distributions to approximate any continuous distribution and match the noise. The Gaussian mixture accurately models the residual distribution, while the inclusion of the Laplacian distribution addresses the limitations of the Gaussian distribution in identifying outliers. We derive a solution to the novel objective function using the expectation maximization (EM) and iteratively reweighted least squares (IRLS) algorithms. We evaluate the effectiveness of the algorithm through numerical simulation and experiments on benchmark datasets, thereby demonstrating its superiority over other state-of-the-art machine learning methods in terms of robustness and generalization

A hybrid Autoformer framework for electricity demand forecasting

Electricity demand forecasting is of great significance to the electricity system and residents’ life, but it is difficult to forecast the electricity demand series because of the influence of cyclical factors. Electricity demand forecasting also faces the problem of small data amounts. Therefore, we need to design a model that is less affected by data volume and can cope with complex electricity demand series. Based on the Autoformer model, this paper establishes a novel forecasting framework with excellent performance. In the part of data preprocessing, multiple linear regression with 10 variables and Bootstrap processing are added. In the part of the model, the Auto Correlation mechanism is modified to better extract the historical and nonlinear characteristics of electricity demand series from different time spans. Using this framework, we further analyze the impact of working days and seasonal changes on the electricity demand in Taixing City and New South Wales. In addition, we propose a new electricity demand forecasting method, which can adjust the original sequence according to the actual situation. The experimental results show that this method can achieve good precision in demand forecasting. Taking Taixing of China and New South Wales of Australia as examples, the forecasting performance with the proposed framework is better than that of Autoformer, Reformer, Informer, and other mainstream models. The forecasting indexes with our proposed framework of the test set are MAE: 35.05, RMSE: 47.28, MAPE: 1.63 in Taixing and MAE: 193.17, RMSE: 239.96, MAPE: 2.43 in NS

Energy efficient model for data gathering in structured multiclustered wireless sensor network

Wireless sensor networks consist of a group of nodes, each equipped with sensing, actuating, computation, communication, and storage resources. These sensor nodes are powered by batteries, which are considered as limited resources. Many applications of sensor networks, such as surveillance systems in both civil and military area, habitual monitoring etc., won\u27t allow the replacement of battery supplies. Therefore, to reduce the energy consumption is the key to prolong the lifetime of sensor networks. In this paper, we present two energy efficient data gathering models to achieve longer lifetime in a structured multiclustered topology. The local homogeneous sensor nodes are grouped together to form clusters and a special processing and relaying node is designated to be responsible for communication among local groups. Such models are developed for power transmission line monitoring systems. The goal is to achieve uninterrupted monitoring over a long time using power constrained sensor nodes because the replacement of battery is a major issue in such applications. We use Markov chain process to analyse the proposed two models and comparison shows that the two level communication model consumes less power and is more suitable than single level communication model on the power transmission line monitoring systems

Distributed fault detection of wireless sensor networks

Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the sensor device itself and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faults and take actions to avoid further degradation of the service. The goal of this paper is to locate the faulty sensors in the wireless sensor networks. We propose and evaluate a localized fault detection algorithm to identify the faulty sensors. The implementation complexity of the algorithm is low and the probability of correct diagnosis is very high even in the existence of large fault sets. Simulation results show the algorithm can clearly identify the faulty sensors with high accuracy

A Semantic Cross-Species Derived Data Management Application

Managing dynamic information in large multi-site, multi-species, and multi-discipline consortia is a challenging task for data management applications. Often in academic research studies the goals for informatics teams are to build applications that provide extract-transform-load (ETL) functionality to archive and catalog source data that has been collected by the research teams. In consortia that cross species and methodological or scientific domains, building interfaces that supply data in a usable fashion and make intuitive sense to scientists from dramatically different backgrounds increases the complexity for developers. Further, reusing source data from outside one's scientific domain is fraught with ambiguities in understanding the data types, analysis methodologies, and how to combine the data with those from other research teams. We report on the design, implementation, and performance of a semantic data management application to support the NIMH funded Conte Center at the University of California, Irvine. The Center is testing a theory of the consequences of "fragmented" (unpredictable, high entropy) early-life experiences on adolescent cognitive and emotional outcomes in both humans and rodents. It employs cross-species neuroimaging, epigenomic, molecular, and neuroanatomical approaches in humans and rodents to assess the potential consequences of fragmented unpredictable experience on brain structure and circuitry. To address this multi-technology, multi-species approach, the system uses semantic web techniques based on the Neuroimaging Data Model (NIDM) to facilitate data ETL functionality. We find this approach enables a low-cost, easy to maintain, and semantically meaningful information management system, enabling the diverse research teams to access and use the data

Asiaticoside Mitigates Alzheimer’s Disease Pathology by Attenuating Inflammation and Enhancing Synaptic Function

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, hallmarked by the accumulation of amyloid-β (Aβ) plaques and neurofibrillary tangles. Due to the uncertainty of the pathogenesis of AD, strategies aimed at suppressing neuroinflammation and fostering synaptic repair are eagerly sought. Asiaticoside (AS), a natural triterpenoid derivative derived from Centella asiatica, is known for its anti-inflammatory, antioxidant, and wound-healing properties; however, its neuroprotective function in AD remains unclear. Our current study reveals that AS, when administered (40 mg/kg) in vivo, can mitigate cognitive dysfunction and attenuate neuroinflammation by inhibiting the activation of microglia and proinflammatory factors in Aβ1-42-induced AD mice. Further mechanistic investigation suggests that AS may ameliorate cognitive impairment by inhibiting the activation of the p38 MAPK pathway and promoting synaptic repair. Our findings propose that AS could be a promising candidate for AD treatment, offering neuroinflammation inhibition and enhancement of synaptic function

Hydrocortisone Mitigates Alzheimer’s-Related Cognitive Decline through Modulating Oxidative Stress and Neuroinflammation

Alzheimer’s disease (AD), an age-related degenerative disorder, is characterized by β-amyloid deposition, abnormal phosphorylation of tau proteins, synaptic dysfunction, neuroinflammation, and oxidative stress. Despite extensive research, there are no medications or therapeutic interventions to completely treat and reverse AD. Herein, we explore the potential of hydrocortisone (HC), a natural and endogenous glucocorticoid known to have potent anti-inflammatory properties, in an Aβ1–42-induced AD mouse model. Our investigation highlights the beneficial effects of HC administration on cognitive impairment, synaptic function enhancement, and neuronal protection in Aβ1–42-induced AD mice. Notably, HC treatment effectively suppresses the hyperactivation of microglia and astrocytes, leading to a reduction in proinflammatory factors and alleviation of neuroinflammation. Furthermore, HC intervention demonstrates the capacity to mitigate the generation of ROS and oxidative stress. These compelling findings underscore the potential therapeutic application of HC in AD and present promising opportunities for its utilization in AD prevention and treatment. The implications drawn from our findings indicate that hydrocortisone holds promise as a viable candidate for adjunctive use with other anti-AD drugs for the clinical management of patients presenting with moderate to severe AD