237 research outputs found
Clustering Opportunistic Ant-based Routing Protocol for Wireless Sensor Networks
The wireless Sensor Networks (WSNs) have a wide range of applications in many ereas, including many kinds of uses such as environmental monitoring and chemical detection. Due to the restriction of energy supply, the improvement of routing performance is the major motivation in WSNs. We present a Clustering Opportunistic Ant-based Routing protocol (COAR), which comprises the following main contributions to achieve high energy efficient and well load-balance: (i) in the clustering algorithm, we caculate the theoretical value of energy dissipation, which will make the number of clusters fluctuate around the expected value, (ii) define novel heuristic function and pheromone update manner, develop an improved ant-based routing algorithm, in this way, the optimal path with lower energy level and shorter link length is established, and (iii) propose the energy-based opportunistic broadcasting mechanism to reduce the routing control overhead. We implement COAR protocol in NS2 simulator and our extensive evaluation shows that COAR is superior to some seminal routing algorithms under a wide range of scenarios
Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs
Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime
Arctigenin-induced reversal of drug resistance in cisplastin-resistant cell line A549/DDP, and the mechanism involved
Purpose: To investigate the drug resistance reversal effect of arctigenin (ARG) on cisplatin-insensitive A549/DDP cancer cells, and to elucidate the underlying mechanism(s).
Methods: Four groups of cells: control, DDP, ARG and ADP were used. The degrees of inhibition of proliferation, drug resistance and apoptotic changes were measured using MTT assay, CCK-8 assay and flow cytometry, respectively. Expressions of PTEN and STAT3 proteins were determined by Western blotting.
Results: At ARG concentration of 5 μmol/L, A549/DDP cells were significantly inhibited (p < 0.05). The combination therapy was more effective in reversing A549/DDP cells resistance than the single therapy. The expression level of PTEN protein increased with increase in ARG concentration, while STAT3 protein expression decreased with increase in ARG concentration. ADP group up-regulated PTEN but decreased STAT3 expression levels.
Conclusion: ARG regulates drug resistance in A549/DDP cells, possibly via a mechanism involving reduction of A549/DDP cell sensitivity to DDP, thereby regulating the stress pathways associated with PTEN and STAT3. The combination of ARG and DDP effectively reduces A549/DDP cells resistance
Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient
A fitness assignment process transforms the features (such as the objective
value) of a candidate solution to a scalar fitness, which then is the basis for
selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding
to an objective value is its encounter frequency in selection steps and is
subject to minimization. FFA creates algorithms that are not biased towards
better solutions and are invariant under all injective transformations of the
objective function value. We investigate the impact of FFA on the performance
of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the
Self-Adjusting (1+(lambda,lambda)) GA. FFA improves their performance
significantly on some problems that are hard for them. In our experiments, one
FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the
theory-based benchmark problems in our study, including traps, jumps, and
plateaus. We propose two hybrid approaches that use both direct and FFA-based
optimization and find that they perform well. All FFA-based algorithms also
perform better on satisfiability problems than any of the pure algorithm
variants
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Text summarization has been a crucial problem in natural language processing
(NLP) for several decades. It aims to condense lengthy documents into shorter
versions while retaining the most critical information. Various methods have
been proposed for text summarization, including extractive and abstractive
summarization. The emergence of large language models (LLMs) like GPT3 and
ChatGPT has recently created significant interest in using these models for
text summarization tasks. Recent studies \cite{goyal2022news,
zhang2023benchmarking} have shown that LLMs-generated news summaries are
already on par with humans. However, the performance of LLMs for more practical
applications like aspect or query-based summaries is underexplored. To fill
this gap, we conducted an evaluation of ChatGPT's performance on four widely
used benchmark datasets, encompassing diverse summaries from Reddit posts, news
articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's
performance is comparable to traditional fine-tuning methods in terms of Rouge
scores. Moreover, we highlight some unique differences between
ChatGPT-generated summaries and human references, providing valuable insights
into the superpower of ChatGPT for diverse text summarization tasks. Our
findings call for new directions in this area, and we plan to conduct further
research to systematically examine the characteristics of ChatGPT-generated
summaries through extensive human evaluation.Comment: Work in progres
Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Large Language Models (LLMs) have demonstrated significant enhancements in
instruction-following abilities through instruction tuning, achieving notable
performances across various tasks. Previous research has focused on fine-tuning
medical domain-specific LLMs using an extensive array of medical-specific data,
incorporating millions of pieces of biomedical literature to augment their
medical capabilities. However, existing medical instruction-tuned LLMs have
been constrained by the limited scope of tasks and instructions available,
restricting the efficacy of instruction tuning and adversely affecting
performance in the general domain. In this paper, we fine-tune LLaMA-series
models using 52k diverse, machine-generated, medical instruction-following
data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive
experimental results on both general and medical-specific domain free-form
instruction evaluations showcase AlpaCare's strong medical proficiency and
generalizability compared to previous instruction-tuned models in both medical
and general domains. We provide public access to our MedInstruct-52k dataset
and a clinician-crafted free-form instruction test set, MedInstruct-test, along
with our codebase, to foster further research and development. Our project page
is available at https://github.com/XZhang97666/AlpaCare
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