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ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

Increasing occurrences of Microcystis surface scum have been observed in the context of global climate change and the increase in anthropogenic pollution, causing deteriorating water quality in aquatic ecosystems. Previous studies on scum formation mainly focus on the buoyancy-driven floating process of larger Microcystis colonies, neglecting other potential mechanisms. To study the non-buoyancy-driven rapid flotation of Microcystis, we here investigate the floating processes of two strains of single-cell species (Microcystis aeruginosa and Microcystis wesenbergii), which are typically buoyant, under light conditions (150 μmol photons s−1 m−2). Our results showed that M. wesenbergii exhibited fast upward migration and formed surface scum within 4 hours, while M. aeruginosa did not form visible scum throughout the experiments. To further explore the underlying mechanism of these processes, we compared the dissolved oxygen (DO), extracellular polymeric substance (EPS) content, and colony size of Microcystis in different treatments. We found supersaturated DO and the formation of micro-bubbles (50–200 µm in diameter) in M. wesenbergii treatments. M. aeruginosa produces bubbles in small quantities and small sizes. Additionally, M. wesenbergii produced more EPS and tended to aggregate into larger colonies. M. wesenbergii had much more derived-soluble extracellular proteins and polysaccharides compared to M. aeruginosa. At the same time, M. wesenbergii contains abundant functional groups, which was beneficial to the formation of agglomerates. The surface scum observed in M. wesenbergii is likely due to micro-bubbles attaching to the surface of cell aggregates or becoming trapped within the colony. Our study reveals a species-specific mechanism for the rapid floatation of Microcystis, providing novel insights into surface scum formation as well as succession of cyanobacterial species.

Due to climate change, Microcystis blooms occur at increasing frequencies in aquatic ecosystems worldwide. Wind-generated turbulence is a crucial environmental stressor that can vertically disperse the Microcystis surface scum, reducing its light availability. Yet, the interactions of Microcystis scum with the wind-generated hydrodynamic processes, particularly those at the air-water interface, remain poorly understood. Here, we explore the response of Microcystis (including colony size and migration dynamics) to varying magnitudes and durations of intermittent wind disturbances in a mesocosm system. The flow velocities, size of Microcystis colonies, and the areal coverage of the water surface by scum were measured through video observations. Our results demonstrate that low wind speeds increase colony size by providing a stable condition where Microcystis forms a scum layer and aggregates into large colonies at the air-water interface. In contrast, wind disturbances disperse scum and generate turbulence, resulting in smaller colonies with higher magnitudes of wind disturbance. We observed that surface scum can form rapidly following a long period (6 h) of high-magnitude (4.5 m s-1) wind disturbance. Furthermore, our results indicate reduced water surface tension caused by the presence of Microcystis, which can decrease surface flow velocity and counteract wind-driven mixing. The reduced surface tension may also drive lateral convection at the water surface. These findings suggest that Microcystis reduces surface tension, likely by releasing surface-active materials, as an adaptive response to various wind conditions. This could result in an increased rate of surface scum re-formation under wind conditions and potentially facilitate the lateral expansion of scum patches during weak wind periods. This study reveals new insights into how Microcystis copes with different wind conditions and highlights the importance of the air-water interface for Microcystis scum dynamics.

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress.

Abstract Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.

Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).

Abstract Background The blocking of the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis has been found to have an anticancer activity against various types of cancer by enhancing T cell immunity, while there are no studies linking the PD-1/PD-L1 axis to chemotherapy drugs in osteosarcoma (OS). The present study aimed to investigate the effects of blocking PD-1/PD-L1 axis on the cisplatin chemotherapy in OS in vitro and in vivo. Methods Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to detect PD-L1 mRNA in OS tissues. Cell proliferation and apoptosis were measured by Cell Counting Kit-8 (CCK-8) and flow cytometry assays, respectively. In vivo, the syngeneic mice were treated with cisplatin and anti-PD-1 antibody alone or jointly. Results In this study, it revealed that PD-L1 mRNA was highly expressed in OS tissues. Further inhibitory evaluation showed that the K7M2-LV cells (PD-L1 overexpression) co-cultured with PD-1+ lymphocytes could promote K7M2 cell proliferation. Meanwhile, the combination of anti-PD-1 antibody and cisplatin significantly decreased the proliferation and increased the apoptosis of K7M2 cells in a co-culture system. In vivo, the combination of anti-PD-1 antibody and cisplatin significantly inhibited tumor growth, while the mechanisms did not involve regulatory T cells. Conclusion The present data suggested that the blocking of PD-1/PD-L1 axis had a positive prognostic value, which can enhance the chemotherapeutic effect of cisplatin in OS. These findings provide a rationale for utilizing PD1/PD-L1 blocking antibodies as a single agent to cure refractory OS in patients receiving cisplatin treatment.

In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling temporal community structures. But the number of communities cannot be determined automatically. Therefore, a model, Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF), is proposed in this paper. It focuses on modeling the temporal community structures with evolution characteristics. More specifically, the evolution behavior, which is introduced into EvoBNMF, can quantify the transfer intensity of communities between adjacent snapshots for modeling the evolution characteristics. Innovatively, the most appropriate number of communities can be determined autonomously by shrinking the corresponding evolution behaviors. Experimental results show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.

Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while ignoring the similarity between different layers. In this paper, for multiplex network embedding, we propose a Layer Information Similarity Concerned Network Embedding (LISCNE) model considering the similarities between layers. Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors learn semantics for each layer. We get the node embeddings in each layer by concatenating the common vectors and layer vectors with the consideration that the node embedding is related not only to the surrounding neighbors but also to the overall semantics. Furthermore, we define an index to formalize the similarity between different layers and the cross-network association. Constrained by layer similarity, the layer vectors with greater similarity are closer to each other and the aligned node embedding in these layers is also closer. To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance compared to existing baseline methods.

With the development of deep learning, deep metric learning (DML) has achieved great improvements in face recognition. Specifically, the widely used softmax losses in the training process often bring large intra-class variations, and feature normalization is only exploited in the testing process to compute the pair similarities. To bridge the gap, we impose the intra-class cosine similarity between the features and weight vectors in softmax loss larger than a margin in the training step and extend it from four aspects. First, we explore the effect of a hard sample mining strategy. To alleviate the human labor of adjusting the margin hyper-parameter, a self-adaptive margin updating strategy is proposed. Then, a normalized version is given to take full advantage of the cosine similarity constraint. Furthermore, we enhance the former constraints to consider the intra-class and inter-class constraints simultaneously in the exponential feature projection space. The extensive experiments on the labeled face in the wild (LFW), youtube faces (YTF), and IARPA Janus benchmark A (IJB-A) datasets demonstrate that the proposed methods outperform the mainstream DML methods and approach the state-of-the-art performance.

Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.

Running very complex applications on mobile devices is still challenging since they are constrained by limited resources, such as memory capacity, network bandwidth, processor speed, and battery power. Mobile Cloud Computing (MCC) is a combination of cloud computing and mobile internet, which could effectively alleviate the resource constraints of mobile devices. How to efficiently offload computation intensive parts of mobile applications from mobile devices to capable cloud servers is one of the keys. In mobile environments, the resource heterogeneity of mobile devices and cloud services, the interruption of heterogeneous wireless networks, the complexity of mobile applications, and the characteristic of transferring a large amount of data, are the major bottlenecks that have prevented this technology from being widely used. This paper takes these constraints into an account at the same time and explores methods of multi-objective decision making for timeand energy-aware task offloading for MCC. It is designed to ensure the right computational tasks are executed in the right way, at the right time and place.

Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.

It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for training an object detector due to its intuition and practicability. For the task of localization quality estimation, there exist two ways of sampling: The same sampling with the main tasks and the uniform sampling by manually augmenting the ground-truth. The first method of sampling is simple but inconsistent for the task of quality estimation. The second method of uniform sampling contains all IoU level distributions but is more complex and difficult for training. In this paper, we propose an H+L-Sampling strategy, selecting the high and low IoU samples simultaneously, to effectively and simply train the branch of quality estimation. This strategy inherits the effectiveness of consistent sampling and reduces the training difficulty of uniform sampling. Finally, we introduce accurate detection confidence, which combines the classification probability and the localization accuracy, as the ranking keyword of NMS. Extensive experiments show the effectiveness of our method in solving the misalignment between classification confidence and localization accuracy and improving the detection performance.

Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices for higher performance and energy saving. Different applications usually give different relative importance to the factors of response time and energy consumption. In this paper, we investigate two types of delayed offloading policies, the partial model where jobs can leave from the slow phase of the offloading process and then executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and then offloaded via the Cellular Queue. In both models we minimise the Energy-Response time Weighted Product (ERWP) metric. We find that jobs abandon the queue very often especially when the availability ratio (AR) of the WiFi network is relatively small. We can optimally choose the reneging deadline to achieve different energy-performance tradeoff by optimizing the ERWP metric. The amount of delay a job can tolerate closely depends on the application type and the potential energy saving for the mobile device. In general one can say that for delay-sensitive applications, the partial offloading model is preferred when having a suitable reneging rate, while for delay-tolerant applications, the full offloading model shows very good results and outperforms the other offloading models when setting the deadline a large value.

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.