Most works on reflecting-only intelligent reflecting surface (IRS) are based on the ideal phase shift model (IPSM), which assumes that the reflection amplitude is independent of the phase shift at each IRS element. However, due to hardware limitations of practical IRS circuits, practical IRS generally follows the practical phase shift model (PPSM), where the reflection amplitude is related to the phase shift. This paper analyzes the average block error rate (BLER) of PPSM-based IRSaided short-packet communication (SPC) with statistical channel state information over Rician fading. Because the optimal continuous phase shifts at the PPSM-based IRS are difficult to be explicitly solved, we first propose a deep deterministic policy gradient (DDPG) based algorithm to optimize the phase shift design. Then, we derive the average BLER of the considered IRS-aided SPC with the optimized reflection matrix. Finally, numerical and simulation results demonstrate that the IRS-aided SPC system with the proposed DDPG-based phase shifts outperforms the system with the IPSM-based optimal phase shifts. They also show that, different from the IPSM-based IRS, the optimal phase shifts at the PPSM-based IRS are related to the Rician factors.

Essential proteins are group of proteins that are indispensable to survival and development of cells. Prediction and analysis of essential genes/proteins are crucial for uncovering the mechanisms of cells. Using bioinformatics and high-throughput technologies, forecasting essential genes/proteins by protein-protein interaction (PPI) networks have become more efficient than traditional approaches which use expensive and time-consuming experimental methods. Previous studies have found that the essentiality of genes closely relates to their properties in PPI network. In this work, we propose a supervised deep model for predicting human essential genes using neighboring details of genes/proteins in the PPI network. Our approach implements a weight-biased random walk on PPI network to get the node network context. Then, some different measures are used to get some feature vectors for each node (gene/protein) that preserve the network structure as well as the gene's properties in the PPI network. These feature vectors are then fed to a Relational AutoEncoder to embed the genes' features into latent space. At last, these embedded features are put into a trained classifier to predict the human essential genes. The prediction results on two human PPI networks show that our model achieves better performance than those that only refer to genes' centrality properties in the network.

Bunyakovsky conjecture is proved to be true.

Gas sensors based on two-dimensional (2D) materials have gained significant research attention due to their numerous advantageous properties, including a high surface-to-volume ratio, exceptional electrical conductivity, tunable electronic properties, mechanical flexibility, and potential for functionalization to enhance sensitivity and selectivity. In this paper, we employ first-principles calculations to conduct a predictive study on the gas-sensing properties of a recently proposed novel 2D phase of Gallium Oxide monolayer, with a unit cell formula of Ga 2 O 2 [L. Shao et al., Physical Chemistry Chemical Physics, 23(1), 666-673, 2021]. Our findings illustrate that the monolayer exhibits remarkable sensitivity and selectivity for detecting NH 3 molecules. Additionally, although NO demonstrates comparatively lower adsorption energy on the monolayer, it reveals exceptionally favorable attributes for sensitivity, making the monolayer suitable for detecting NO molecules at lower temperatures. The low adsorption energy of the monolayer towards ambient molecules makes it suitable for deployment in ambient environments. The monolayer also shows potential for enhanced NH 3 sensitivity through application of appropriate strain. Thus, the Ga 2 O 2 monolayer emerges as a promising candidate for the fabrication of commercial 2D gas sensors.

In this research work, a new approach to compensate for both the response In this paper, we highlight the crucial role of substation digitalization in identifying the origins of harmonic disturbances within iron and steel plants connected to the point of common coupling (PCC). Our method capitalizes on the benefits of a digital substation infrastructure by utilizing merging units that operate at a 28.8 kHz sampling rate, in line with the IEC 61850-9-2 standard. This setup allows for the real-time measurement of voltage and current from all feeders, streaming these high-resolution data directly to substation servers. This digital approach not only facilitates precise and timely data acquisition but also enhances the overall reliability and efficiency of the monitoring process. By digitalizing the substation, we enable a more dynamic response to power quality issues and improve the capacity for preventive maintenance. Integrating our method into existing digital substations supports a robust framework for managing and mitigating the effects of current harmonics and interharmonics, showcasing the practical benefits of advanced digital infrastructure in critical utility operations. We propose attention based long short-term memory as a novel method for determining the harmonic responsibility of each plant without the need for instantaneous impedance measurements. This method relies on the correlation of time-synchronized voltage and current measurements taken from all feeders at the PCC. The approach is first validated using synthetic data generated in a PSCAD/EMTDC simulation environment, as well as real-world field data. Further verification is conducted through measurements of voltage and current waveforms using mobile Power Quality (PQ) measurement systems at one of the PCCs in the electricity transmission system. Our results demonstrate that the harmonic responsibility of each IS plant can be accurately determined for each harmonic order. Unlike existing methods, our proposed approach eliminates the need for online system impedance measurements or in-plant measurements.

The tracheotomy procedure is crucial for situations involving intubation, airway blockages, or neck injuries, requiring precise incision placement to minimize risks and ensure effectiveness. Traditional methods involve palpating neck landmarks, but challenges arise in scenarios like teleoperation or critical care. Recent advancements in Augmented Reality (AR) and robotic-assisted surgery (RAS) offer promising solutions to enhance procedural safety and accuracy. In our study, we aim to explore the utilization of AR guidance in assisting tracheostomy incision localization. We employ a handheld ultrasound (US) probe to acquire preoperative anatomical data from a larynx phantom and convert ultrasound data into visual feedback, thus employing a multimodal approach that integrates US information into the visual domain. By marking the region of interest (ROI) on the laryngeal phantom model and importing it into the Hololens 2 device, we achieve visual guidance for precise incision insertion within the ROI. Additionally, we conducted laser localization comparison experiments, comparing procedures performed with and without AR glasses. AR-guided incision localization exhibited impressive performance metrics with a high precision of 0.932 for the cricothyrotomy scene and 0.938 for standard tracheotomy while demonstrating minimal mean central positioning errors of 0.301 mm and 0.236 mm, respectively. The results demonstrate that AR guidance enables surgeons to locate the corresponding laryngeal area touchlessly, efficiently, and accurately, thereby facilitating the progress of robotic-assisted tracheotomy.

Power quality phenomena, especially voltage sags, characterized by short-duration reductions in RMS voltage, pose significant challenges to power systems. This study investigates and validates different faults-induced balanced and imbalanced voltage sags at comparative magnitudes of 50% and 30% with a frequency of 50Hz in a multilevel industrial distribution system. The sag magnitudes and phase angle jumps are calculated and further validated through simulations based on phase and RMS voltage drops, sag-type transformations, and sequence component propagation. Fast Fourier transform (FFT) analysis is employed to investigate the magnitudes and propagation of sequence components across different distribution levels. Based on fault induction, the results reveal the transformation of different types of sags or the conversion to a new distorted version of the original type of sag with a unique matrix. The zero-sequence component for each type of sag is removed after passing through a ∆Y configured transformer with a Y-connected load. Moreover, a comparison shows the critical role of different sag magnitudes on phase angle jumps. At both 50% and 30% magnitudes, the system becomes more vulnerable during a phase-to-phase fault. Based solely on phase angle jumps, the sag types D and F are more susceptible at 30% magnitude, while the types C and G are more susceptible at 50%.

Large language models (LLMs) have received considerable interest recently due to their outstanding reasoning and comprehension capabilities. This work explores applying LLMs to vehicular networks, aiming to jointly optimize vehicleto-infrastructure (V2I) communications and autonomous driving (AD) policies. We deploy LLMs for AD decision-making to maximize traffic flow and avoid collisions for road safety, and a double deep Q-learning algorithm (DDQN) is used for V2I optimization to maximize the received data rate and reduce frequent handovers. In particular, for LLM-enabled AD, we employ the Euclidean distance to identify previously explored AD experiences, and then LLMs can learn from past good and bad decisions for further improvement. Then, LLM-based AD decisions will become part of states in V2I problems, and DDQN will optimize the V2I decisions accordingly. After that, the AD and V2I decisions are iteratively optimized until convergence. Such an iterative optimization approach can better explore the interactions between LLMs and conventional reinforcement learning techniques, revealing the potential of using LLMs for network optimization and management. Finally, the simulations demonstrate that our proposed hybrid LLM-DDQN approach outperforms the conventional DDQN algorithm, showing faster convergence and higher average rewards.

IntroductionThe time spent by students using digital devices for learning is increasing rapidly with the development of new, portable and instantly accessible technology, such as smartphones and digital tablets. Furthermore, during the pandemic, online learning has dramatically increased. This explosion in e-Learning which is traditionally different from pen and paper has brought a difficult situation because screen and paper have different natures. Although the screen has some advantages over paper, the screen glows and emits blue light to show the content, while paper doesn’t. The emission of bright lights, especially blue light during night from a screen has many negative consequences for a user in the long term. [1] While studying online, students have to view the screen for a longer period of time. The brightness of the screen can be a bottleneck for students to study for a longer period of time. So, to reduce the emission of blue light, dark mode is a simple but a great option to have. [2] Implementing dark mode is not just about making the background darker, but also about balancing the color combination, contrast between background and items such as icons, text, images and many more UI elements. [3] Dark mode provides a great relief on minimizing the dry and painful eyes syndrome that are caused due to studying on a bright blue screen all night long. Bright lights also affect secretion of melatonin, a hormone needed for sleep. [4] Besides the health benefits, dark mode on various applications helps to enhance battery life. Using dark mode on an OLED screen can extend battery life in noticeable figures. [5] The dark mode created is analyzed using principles of HCI. Human Computer Interaction (HCI) is understood as the design, evaluation and implementation of interactive computing systems for human use. HCI is a key part of computer science, psychology, and design because designing a successful interface requires an understanding of how people interact with devices, user interactive graphical and collaborative platforms. Dark mode is a new trend in UI/UX design. Almost all Silicon Valley companies are adopting this trend. [6] Today, almost all the major websites support dark mode. Even the Operating Systems are adopting dark mode, including Windows, IOS and Android, in their latest update. Facebook, Twitter, YouTube, Reddit etc. are few big companies that have already included dark mode in their products. Dark mode even though a simple concept has significant advantages to other interface designs, which is why the trend is still growing, and rapidly being adopted by companies worldwide [7]. Tested in an exploratory project involving apartment selection, a human-computer interface was created in a study by Archer et al. [8] to investigate user preferences and the efficacy of output styles and levels of information abstraction in a decision-making environment. The study discovered that voice alone was not as effective for information search as text plus voice. Nevertheless, there were no appreciable differences in preferences between text and voice or text and text plus voice, and task performance was more influenced by cognitive style than by task domain experience. The first online brain-computer interface using deep learning and a menu-driven language generating system based on referencing expressions were created in a study by Kuhner et al. [9] for a BCI-controlled autonomous robotic service assistant. To determine the efficacy of this system, it was connected with a modular ROS-based mobile robot and tested experimentally on a real robot. To investigate user preferences, the efficacy of output modalities, and the degrees of information abstraction in decision making, a human-computer interface was created in a study by Yang et al. [10]. The exploratory study on apartment selection identified disparities between perceived relevance and actual utilization of information qualities and abstraction levels, showing that text with voice was favored over voice alone and that text was the most effective for information search. Software designers utilize anthropomorphism to improve interface usability as computers get more sophisticated. In the study by Laere et al. [11], participants’ reflected assessments and self-appraisals were strongly impacted by computer input, but there was no discernible difference in the impact between the two interface types. The study examined human-like and machine-like interaction styles. Software designers utilize anthropomorphism to improve interface usability as computers get more sophisticated. In the study [12] by Mukhopadhyay et al., participants’ reflected assessments and self-appraisals were strongly impacted by computer input, but there was no discernible difference in the impact between the two interface types. The study examined human-like and machine-like interaction styles. The use of eye movements in user interfaces for human-computer interaction control and usability study was covered in the chapter by Birbaumer et al. [13]. Eye movements were recorded, analyzed for usability after the fact, and also used in real time as an input method. This demonstrated the promising potential of integrating eye-tracking in human-computer interaction, as it could be used as the only input for users with disabilities or hands-occupied applications, or it could be combined with other inputs. Jaklič et al. in the study [14] discussed video-conferencing systems that were created to enable reliable and effective communication across vast distances utilizing the Internet and personal computers. Eye contact has always been important for this kind of communication. Since cameras are usually mounted above computer monitors, making eye contact can be difficult. This article suggests a straightforward method to improve the perception of eye contact based on how 3D scenes are viewed on slanted surfaces, which is supported by experimental evidence. Despite being inexpensive, these systems have not been widely adopted. Mobile device interface and interaction design has a big impact on cognitive offloading. Compared to less responsive or mouse-based controls, participants offloaded more information while using highly responsive touch-based controls, and subjective measurements suggested that metacognitive judgments were also involved in this process as discussed in [15] by Grinschl; et al.. According to the study [16] by Zaina et al. Barriers to accessibility may be generated while developing mobile applications, frequently as a result of standard software development UI design patterns. These hurdles must be understood in the context of the challenges faced by software experts; recommendations have been put forward to prevent accessibility concerns in mobile development. Two innovative gaze-based predictive user interfaces that are able to dynamically provide adaptive interventions in line with the user’s objectives and intents linked to the task were offered. In addition to managing uncertainty in prediction model outputs, these interfaces preserve usability and do not negatively impact perceived job burden as mentioned in the study [17]. A cross-stitch technique was used to construct a wearable tactile sensor array made of fiber that was incorporated into human-machine interfaces in the study [18] by Zhong et al.. The system, which included sound feedback components, microprocessors, and sensors, allowed for an interactive process. To speed up reaction times and enhance operational behavior, a quick signal prediction technique was suggested. Many social media, websites and applications are introducing the dark mode as an additional feature. Within just a span of a couple of years, almost every popular website, app and even operating systems now have dark mode as their integral feature. The e-learning sites are also adopting this feature. But the thing that is different about the e-learning sites is that they are the replacement of books and copies. While on the other sites, it might be for many different use cases, in e-learning sites, the main purpose is to learn and teach. While this learning teaching methodology has been long practiced with the help of paper books and copies, the introduction of screens in their replacement will surely have impacts on their users and uses. The dark mode replaces the typical dark text on white background view to white text on dark background view. So, this research is the study about the impacts of dark mode on HCI in students. Our hypothesis is that, for those students who use the computer screen for longer duration for study, dark mode helps to reduce eye strain and motivates the students to study for a longer period of time in comparison to normal light mode. In this paper, we discuss and investigate this topic on an example of the MOOC system which is used in our university as an online learning platform.

Inverse synthetic aperture radar (ISAR) imaging of sparse aperture is a challenging problem. Noting the ISAR image generally exhibits strong sparsity, compressive sensing (CS) and sparse signal recovery methods are widely applied for sparse aperture data. However, the existing sparse aperture ISAR imaging algorithms are either computationally heavy or require manual adjustment of too many parameters, which limits their applications in the real-time ISAR imaging system. This paper proposes a high precision and computationally efficient ISAR imaging algorithm for sparse aperture. A generalized CS model with log-sum minimization is first considered for ISAR imaging, and then the iterative log-sum thresholding (ILT) algorithm is utilized to solve the optimization problem, where no large-scale matrix inversion is performed for reducing the computational overhead. Specifically, to accurately estimate ISAR image sparsity and avoid manually tuning parameters, a novel adjustment strategy of the trade-off parameter λ is applied for the ILT algorithm. To analyze the influence of different measurement matrix on the imaging algorithm, four sparse sampling patterns, including centralized sampling (CES), gap missing sampling (GMS), equally space sampling (ESS), and random missing sampling (RMS), are analyzed based on the mutual incoherence property (MIP). Experiments based on both simulated and measured data validate that the proposed algorithm can achieve well-focused ISAR images with a few seconds and is very efficient to implement.

In the rapidly evolving landscape of wireless communication, securing transmitted information remains a critical challenge. This study provides a comprehensive review of secret key generation (SKG) techniques designed to secure physical layer wireless communication channels. We focus on key generation methods that exploit physical layer characteristics such as channel state information, received signal strength, and bit error rate. The computational complexity of these techniques is also examined, emphasizing the trade-offs between security and efficiency for real-world implementations. The review identifies open challenges such as developing low-cost, resource-efficient systems, managing artificial noise, and addressing the impact of mobility and multiuser massive MIMO systems on SKG rates. Our findings aim to guide the development of more efficient, scalable SKG methods for next-generation wireless networks. Additionally, we propose a method for securely generating identical secret keys between nodes using Cuckoo filters and a Map-Reduce model for frame hashing, ensuring long-term security in IoT environments.

This review paper explores advanced methods to prompt Large Language Models (LLMs) into generating objectionable or unintended behaviors through adversarial prompt injection attacks. We examine a series of novel projects like HOUYI, Robustly Aligned LLM (RA-LLM), StruQ, and Virtual Prompt Injection, that compel LLMs to produce affirmative responses to harmful queries. Additionally, the paper investigates the robustness of these attacks across different models and prompts. Several new benchmarks, such as PromptBench, AdvBench, AttackEval, INJECAGENT, and RobustnessSuite, have been created to evaluate the overall performance and resilience of LLMs against adversarial prompt injection attacks. Results show significant success rates in misleading models like Vicuna-7B, Llama-2-7B-Chat, GPT-3.5, and GPT-4. The review highlights limitations in existing defense mechanisms and proposes future directions for enhancing LLM alignment and safety mechanisms such as LLM SELF DEFENSE, Unlike previous studies, this paper provides a more comprehensive evaluation by integrating a broader range of attack methods and target models. Our study shows that there is a need for improved robustness in LLMs, which will potentially shape the future of AI-driven applications and security protocols.

In this paper, a new control algorithm was developed for a body weight support system (BWS). BWSs are used for rehabilitation purposes and to help patients regain the ability of walking after conditions like stroke or other neurological disorders. For designing the controller, different approaches, model predictive control; classical PID control and LQR control, have been used and the results are compared.

The pursuit for industrial energy efficiency and the mitigation of greenhouse gas emissions motivate researchers to develop or restructure energy materials that can lower the global energy carbon footprint. This study hybridizes three basic Triply periodic minimal surface topologies using into one shape. A novel heat exchanger is proposed by hybridization of 3 basic topologies; Schoen Gyroid, Schwarz Diamond and Fischer Koch S to leverage and combine their individual thermo hydraulic properties. The aluminium alloy used is a thin walled AlSi10Mg. Hybridization was achieved by level set trigonometric functions and modeled by MSLattice. The new shape was simulated as an evaporator using Simulink, MATLAB 2020a running the Tetrafluoroethane (R134a) on the material with a geothermal (sustainable energy) heat source. The highest surface area density is 29.59 l/mm with a wall thickness of 0.12 mm. The best obtained steady state values of specific enthalpy and heat flow rates are 439.4 kJ/kg and 0.34 kW/s respectively. This novelty sheds some light into the 2 phase 3 zone evaporator’s zone length and mass fraction of phase transformation and zonal fluid mass along the volume length, promising better thermo hydraulic results as more Triply periodic minimal surfaces are hybridized.

The assessment of neutron-induced displacement effects in electronic components is a subject combining many aspects. First of all, the material of the electronic component is an important element. In addition, the physics of neutron interactions varies depending on the energy domain. Displacement cross sections are usually estimated using two main methods based on either displacement energy or displacement number. Notably, neutron testing standards for electronics use displacement energy. Numerical modelling, which describes matter very precisely, uses mainly the number of defects. In this work, we propose to establish a link between the two displacement cross-sections. Geant4 is used for the statistical treatment of neutron transfer in secondary ion populations. The Iradina tool is then run for each event to translate each secondary ion trajectory into a displacement number. Classically, an analytical method is used to convert ion populations into displacement energies. In our case, a Monte Carlo approach is applied from the incident neutron to the defects. Components in Si and GaAs technology have been the most extensively used and studied. GaN and S iC technologies are also good candidates. Thus, these four materials are studied. Various di splacement characteristics are given. Finally, equivalence factors for the neutron spectra used for testing are proposed.

The rich-club phenomenon describes the tendency of well-connected nodes to form highly cohesive cores within networks, offering insights into their fundamental structure. This paper presents the Rich-Club Integrated (RCI) model, developed for predicting missing edges in large and complex bipartite networks, which is particularly relevant for product recommendation systems. By incorporating the rich-club coefficient, RCI demonstrates enhanced performance compared to models that do not account for this phenomenon. This study highlights the practical utility of the rich-club coefficient in enhancing recommendation systems and encourages further exploration of this approach in network analysis.

Modular-Multilevel-Cascade Converters are the preferable choice for high-voltage and high-power applications. The price of simple scalability, modularity, and flexibility is paid by a large number of components, especially semiconductors and capacitors. Such components are prone to failure, so fault detection and diagnosis, fault-tolerant control concepts, and reliability analysis are research fields within this topology. This paper presents several fault-tolerant approaches for the class of Modular-Multilevel Converters under the severe failure of a whole cluster and compares them in detail. Furthermore, the different fault modes are emphasized, and operation recommendations are given. All approaches are extensively derived and verified by simulation and experimental results in a steady-state. Additionally, experimental results of transients, i.e., the transition from normal-to fault-operation and power steps, are provided for the most suitable proposed approach. The results show the proper functionality of the control strategies. The simple implementation enables easy integration into (existing) systems and thus provides better fault-tolerance.

The paper presents insights gained over two years, working on an Australian Commonwealth Government-sponsored research project based on a Smart City ontology. The research focused IoT ecosystem enables the study of the range and scale of services that can be delivered to a multifaceted IoT environment. Custom-engineered, Sensor nodes, generate parametric measures from fixed or mobile locations, 24hours a day, for 365days a year. Case-1, with public health and safety as the key objective, as an example and demonstrates how lessons learned were applied to other use cases, using a flexible and configurable IoT ecosystem. A Semantic framework, based on the researched ontology and operational process, is presented. The time-series data is stored on an "automated" database server, custom-configured to routinely "collect", "clean" data using predictive analytics to maintain data integrity, store and present the data securely for research. Several techniques are discussed in the paper to fuse different parametric measures using logic and analysis "at the edge", to integrate all compute hardware or cloud capability, using OTA (over the air) techniques. The data collected presents researchers with a rich resource from which to extract, amongst other insights, traffic patterns, weather relationships, risk management dependencies, health risk considerations, time sync error issues that are central to the integrity of any time-sensitive operation. This paper specifically investigates the technology, the process, techniques such as business process workflows, semantic modelling, data analysis, optimisation of tasks, the effect of transmission delays on the results of analysis, predictive techniques, ontegration via APIs and the concept of the "digital twin" to study the operational effectiveness of the Smart City IoT ecosystem.