Miracle-dz/UniFAR-FTUs huggingface dataset:
Severe 21st-century ocean acidi ﬁ cation in Antarctic Marine Protected Areas

Antarctic coastal waters are home to several established or proposed Marine Protected Areas (MPAs) supporting exceptional biodiversity. Despite being threatened by anthropogenic climate change, uncertainties remain surrounding the future ocean acidi ﬁ cation (OA) of these waters. Here we present 21st-century projections of OA in Antarctic MPAs under four emission scenarios using a high-resolution ocean – sea ice – biogeochemistry model with realistic ice-shelf geometry. By 2100, we project pH declines of up to 0.36 (total scale) for the top 200 m. Vigorous vertical mixing of anthropogenic carbon produces severe OA throughout the water column in coastal waters of proposed and existing MPAs. Consequently, end-of-century aragonite under-saturation is ubiquitous under the three highest emission scenarios. Given the cumulative threat to marine ecosystems by environmental change and activities such as ﬁ shing, our ﬁ ndings call for strong emission-mitigation efforts and further management strategies to reduce pressures on ecosystems, such as the continuation and expansion of Antarctic MPAs.
Design of Ultra-Wideband Optical Frequency Comb and Its Implementation in Coherent WDM Systems

: Conventional wavelength-division multiplexing (WDM) systems use a bank of semiconductor lasers at the transmitter side to carry channels data and may use a similar bank at the receiver side to demodulate the data coherently. The power and frequency spacing of the laser’s bank should be controlled carefully leading to an increase in the cost and reducing the robustness of the WDM transmitter and receiver. To solve these limitations, an optical frequency comb generator (OFCG) has been proposed in this research to replace the laser banks in WDM systems. This work faces the challenge of how to design an ultra-wideband (UWB) comb with small size and easy frequency tunability to satisfy the requirements of advanced high-bit rate based-WDM systems. This challenge is addressed in this paper. Firstly, the design of an electrooptic modulator (EOM)-based UWB comb is presented. The comb consists of two parallel single-drive push-pull Mach-Zehnder modulators (MZMs) cascaded with an electroabsorption modulator (EAM) and can offer 25, 31, and 39 optical lines with 0.5, 1, and 3 dB-flatness spectra, respectively. The line frequency spacing ∆f of 50, 100, and 150 GHz are reported without affecting the number of generated comb lines. Three of the designed combs are used to generate a 72-line comb of 100 GHz frequency spacing to support an extended C-band WDM system. Then the proposed comb configurations are used in dual-polarization (DP) 16-QAM coherent WDM systems to replaces the laser bank at both the transmitter and receiver sides. The transmission performance of the designed WDM systems is simulated using Optisystem software ver.15. Simulation results are recorded for different values of symbol rate Rs (40, 80, and 120 GSps) which correspond to 0.8 ∆f, and for a different number of multiplexed channels (8, 16, 32, and 72). The simulation results indicate that the 32-and 72-channel comb-based WDM system can carry a data rate of 30 and 46 Tbps when the comb is designed with ∆f =100 and 150 GHz, respectively.
Lightweight Substitution Box Using Elliptic Curve Cryptography for Image Encryption Applications

This article proposes a Substitution box (S-box) in the light of Elliptic Curve Cryptography (ECC) for efficient image encryption. Conventional S-box design is accomplished by various methods having diverse benefits and drawbacks. Nonlinear equations and chaos theory are the most eminent techniques for the S-box design. One of the techniques used for the S-box design and user authentication is the ECC. There are various ECC-based S-boxes in the literature. However, majority of them are designed by adhering to intricate schemes generating a computational burden for multimedia devices. It is observed that a comparative degree of security can be obtained by parameter selection rather than following enigmatic and hybrid cryptographic schemes. In fewer computations, the proposed S-box has achieved an equivalent level of security. When compared to other ECC-based S-boxes, the proposed lightweight S-box exhibits a similar level of nonlinearity as well. Finally, the proposed S-box is used in practice to encrypt images, and the statistical analysis of the encrypted images likewise produces excellent results. A comparison between various S-box encrypted images and the proposed S-box encrypted images has been carried out for further evaluation. Together all these features make the proposed lightweight S-box a much better option in the present resource-constrained Internet of Things (IoT) era.
Independent and Combined Effects of Prenatal Alcohol Exposure and Prenatal Stress on Fetal HPA Axis Development

Prenatal alcohol exposure (PAE) and prenatal stress (PS) are highly prevalent conditions known to affect fetal programming of the hypothalamic-pituitary-adrenal (HPA) axis. The objectives of this study were to assess the effect of light PAE, PS, and PAE-PS interaction on fetal HPA axis activity assessed via placental and umbilical cord blood biomarkers. Participants of the ENRICH-2 cohort were recruited during the second trimester and classified into the PAE and unexposed control groups. PS was assessed by the Perceived Stress Scale. Placental tissue was collected promptly after delivery; gene and protein analysis for 11β-HSD1, 11β-HSD2, and pCRH were conducted by qPCR and ELISA, respectively. Umbilical cord blood was analyzed for cortisone and cortisol. Pearson correlation and multivariable linear regression examined the association of PAE and PS with HPA axis biomarkers. Mean alcohol consumption in the PAE group was ~2 drinks/week. Higher PS was observed in the PAE group (p < 0.01). In multivariable modeling, PS was associated with pCRH gene expression (β = 0.006, p < 0.01), while PAE was associated with 11β-HSD2 protein expression (β = 0.56, p < 0.01). A significant alcohol-by-stress interaction was observed with respect to 11β-HSD2 protein expression (p < 0.01). Results indicate that PAE and PS may independently and in combination affect fetal programming of the HPA axis.
Smart Attendance System using Face Recognition

Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.
Energy-Efficient 3D Trajectory Optimization for UAV-Aided Wireless Sensor Networks

In non-terrestrial networks (NTN), optimal planning of the optimization problem of 3-dimensional (3D) trajectory is a key research topic. In this article, the optimization problem of the unmanned aerial vehicle (UAV) aided wireless sensor networks is addressed. To maximize the energy efficiency (EE) performance, we formulate the 3D trajectory optimization problem as a non-convex optimization and divide it into two sub-problems, the UAV's horizontal trajectory optimization problem with given altitude and the UAV's altitude optimization problem with given horizontal location. By combining with the discrete linear state-space approximation method, the energy-efficient algorithm with given transmit power of each sensor is proposed. Numerical results show that the proposed methods achieve significant improvements compared to the existing; EE schemes.
Application of 3-D Ground Laser Scanning Technology to Measure The Scenic Spot of The Best Spring in the World

This paper introduces the main types of 3-D ground laser scanners in the field of surveying and mapping, provides a technical scheme of applying 3-D laser scanning technology to measure ancient buildings in scenic spots, and uses the pulsed 3-D laser scanner which can transmit and receive infinite echoes to solve the problem that vegetation in the surveying area seriously blocks buildings. As an example, the feasibility of the proposed technical scheme is confirmed and the reliability of the proposed technical scheme is demonstrated by precision verification experiments at the scenic spot of the best spring in the world.
FACTORS INFLUENCING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN ACCOUNTING AMONG MICRO, SMALL MEDIUM ENTERPRISES (MSMES)

This study investigated the factors that influence the adoption of artificial intelligence (AI) in accounting among Malaysian Micro, Small Medium Enterprises (MSMEs). It focused on three factors based on the Technology, Organization, and Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory. The factors examined include compatibility, complexity, security and privacy, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations. A quantitative approach was employed to collect data which were then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) software. The study’s findings indicate that compatibility, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations significantly impact AI adoption in MSMEs. However, the study did not find significant influences of complexity as well as security and privacy on AI adoption of Malaysian MSMEs. In conclusion, this study’s findings offer valuable insights on how organizations can effectively navigate these factors to achieve successful AI adoption in their accounting practices.
Optimizing Fault Ride-Through of DGs in Distribution Networks to Preserve Recloser-Fuse Coordination

Fuse saving protection is usually applied in distribution networks, through auto-reclosing, to prevent fuse burning under transient faults. The modern fault ride-through (FRT) standards, which require distributed generators (DGs) to remain connected during the fault, may lead to the miscoordination of recloser-fuse. This paper proposes a discrete coordinate-descent (DCD) approach that optimizes the FRT of DGs so that the coordination between recloser and fuse is preserved. First, the method performs a short-circuit calculation (SCC) considering the currents contributed by the DGs, according to the FRT standards of each country. Then, if a miscoordination between recloser-fuses is observed, it optimally scales down the DG currents, until the coordination is restored. The proposed method has simple implementation, low communication requirements and offers adequate voltage support during the fault. Simulations were carried out in the IEEE 13-bus and the IEEE 8500-node network to highlight the distinct features of the proposed approach against the state-of-the-art current limiting approaches.
Dynamic Framing and Power Allocation for Real-Time Wireless Communications with Variable-Length Coding

Achieving high reliability and low latency is a critical challenge for a wide range of applications that demand strict performance guarantees, such as real-time systems, industrial automation, and autonomous vehicles. Our primary focus is on ultra-reliable low-latency communication (URLLC), which aims to ensure a real-time requirement. We propose a solution for hard delay-constrained communication, which meets strict latency requirements by incorporating variable-length coding in short-packet transmission systems. Our approach utilizes a cross-layer design using truncated channel inversion transmission across parallel channels. This system can be characterized as a two-dimensional Markov chain, which consists of both the packet queue buffering bits to be encoded and symbol queue buffering coded symbols to be transmitted. By leveraging embedded Markov chains, we formulate an optimization problem to minimize the average power consumption while converting the problem into a one-dimensional Markov chain. We present a heuristic algorithm to obtain hard delay-constrained policies and utilize gradient descent policy to refine the policies and explore the trade-offs between hard delay constraints and power consumption.
Opportunistic Multiple Access for Reliable Minimal Latency Communications

Ultra-Reliable Low Latency Communication (URLLC) remains a daunting challenge toward 6G, of particular interest in use cases involving machine-to-machine communications and Extended Reality (XR) applications. Proactive communication was proposed to effectively establish URLLC for minimal latency. In this paper, we further innovate opportunistic multi-access of proactive virtual cell communications for smart machines. We employ game-theoretic analysis to explore the fundamental behavior of such a decentralized mechanism. Instead of optimizing throughput in traditional wireless access, maximizing ultra-reliability by a strategy-proof proactive mechanism of multi-cell networking architecture disruptively achieves minimal latency without the need for any additional control overhead, whose impressive performance is further confirmed by numerical evaluations.
Characterization of a Hygro-Regulated Wall Base Ventilation System for Treatment of Rising Damp

The treatment of rising damp in the walls of historical buildings is very complex, due to the thickness and heterogeneity of the walls. The techniques traditionally used for dealing with this problem (such as watertight barriers, injection of hydrofuge products, etc.) have sometimes proved ineffective, and that is why it is necessary to find a new approach. In recent years, the Building Physics Laboratory at the Faculty of Engineering, University of Porto has been conducting experimental research on the effectiveness of the wall base ventilation system, using natural or mechanical higro-regulate systems to reduce the level of the damp area. This experimental research and the simulations that were performed, clearly show that wall base ventilation is a system with potential. This paper presents the characterization of the hygro-regulated systems operation based on experimental studies developed in laboratory, which allowed the influence of the velocity of the air, condensation risk and the possibility of salt crystallization.
Scheduling of Residential Appliances for Demand Response Considering Customer Satisfaction

Energy management systems (EMSs) must adapt in real-time to the electricity market, which has an everchanging element, to make the best judgments possible. Demand response (D.R.) is the strategy in distributed side energy management systems to increase efficiency and stability by scheduling the load properly. The energy Scheduling issue for residential consumers is examined in this study; two types of load are taken in this paper; the first class of appliance is fixed load while the other is flexible load. Customers can change flexible loads according to the hourly price to lower the total payment by reducing the total power consumption. This paper also considers customer dissatisfaction while lowering the total power consumption and establishes a trade-off between total payment and satisfaction cost using the weight sum method. The simulation result demonstrates that the D.R. algorithm minimizes total payment as well as dissatisfaction costs which help users to schedule the applinaces, for benchmarking the result is compared to the results without D.R.
AirJSCC: Reconfigurable Intelligent Surfaces Based Over-the-Air Joint Source-Channel Coding

Reconfigurable intelligent surface (RIS) is a programmable metasurface composed of sub-wavelength meta-atoms and a controller. Recent research works have shown that multi-layer RISs can form over-the-air neural networks. This enables support for a variety of tasks, including image recognition, mobile communication coding-decoding, and real-time multi-beam focusing. In this work, we present a RISs-based over-the-air joint source channel coding (JSCC) scheme, named as AirJSCC, where multi-layer RISs are used to realize wave-based complex-valued neural networks (CVNNs). AirJSCC transforms the computation process inherited in JSCC into the transmission of wireless signals through RISs, and thus many benefits, such as light-speed computation and high parallel processing capability, can be achieved. To the best of our knowledge, this is the first deep JSCC scheme that incorporates RISs and CVNNs. Simulation results demonstrate that AirJSCC achieves better image reconstruction performance compared with the baseline scheme, even under low signal-to-noise ratio (SNR) and limited bandwidth, and exhibits robustness against varying channel conditions.
Enhanced Federated Reinforcement Learning for Mobility-Aware Node Selection and Model Compression

Federated Learning (FL) is an emerging distributed learning architecture that allows multiple agents to share knowledge in machine learning. However, in the scenario with mobile agents, the mobility of agents significantly affects the learning performance. Besides, frequent exchange of local models could result in high communication overhead. In this paper, we propose a Mobility-aware Federated Reinforcement Learning (MFRL) framework. In MFRL, we model the influences of agent mobility on communication quality and data correlation, and devise a mobility-aware node selection algorithm, so as to accelerate the training procedure and improve the learning performance, taking learning quality, wireless channel quality, and data correlation of agents into consideration. A knowledge distillation (KD) based model compression method is integrated into the MFRL to reduce the communication overhead as well as accelerate the inference process. Finally, taking deep reinforcement learning (DRL) based collision avoidance of intelligent vehicles as a study case, the effectiveness of MFRL is verified. Numerical results demonstrate that the proposed MFRL can accelerate the training process and improve the learning performance.
The role of conspiracy mentality, reactance, and anxiety in the effectiveness of gain- vs. loss-framed messages promoting COVID-19 protective measures: Is vaccination different?

We explore how conspiracy beliefs change the effectiveness of gain- vs. loss-framed messages in promoting health-protective behavior. We focused on various recommended COVID-19 protective measures, not only vaccinations but also other preventive (like wearing masks) and detection behaviors (like testing). Our results indicate that conspiracy beliefs moderate the effectiveness of gain vs. loss framing. When participants endorse conspiracy worldviews above the average level, the gain frame may be more effective than the loss frame. In other words, in the loss frame condition, conspiracy beliefs negatively and significantly predicted attitudes toward the behavior recommended. However, in the case of the gain frame, the relationship between conspiracist views and attitudes toward promoted behavior was weaker or even nonsignificant. We also found, although only in the case of one behavior, that when participants' eagerness to look for conspiracies almost does not exist, the loss frame may be a better option than the gain frame. Finally, neither of these effects was mediated by emotional reactance or anxiety.
Privacy-Preserving Mutual Authentication Protocol for Drone Delivery Services

Drones are becoming popular in a variety of applications. One of them is to collect packages from sellers and deliver them to buyers who are connected through a market-place platform. However, drones are also vulnerable to cyber-attacks. Drone delivery service also brings up privacy concerns about the personal information of users of the marketplace. This paper addresses such security and privacy threats and proposes a privacy-preserving authentication protocol for drone delivery services. The proposed protocol is built on privacy-preserving Decentralized Identifiers (DIDs) and Verifiable cre-dentials (VCs). The use of DID helps marketplace users to preserve their privacy rights and enables them to request drone services in a privacy-preserving manner. At the same time, their legitimacy can be verified by the drones using VCs. The proposed protocol also incorporates an efficient dynamic revocation mechanism to remove the marketplace users from the service subscription if required.
Analysing the Effect of the Vehicle's Length on the Aerodynamic Drag

In overall car performance and ride stability, external car aerodynamic study is of great importance, making it a key element in effective automobile design. In this study, the effect of the vehicle's length on the drag coefficient was numerically investigated. For this purpose, a CFD analysis based on RANS turbulence models was carried out for six configurations for the Ahmed body model with different length in addition to the baseline model. Tetrahedral cells were adopted throughout air enclosure except some prism cells around the vehicle's surfaces. Good agreement for the benchmark model was obtained by comparing current numerical results with experimental related data. The numerical results demonstrate that 1244mm is the optimal length of the Ahmed body. Increasing the overall length of the Ahmed body by about 19.15% leads to decreasing drag coefficient by 8.95%.
Understanding AI: Interpretability and Transparency in Machine Learning Models

Artificial intelligence (AI) is recognized as a valuable tool in various healthcare uses for diagnosing and therapeutic decision-making. Due to the tremendous rise in accessible data and processing capacity, machine learning (ML) models have performed well or greater than doctors in numerous activities. The AI platform needs to be transparent, resilient, and interpretable to adhere to the principles of trusted AI. Present ML systems are alluded to as black boxes because of the absence of understanding of the mechanics related to the decision-making procedures. As a result, before ML can be implemented into ordinary healthcare processes, its transparency and interpretability must be understood. To address this issue, this study presents a method for understanding the transparency and interpretability of ML suggestion systems. We particularly adapt the suggested technique to a chronic condition that is frequent in seniors: heart disease. The suggested approach illustrates the fundamental cause for these suggestions and increases patient trust and interpretability of ML models by assessing the influence of various patient features on the suggestions.
Noise Reduction via Low Rank Tensor Decomposition for MIMO ISAC Systems

Sensing function in integrated sensing and communication (ISAC) system concentrates on collecting and extracting information of the targets from noisy observations, which can assist positioning the users and enable a precise directional communication link. This paper deals with noise reduction via tensor ring (TR) decomposition and total variation (TV) for linear frequency modulated continuous-wave (FMCW) signals in the multiple-input multiple-output ISAC system. Specifically, TR decomposition is used to exploit the low-rankness and describe the global correlation among different dimensions of the high-order received signal. The noise suppression is addressed by the integration of a TV regularization and a Frobenius norm term to ensure sufficient signal-to-noise ratio (SNR). The corresponding optimization problem is solved using augmented Lagrange multiplier (ALM) and proximal alternating minimization. Simulation results illustrate that the proposed method improves denoising performance, leading to a higher output SNR of the target and a better detection probability.