Fish mortality is a significant issue in mariculture, affecting productivity and sustainability. Predicting mortality risk in real-time is crucial for improving decision making and operational efficiency in mariculture management. This paper presents the development of a real-time fish mortality risk prediction model, designed as part of a Decision Support System using the Random Forest machine learning algorithm. The innovative aspect of this study lies in the real-time processing of sensor data to deliver daily mortality risk predictions, allowing for immediate adjustments to management practices. This study integrates water quality parameters (seawater temperature, salinity, conductivity, chlorophyll-a, turbidity, and dissolved oxygen) monitored through a sensor network, with daily fish mortality records input by farmers. The Random Forest model predicted fish mortality risk across five levels with an overall accuracy of 78.6 % and precision exceeding 70 % for each level. The model's feature importance analysis highlights seawater temperature, salinity, and turbidity as key predictors of fish mortality risk. This system supports fish farmers and site managers in daily operational decision making, particularly regarding feed and labor management. Future improvements in data collection and continuous model updates are expected to enhance the accuracy and utility of the Decision Support System in mariculture management.
We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.
Soft fracture in highly deformable solids involves both geometric and constitutive nonlinearities, necessitating advanced theoretical and computational frameworks for its accurate understanding. Tensile fractures subjected to mixed-mode loading deviate from their original planar shape, resulting in echelon crack patterns. When out-of-plane shear is superimposed, a crack front segments into an array of tilted facets. The physical interpretation of echelon cracks is only marginally understood, and it is typically based on rather limited approaches within Linear Elastic Fracture Mechanics. Here, we investigate mixed-mode I + III fracture within the framework of configurational mechanics. Using the Configurational Force Method, implemented as a post-processing algorithm in a finite-element-based simulation, we compute the configurational forces that act at the crack tip of model fracture geometries prior to propagation. Configurational forces characterize both the magnitude and direction of propagation for maximal energy release rate. Our results reveal the complex interactions between tilted facets and their critical role in shaping the fracture morphology. We also examine the effects of facet coalescence-driven by the growth of the parent crack-where neighboring facets merge into a unified crack front. These findings provide new insights into fracture processes in soft, quasi-brittle materials under mixed-mode loading.
Terahertz (THz) wireless networks assisted by unmanned aerial vehicles (UAVs) can enable high-speed, line-of-sight (LoS) wireless communications using directional antennas in the THz bands, specifically for 6G network applications. However, such networks also suffer from the challenge of sensitive information leakage once an attacker resides in the signal beam. We investigate joint transmission probability and power optimization for wireless covert communications in a UAV-aided THz wireless network (UTWN) composed of a transmitter, a UAV relay, a receiver, and two UAV wardens, where these two wardens attempt to detect the presence of wireless transmissions across two respective hops. Specifically, we first derive the minimum detection error probability (DEP) associated with information transmission probabilities and power at the transmitter and UAV relay. We then model the average covert throughput (ACT) and formulate the maximum ACT as an optimization problem, considering constraints such as covertness requirement, information transmission probability, and power. Additionally, we introduce a heuristic algorithm aimed at solving the optimization problem through the joint optimization of information transmission probabilities and power in two hops. Finally, we present simulation and numerical results to validate our theoretical analysis and also to illustrate the impacts of network parameters on the maximum ACT.Assuming that wardens know or correctly guess the prior probabilities of covert transmissions over two hops in their hypothesis testing, we find that prior probabilities are not always equal to 0.5 when achieving maximum ACT in the UTWN, which indicates that covert communications with general prior probabilities and such wardens need more consideration and discussion.
We consider effect of particle loss on the current in a system of an Aharonov-Bohm ring with an embedded quantum dot. In the system, the particle loss is assumed to occur at the ends of normal conducting lead and the quantum dot. The Keldysh Green’s function method is extended to the dissipative system, the formula of nonequilibrium current is derived. The effect of particle loss on differential conductance is examined numerically. Although the phase shift in differential conductance does not occur in the presence of particle loss, the particle loss at both ends of the lead reduces differential conductance. By contrast, the particle loss at a quantum dot widens the resonant curve of the differential conductance, and reduces the maximum value. In the presence of Coulomb interaction, the Hartree-Fock approximation is employed. Although the differential conductance has double peaks because of the stepwise total occupation number of the quantum dot, the effect of particle loss on differential conductance is the same as that in the absence of Coulomb interaction. The validity of our calculation method is discussed by referring to previous literature.
