Search Results (105)

Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances, adding noise to training instances, and adding out-of-distribution training instances in the local data to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, the prevention and detection of such data poisoning attacks is crucial to build a robust federated training framework. To address this, we propose a prevention strategy in federated learning, namely confident federated learning, to protect workers from such data poisoning attacks. Our proposed prevention strategy at first validates the label quality of local training samples by characterizing and identifying label errors in the local training data, and then excludes the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on both the image and audio domains, and our experimental results validated the robustness of our proposed confident federated learning in preventing the data poisoning attacks. Our proposed method can successfully detect the mislabeled training samples with above 85% accuracy and exclude those detected samples from the training set to prevent data poisoning attacks on the local workers. However, our prevention strategy can successfully prevent the attack locally in the presence of a certain percentage of poisonous samples. Beyond that percentage, the prevention strategy may not be effective in preventing attacks. In such cases, detection of the attacked workers is needed. So, in addition to the prevention strategy, we propose a novel detection strategy in the federated learning framework to detect the malicious workers under attack. We propose to create a class-wise cluster representation for every participating worker by utilizing the neuron activation maps of local models and analyze the resulting clusters to filter out the workers under attack before model aggregation. We experimentally demonstrated the efficacy of our proposed detection strategy in detecting workers affected by data poisoning attacks, along with the attack types, e.g., label-flipping or dirty labeling. In addition, our experimental results suggest that the global model could not converge even after a large number of training rounds in the presence of malicious workers, whereas after detecting the malicious workers with our proposed detection method and discarding them from model aggregation, we ensured that the global model achieved convergence within very few training rounds. Furthermore, our proposed approach stays robust under different data distributions and model sizes and does not require prior knowledge about the number of attackers in the system. Full article

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent ${\mathrm{CO}}_2$ emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated ${\mathrm{CO}}_2$ emissions by more than 26%. Full article

A swarm of unmanned aerial vehicles (UAVs) often rely on exceptional wireless coverage of embedded or flush-mounted antennas or arrays, especially in long-range communication. While arrays offer significant range and beam steerability control, they often suffer from size, weight, and power (SWaP) limitations. On the other hand, achieving a wideband, high-gain, and beam-steerable response from a single antenna is highly desired for its compact SWaP characteristics. In this study, a cube-shaped cavity antenna excited by a monopole feed is designed, fabricated, and measured. The proposed antenna operates from 4.1 to 5.56 GHz with a 30.22% fractional bandwidth and a peak gain of 8 dBi. In addition, a frequency-selective surface (FSS) is developed to replace the metallic faces of the cavity, enabling 360° electronic beam steerability. Thermal analysis of the FSS-based cavity design is conducted to determine its maximum power handling capability, revealing a maximum power handling capability of 1.3 KW continuous. In addition, the maximum rating currents of the FSS diodes can be reached only at 165 W, limiting the maximum power handling to only 165 W in the case of using the diodes used in this analysis. The antenna prototype is successfully fabricated, and the radiation pattern is experimentally measured, showing a strong agreement between the simulated and measured results. The electronic steerability of the proposed antenna indicates its suitability for 5G new radio and UAV applications. Full article

The development of vehicle-to-grid (V2G) technique and the growth of battery swapping stations are expected to enhance the resilience of power networks. However, V2G battery swapping stations exhibit inconsistencies among internal battery packs, where the power capacity is significantly affected by the battery swapping behavior of electric vehicle (EV) users. To address this issue, this paper proposes a secondary frequency control strategy for V2G battery swapping stations that accounts for battery pack heterogeneity. First, a user behavioral model is developed through quantitative analysis of key factors such as economic incentives, time costs, and battery degradation, which is then used to optimize the operation of V2G battery swapping stations. Moreover, active balancing of EV battery energy levels is achieved by incorporating penalty terms into the objective function. Finally, a distributed secondary frequency control strategy based on the consensus algorithm is established to minimize total frequency control loss. Simulation results demonstrate that the proposed strategy effectively meets the secondary frequency control requirements of the power grid. Full article

Laying hens’ metabolism goes through a cyclic process to produce eggs, which requires higher dietary protein and energy in the morning (AM) and higher calcium (Ca) in the afternoon/evening (PM) than the rest of the day. Therefore, poultry scientists are trying to adopt a new feeding strategy called AM/PM or split feeding to precisely meet hen’s requirements more effectively than conventional methods. A 10-week cage layer trial was carried out via a Box–Behnken response surface design to identify the optimal amount of protein, energy, and calcium of the AM/PM diets. There were 13 test treatments with three levels of crude protein (19.6%/18.4%, 20.3%/17.7%, 21%/17%), calcium (3.3%/4.9%, 2.5%/5.7%, 1.6%/6.6%), and apparent metabolizable energy (AME) (12 MJ/kg/11.2 MJ/kg, 12.4 MJ/kg/10.8 MJ/kg, 12.8 MJ/kg/10.4 MJ/kg) for AM/PM diets respectively and a control treatment with industry baseline (CP-19%, Ca-4.1% and ME 11.6 MJ/kg). These are the calculated values of nutrients on a dry matter basis. A total of 364 hens were randomly distributed into 2 dietary treatments where each treatment had 13 replicates (2 hens per replicate cage, 26 hens per treatment). AM and PM diets were swapped out at approximately 8 am and 4 pm each day. Egg production and hen performance were measured daily and weekly, respectively, with egg quality, serum Ca, and nutrient digestibility measured at week 10. AM:PM intake and feed cost were calculated for each treatment. The optimal FCR, feed cost, and AM:PM intake were used to determine Ca, CP, and AME levels. The result showed that 6 out of 13 of our test treatments gave improved FCR compared to the control treatment (p = 0.017). Dietary treatments did not affect overall hen weight and serum Ca and egg quality at week 10, except for the lower yolk color score in the control treatment (p = 0.002). Hens in the experimental treatment, with calcium levels of 1.6% and 6.6%, crude protein (CP) levels of 19.6% and 18.4%, and AME content of 12.4 MJ/kg and 10.8 MJ/kg in the AM and PM diet, respectively, showed the highest apparent protein digestibility (56.6%) compared to the control group (p < 0.05). Similarly, hens receiving a treatment containing calcium at 3.3% and 4.9%, CP at 21% and 17%, and AME at 12.4 MJ/kg and 10.8 MJ/kg in the AM and PM diet, respectively, achieved the highest calcium digestibility (62.13%), while the control treatment yielded the lowest calcium digestibility (p < 0.05). After analyzing the data using the Box–Behnken response surface methodology, we found that (21/17)% CP, (3.3/4.9)% Ca, and (12/11.12) MJ/kg energy in the AM/PM diet gave the optimum performance in terms of lower feed cost and better feed efficiency. The data of AM:PM intake demonstrate that selective feeding occurs in between treatments (p < 0.001) and the degree of selection depends on the difference between the level of nutrients in AM and PM diet. This study revealed that when optimized, AM/PM feeding improves feed efficiency and egg quality of laying hens. Full article

Fossil fuel depletion, environmental concerns, and energy efficiency initiatives drive the rapid growth in the use of electric vehicles. However, lengthy battery charging times significantly hinder their widespread use. One proposed solution is implementing battery swapping stations, where depleted electric vehicle batteries are quickly exchanged for fully charged ones in a short time. This paper evaluates the techno-economic feasibility and optimal design of a grid-connected hybrid wind–photovoltaic power system for electric vehicle battery swapping stations. The aim is to evaluate the viability of this hybrid power supply system as an alternative energy source, focusing on its cost-effectiveness. An optimal control model is developed to minimize the total life cycle cost of the proposed system while reducing the reliance on the utility grid and maximizing system reliability, measured by loss of power supply probability. This model is solved using mixed-integer linear programming to determine key decision variables such as the power drawn from the utility grid and the number of wind turbines and solar photovoltaic panels. A case study validates the effectiveness of this approach. The simulation results indicate that the optimal configuration comprises 64 wind turbines and 402 solar panels, with a total life cycle cost of ZAR 1,963,520.12. These results lead to an estimated energy cost savings of 41.58%. A life cycle cost analysis, incorporating initial investment, maintenance, and operational expenses, estimates a payback period of 5 years and 6 months. These findings confirm that the proposed hybrid power supply system is technically and economically viable for electric vehicle battery swapping stations. Full article

Collision models have attracted significant attention in recent years due to their versatility to simulate open quantum systems in different dynamical regimes. They have been used to study various interesting phenomena such as the dynamical emergence of non-Markovian memory effects and the spontaneous establishment of synchronization in open quantum systems. In such models, the repeated pairwise interactions between the system and the environment and also the possible coupling between different environmental units are typically modeled using the coherent partial SWAP (PSWAP) operation as it is known to be a universal homogenizer. In this study, we investigate the dynamical behavior of incoherent collision models, where the interactions between different units are modeled by the incoherent controlled SWAP (CSWAP) operation, which is also a universal homogenizer. Even though the asymptotic dynamics of the open system in cases of both coherent and incoherent swap interactions appear to be identical, its transient dynamics turns out to be significantly different. Here, we present a comparative analysis of the consequences of having coherent or incoherent couplings in collision models, namely, PSWAP or CSWAP interactions, respectively, for the emergence of memory effects for a single-qubit system and for the onset synchronization between a pair of qubits, both of which are strictly determined by the transient dynamics of the open system. Full article

This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article

The driving range and replenishment problem of electric vehicles have become the main contradictions that interfere with consumers’ purchasing decisions. To alleviate these problems, battery-swap technology has been introduced into the public view. Existing research rarely explores the factors that affect consumers’ decision of purchasing electric vehicles. This article introduces the Technology Acceptance Model (TAM), as well as the Theory of Planned Behavior (TPB) with its extensions and the perceived risk, to construct the structural equation model (SEM) based on TAM and TPB, and studies the influence mechanism of the purchase intention of electric vehicles with a battery-swap mode. A total of 530 valid questionnaires were collected from participants in Beijing, providing a representative sample for the study. The results show that attitude, technological development, perceived behavior control, environmental awareness, and subjective norm have significant positive influences on the purchase intention, and the influences increase in turn; perceived risk has a significant negative effect; subjective norms and environmental awareness have an indirect positive effect. Full article

Mitochondrial carriers transport organic acids, amino acids, nucleotides and cofactors across the mitochondrial inner membrane. These transporters consist of a three-fold symmetric bundle of six transmembrane α-helices that encircle a pore with a central substrate binding site, whose alternating access is controlled by a cytoplasmic and a matrix gate (C- and M-gates). The C- and M-gates close by forming two different salt-bridge networks involving the conserved motifs [YF][DE]XX[KR] on the even-numbered and PX[DE]XX[KR] on the odd-numbered transmembrane α-helices, respectively. We have investigated the effects on transport of mutating the C-gate charged residues of the yeast NAD⁺ transporter Ndt1p and performed molecular docking with NAD⁺ and other substrates into structural models of Ndt1p. Double-cysteine substitutions and swapping the positions of the C-gate charged-pair residues showed that all of them contribute to the high transport rate of wild-type Ndt1p, although no single salt bridge is essential for activity. The in silico docking results strongly suggest that both the C-gate motif mutations and our previously reported M-gate mutations affect gate closing, whereas those of the M-gate also affect substrate binding, which is further supported by molecular dynamics. In particular, NAD⁺ most likely interferes with the cation-π interaction between R303-W198, which has been proposed to exist in the Ndt1p M-gate in the place of one of the salt bridges. These findings contribute to understanding the roles of the charged C- and M-gate residues in the transport mechanism of Ndt1p. Full article

Aiming at the coordinated control of charging and swapping loads in complex environments, this research proposes an optimization strategy for microgrids with new energy charging and swapping stations based on adaptive multi-agent reinforcement learning. First, a microgrid model including charging and swapping loads, photovoltaic power generation, and wind power generation was constructed, and the Markov decision process was used to characterize the stochastic characteristics of new energy power generation, including charging and swapping loads. The deep relationship between uncertainty factors and charging and swapping laws was explored, and an adaptive multi-agent deep reinforcement learning method was used to optimize the random action selection process, improve the convergence speed of the coordinated optimization model, and realize coordinated control of multiple charging and swapping loads. Finally, through the analysis of different scenarios, the effectiveness of the proposed adaptive multi-agent reinforcement learning model for coordinated control of charging and swapping loads was verified. The results show that the proposed method has a faster convergence speed and can effectively optimize the charging process of charging and swapping loads, reducing power fluctuations of the newly connected energy grid. Full article

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45$°$ sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel. Full article

A derivation of a tight-binding model from Schrödinger formalism for various topologies of position-based semiconductor qubits is presented in the case of static and time-dependent electric fields. The simplistic tight-binding model enables the description of single-electron devices at a large integration scale. The case of two electrostatically Wannier qubits (also known as position-based qubits) in a Schrödinger model is presented with omission of spin degrees of freedom. The concept of programmable quantum matter can be implemented in the chain of coupled semiconductor quantum dots. Highly integrated and developed cryogenic CMOS nanostructures can be mapped to coupled quantum dots, the connectivity of which can be controlled by a voltage applied across the transistor gates as well as using an external magnetic field. Using the anti-correlation principle arising from the Coulomb repulsion interaction between electrons, one can implement classical and quantum inverters (Classical/Quantum Swap Gate) and many other logical gates. The anti-correlation will be weakened due to the fact that the quantumness of the physical process brings about the coexistence of correlation and anti-correlation at the same time. One of the central results presented in this work relies on the appearance of dissipation-like processes and effective potential renormalization building effective barriers in both semiconductors and in superconductors between not bended nanowire regions both in classical and in quantum regimes. The presence of non-straight wire regions is also expressed by the geometrical dissipative quantum Aharonov–Bohm effect in superconductors/semiconductors when one obtains a complex value vector potential-like field. The existence of a Coulomb interaction provides a base for the physical description of an electrostatic Q-Swap gate with any topology using open-loop nanowires, with programmable functionality. We observe strong localization of the wavepacket due to nanowire bending. Therefore, it is not always necessary to build a barrier between two nanowires to obtain two quantum dot systems. On the other hand, the results can be mapped to the problem of an electron in curved space, so they can be expressed with a programmable position-dependent metric embedded in Schrödinger’s equation. The semiconductor quantum dot system is capable of mimicking curved space, providing a bridge between fundamental and applied science in the implementation of single-electron devices. Full article

This article estimates the weighted average cost of capital (WACC) for the energy sector in Ecuador, a country with a dollarized economy and illiquid stock markets. Thus, reference companies in the region were taken, and at the same time combined with characteristics of national companies, establishing a useful methodology, which makes sense with the acceptable discount rates in the Ecuadorian economy. For the above, four estimation alternatives were used. In method one, the traditional WACC formula was applied using interest rates and risk premiums from the U.S. market, which resulted in an overestimation due to the double penalty of the country risk and the U.S. market premium. Method two adjusted the market risk premium to consider only the Ecuador-specific risk premium, thus avoiding the double penalty. In method three, the credit default swap (CDS) was used to calculate the country risk premium, and the CDS was excluded from the nominal interest rate, avoiding redundancies. Finally, method four combined the U.S. interest rate with the CDS directly to calculate the market risk premium, more accurately reflecting local economic conditions in a dollarized economy. The WACC results range from 12.63% to 29.70%. In addition, a dummy variable was controlled for during the pandemic period. This article highlights the need for methodologies adapted to emerging markets, since traditional approaches would overestimate the WACC. Full article

Cost reduction and staff retention are important optimization objectives in home healthcare (HHC) systems. Home healthcare operators need to balance their objectives by optimizing resource use, service delivery and profits. Minimizing total travel distances to control costs is a common routing problem objective while minimizing total finishing time differences is a scheduling objective whose purpose is to enhance staff satisfaction. To optimize routing and scheduling, we propose mixed integer linear programming with a bi-objective function, which is a subset of the vehicle routing problem with time windows (VRPTWs). VRPTWs is a known NP-hard problem, and optimal solutions are very hard to obtain in practice. Metaheuristics offer an alternative solution to this type of problem. Our metaheuristic uses the simulated annealing algorithm and weighted sum approach to convert the problems to single-objective problems and is equipped with operators including swapping, moving, path exchange and ruin and recreate. The results show, firstly, that the algorithm can effectively find the Pareto front, and secondly, that minimizing total finishing time differences to balance the number of jobs per caretaker is an efficient way to tackle HHC scheduling. A statistical test shows that the algorithm can obtain the Pareto front with a lower number of weighted sum problems. Full article