Performance Advantages Research Articles

Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/C++ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincaré and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincaré serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincaré and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-in-time compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincaré and SimBio as valuable tools for the modeling community. Poincaré and SimBio are released under the MIT license. Their source code is available on GitHub (https://github.com/maurosilber/poincare and https://github.com/hgrecco/simbio) and can be installed from PyPI or conda-forge.

Democratizing Direct-to-Cell Low Earth Orbit Satellite Networks

The emergent Direct-to-cell Low Earth Orbit (LEO) satellites promise to eliminate cellular coverage dead zones by enabling direct network access to our regular phones and IoTs everywhere on Earth via 4G, 5G, and beyond. However, their large-scale deployment can be impeded by scarce orbital slots in space, a lack of licensed cellular spectrums, and prohibitive capital costs for satellite deployment and operation. This article explores how to enable multi-tenant direct-to-cell LEO satellites as a cost-effective win-win solution for satellite operators, mobile operators, and users. We demonstrate that the state-of-the-art cellular networks' stateful hop-by-hop session-based architecture restricts the LEO satellite multi-tenancy. To remove this technical barrier, we propose MOSAIC [1], an alternative scheme to shift from the hop-by-hop session-based cellular service to pay-as-you-go satellite self-service. MOSAIC lets mobile operators supply their users with one-time digitally signed tokens as "coins" to pay for local satellite access on demand. Any satellite operator's authentic satellite can accept these tokens to serve this user without pre-establishing sessions or contacting mobile operators. Akin to cloud computing, this paradigm lets satellite operators flexibly lease their satellites to more mobile operators or higher revenues and return on investment, and allows mobile operators and users to enjoy competitive satellites for complementary coverage and cost optimizations. Our evaluations with the real satellite data and commodity 3GPP NTN protocol stack validate MOSAIC's scalability and performance advantages compared to the state-of-the-art.

A unique pseudo-eligibility analysis of longitudinal laboratory performance data from a transgender female competitive cyclist

Abstract Background and Aims The Union Cycliste Internationale has deemed transgender female athlete’s ineligible for the female category due to concerns about performance advantages. We conducted a follow-up analysis on laboratory-based performance indicators of sports performance using data from a longitudinally assessed transgender woman athlete undergoing gender-affirming hormone therapy (GAHT). Methods We evaluated laboratory performance indicators in transgender and cisgender women athletes using dual-energy X-ray absorptiometry scanning, handgrip strength measurement, jump testing, and cardiopulmonary exercise. Additionally, we assessed a transgender sub-elite cyclist before and after undergoing GAHT. Results After one year of GAHT, the transgender athlete showed declines in handgrip strength (7–13 %), countermovement jump (23–29 %), and V̇O2max (15–30 %). After 3 months, several performance indicators (absolute handgrip, peak power, relative peak power, average power, relative average power, V̇O2max and relative V̇O2max) were above the mean of cisgender female athletes, while others (Relative handgrip, countermovement jump and relative countermovement jump) were below. Similar trends were observed at 6 months and 1 year. Summary This hypothetical analysis, although with limited evidence, suggests the transgender athlete could compete equitably in elite cycling events within the female category after one year of GAHT. Adjustments based on competition data would ensure fairness. Further analysis after an additional 12 months is recommended to assess the impact of 2 years of GAHT. An outright ineligibility for the female category for transgender women athletes would hinder a true assessment of performance fairness.

Class Incremental Learning Method Based on Dynamic Structure Extension and Feature enhancement

With the advancement and widespread adoption of deep learning models, there has been a growing interest in class incremental learning. This approach aims to continuously learn new classes while retaining the recognition and memory capabilities for previously learned classes within an open and dynamic environment. The primary focus of class incremental learning is on maintaining the ability to learn new classes while mitigating catastrophic forgetting, thus achieving a better balance between stability and adaptability. To address this challenge, we propose an innovative method for incremental class learning that leverages dynamically representations to facilitate more efficient incremental class learning, preserving previously acquired features while adapting to new ones and effectively reducing catastrophic forgetting. Furthermore, we introduce a feature augmentation mechanism to significantly enhance the model's classification performance when incorporating new classes. This approach ensures efficient learning of both old and new classes without compromising the effectiveness of previous models. We conducted extensive experiments on two classes incremental learning benchmarks, consistently demonstrating significant performance advantages over other methods.

Fluorine-doped Na0.65MnO2 with a layer-tunnel structure for high-rate and long-cycle-life sodium-ion cathodes

Sodium-ion batteries (SIBs) exhibit comprehensive advantages in excellent cycling stability and high-rate performance. In this work, we present a novel cathode with a layered-tunnel coexisted structure with F ions, denoted as Na0.65MnO2-xFx. This material was confirmed the coexistence of layered and rod-like tunnel structures by advanced characterization methods. Due to the high specific capacity of the layered structure and the cycling stability of the rod-like tunnel structure, the Na0.65MnO2-xFx (x=0.2) cathode material exhibits outstanding comprehensive performance. At a rate of 0.1 C, the reversible discharge specific capacity is 124.6 mAh g−1, and notably, after 1000 cycles at a high rate of 10 C, the specific capacity is 55.9 mAh g−1 with a capacity retention rate of 98.2 %. This is significantly higher than the 34.1 mAh g−1 and 48.3 % capacity retention of pristine Na0.65MnO2. The introduction of F leads to an increase in the spacing between the oxide layers, the formation of strong metallic bonds and the formation of composite structures, which not only enhances the stability of the transition metal layer structure but also induces complex changes in the microstructure. This approach of ion doping and microstructure optimization provides a fresh perspective for the design and optimization of high-performance materials for SIBs.

Sulfur-driven microbial fuel cells denitrification system for targeted treatment of nitrate-containing groundwater: Performance and mechanism

Sulfur (S0)-driven autotrophic denitrification system has obvious advantages in economy and performance in treating the low carbon/nitrogen groundwater. However, the by-products produced from S0 oxidation will cause disturbance to the water quality. In this study, a dual-chamber microbial fuel cell was driven by S0 (S0-MFC) to separate the S0 and nitrate, the electrons generated by the oxidation of S0 in the anodic chamber transferred to the cathode for nitrate reduction. Under the action of the anode microorganisms, the S0 was directly oxidized to sulfate. The maximum nitrate removal rate (NRR) in the cathodic chamber was 2.31 ± 0.03 mg N/L/h, and the anode electron utilization rate could reached to 19.69 %. Electrochemical results showed that flavin and c-type cytochrome could acted as electron mediators promote the electron transfer processes. The presence of loop current contributed to the enhancement of microbial metabolic activity, thus improving the stability of sludge and NRR. The denitrifiers (Thiobacillus and Denitratisoma) and sulfur autotrophs (Desulfocapsa and Acidithiobacillus) played an important role in the high NRR. A low-cost and pollution-free method for removing nitrate from groundwater was realized in this study.

Understanding the structural relation and electrochemical evolution between ZnGeP2 and ZnSiP2 twin phosphides for advanced Li-ion batteries

Ternary ZnGeP2 with large capacity and suitable plateau is considered to be alternative anode for LIBs, while it suffers heavy-use of Ge raw-material inducing high cost-price thus failure of commercialization. Herein, a unique atomic substitution strategy is proposed by using Si-atom to totally replace Ge site in ZnGeP2. Interestingly, novel ZnSiP2 as twin-analogue is achieved, keeping the great advantages of high ICE (90 %), low plateau (∼0.35 V) and comparable rate performance (4000 mA/g, 629 mAh/g) for LIBs. More specially, such ZnSiP2 twin even exhibits higher thermo-stability (1046 vs 891 °C), larger capacity (1554 vs 1387 mAh/g) and lower voltage-hysteresis (0.28 vs 0.44 V) than ZnGeP2, which are unexpected and attractive for battery-safety and energy improvement. The above unusual phenomena are investigated in-detail, and the results show that owing to the similar radius and electronegativity between Si&Ge, the ZnSiP2 enables the same structural phase with ZnGeP2 matrix thus keeping its electrochemical advantages. Meanwhile, the ZnSiP2 exhibits larger formation energy (−1.24 eV) toward better structural-stability, lighter Si-component to higher capacity, lower Si-lithiated potential oriented smaller voltage-hysteresis, thus showing high performances in LiCoO2//ZnSiP2 full-cell. The above atomic substitution strategy can be easily extended to other good performance yet high cost electrode materials toward application.

Anomaly detection with dual-channel heterogeneous graph based on hypersphere learning

Graph anomaly detection is essential for identifying irregular patterns and outliers within complex network structures in domains like social networks, cybersecurity, finance, and transportation systems. It helps detect security breaches, fraud, and errors, improving decision-making and system reliability. Although current methodologies have advanced the unsupervised detection of graph anomalies, they frequently fail to fully address the nuanced specificities of graph anomalies, such as anomalous nodes, edges, and subgraphs. To overcome this limitation, the study presents MulDualGNN, a unique dual-channel heterogeneous graph anomaly detection framework. MulDualGNN incorporates a global substructure-aware GNN and a local substructure-aware GNN to capture both global and local substructure properties for accurate anomaly detection. Our model incorporates a multi-hypersphere learning target function, which includes macroscopic and mesoscopic hyperspheres. These hyperspheres measure abnormal nodes that deviate from most normal nodes in the entire graph and community structure, respectively. To overcome the model collapse problem in multi-hypersphere learning, our model utilizes the EmbSim similarity function to optimize the training target. The effectiveness and performance advantages of the proposed method are evaluated through extensive experiments on five datasets. The results demonstrate the superior performance of our approach in graph anomaly detection tasks.

Critical Evaluation of the Effect of Outsourcing on Nigeria's Workforce Development: A Content Analysis

This study critically examines the effect of outsourcing on workforce development in Nigeria using a content analysis approach. The research is framed within systems theory, which views Nigeria's workforce development as a complex system influenced by various interconnected components, including outsourcing, economic conditions, education and training, government policies, labor market dynamics, and organizational strategies. The empirical review explores the effects of outsourcing on organizational performance, employee commitment, and competitive advantage in Nigeria. Key findings indicate that outsourcing can lead to cost savings, improved efficiency, and access to specialized expertise, contributing positively to organizational performance. However, significant negative impacts are also identified, including underpayment, job insecurity, violation of workers' rights, diminished employee commitment, and adverse effects on the local economy. Despite the potential benefits of outsourcing, such as cost reduction and technological advancements, the study underscores the need to balance these benefits against the negative impacts on workers. Recommendations include enforcing labor laws to ensure fair compensation, providing equal treatment for core and contract staff, empowering outsourced workers through unionization, enhancing anti-discrimination protections, fostering emotional attachment and commitment among workers, and promoting local job creation to mitigate the adverse effects of outsourcing on the local economy.

Exploring the relationship between caffeine metabolism-related CYP1A2 rs762551 polymorphism and team sport athlete status and training adaptations

BackgroundThis study aimed to achieve a dual objective: to compare the frequencies of CYP1A2 rs762551 genotypes between team sport athletes and a control group, and to determine the association between the rs762551 polymorphism and changes in physical performance after a six-week training program among elite basketball players.MethodsThe study encompassed an analysis of 504 individuals, comprising 320 athletes and 184 controls. For the Turkish cohort, DNA was isolated using the buccal swab method, and genotyping was conducted using the KASP technique. Performance assessments included the Yo-Yo IR2 and 30 m sprint tests. For Russian participants, DNA samples were extracted from peripheral blood, a commercial kit was used for DNA extraction, and genotyping of the rs762551 polymorphism was conducted using DNA microarray.ResultNotably, a statistically significant linear decline in the prevalence of the CC genotype was observed with ascending levels of athletic achievement within team sports (sub-elite: 18.0%, elite: 8.2%, highly elite: 0%; p = 0.001). Additionally, the CA genotype was the most prevalent genotype in the highly elite group compared to controls (80.0% vs. 45.1%, p = 0.048). Furthermore, statistically significant improvements in Yo-Yo IR2 performance were noted exclusively among basketball players harboring the CA genotype (p = 0.048).ConclusionsThe study’s findings indicate that the rs762551 CC genotype is a disadvantage in elite team sports, whereas the CA genotype provides an advantage in basketball performance.

Simulation and experimental study of thermoelectric generators with an axial gradient metal foam heat exchanger

The utilization of metal foam for heat transfer augmentation is regarded as a highly efficient technique, albeit associated with significant pressure losses. To enhance the feasibility of employing metal foam in thermoelectric generators and mitigate the high-pressure drop, we propose an enhancement strategy involving the partial axial filling of gradient metal foam. Both analytical modeling and experimental investigation were employed to evaluate the effects of porosity, pore density, and gradient structure at various filling rates on the overall performance of thermoelectric generators. The results show that arranging metal foam with increasingly high frame density in the direction of fluid flow, rather than adopting increasingly sparse or constant structures, leads to improved voltage uniformity and reduced pressure drop. A positive gradient configuration with a pore density distribution of 5-10-20 PPI yielded the highest net power at 118.3 W, which is 12.5 % higher than that of metal foam with constant 20 PPI. Ultimately, empirical verification substantiates the comprehensive performance advantages of positive gradient configuration. For filling rates of 30 %, 60 %, and 100 %, pressure drop is reduced by 35.9 %, 33.4 %, and 29.2 %, respectively, in comparison to constant 20 PPI metal foam, despite a modest reduction in output power, which remains less than 3 %.

Source-independent quantum secret sharing with entangled photon pair networks.

The large-scale deployment of quantum secret sharing (QSS) in quantum networks is currently challenging due to the requirements for the generation and distribution of multipartite entanglement states. Here we present an efficient source-independent QSS protocol utilizing entangled photon pairs in quantum networks. Through the post-matching method, which means the measurement events in the same basis are matched, the key rate is almost independent of the number of participants. In addition, the unconditional security of our QSS against internal and external eavesdroppers can be proved by introducing an equivalent virtual protocol. Our protocol has great performance and technical advantages in future quantum networks.

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

Digital twin based DDPG reinforcement learning for sum-rate maximization of AI-UAV communications

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs).

A Novel Mamba Architecture with a Semantic Transformer for Efficient Real-Time Remote Sensing Semantic Segmentation

Real-time remote sensing segmentation technology is crucial for unmanned aerial vehicles (UAVs) in battlefield surveillance, land characterization observation, earthquake disaster assessment, etc., and can significantly enhance the application value of UAVs in military and civilian fields. To realize this potential, it is essential to develop real-time semantic segmentation methods that can be applied to resource-limited platforms, such as edge devices. The majority of mainstream real-time semantic segmentation methods rely on convolutional neural networks (CNNs) and transformers. However, CNNs cannot effectively capture long-range dependencies, while transformers have high computational complexity. This paper proposes a novel remote sensing Mamba architecture for real-time segmentation tasks in remote sensing, named RTMamba. Specifically, the backbone utilizes a Visual State-Space (VSS) block to extract deep features and maintains linear computational complexity, thereby capturing long-range contextual information. Additionally, a novel Inverted Triangle Pyramid Pooling (ITP) module is incorporated into the decoder. The ITP module can effectively filter redundant feature information and enhance the perception of objects and their boundaries in remote sensing images. Extensive experiments were conducted on three challenging aerial remote sensing segmentation benchmarks, including Vaihingen, Potsdam, and LoveDA. The results show that RTMamba achieves competitive performance advantages in terms of segmentation accuracy and inference speed compared to state-of-the-art CNN and transformer methods. To further validate the deployment potential of the model on embedded devices with limited resources, such as UAVs, we conducted tests on the Jetson AGX Orin edge device. The experimental results demonstrate that RTMamba achieves impressive real-time segmentation performance.

Predictive State-of-Health (SOH) of lithium-ion batteries throughout their lifecycle based on LSTM neural networks

With the rapid development of new energy sources, the demand for energy storage systems in the power industry has surged. While lithium-ion battery energy storage systems have gained widespread market acceptance due to their performance advantages, concerns regarding battery safety and durability have also escalated. Therefore, accurately predicting the health status of batteries has become a crucial aspect in assessing their safety and durability. State of Health (SOH), as a vital indicator of battery health status, has become a focal point for many researchers aiming to enhance the accuracy of its prediction. Today, owing to the rapid advancements in machine learning and neural networks, data-driven approaches have gradually emerged as one of the primary methods for SOH prediction. This paper proposes a method based on Long Short-Term Memory (LSTM) neural networks, which combines theoretical analysis with neural network models, utilizing authentic battery aging data from NASA to predict the health status of lithium-ion batteries throughout their lifecycle. Experimental results demonstrate that the proposed method exhibits high accuracy.

Predictive State-of-Health (SOH) of lithium-ion batteries throughout their lifecycle based on LSTM neural networks

With the rapid development of new energy sources, the demand for energy storage systems in the power industry has surged. While lithium-ion battery energy storage systems have gained widespread market acceptance due to their performance advantages, concerns regarding battery safety and durability have also escalated. Therefore, accurately predicting the health status of batteries has become a crucial aspect in assessing their safety and durability. State of Health (SOH), as a vital indicator of battery health status, has become a focal point for many researchers aiming to enhance the accuracy of its prediction. Today, owing to the rapid advancements in machine learning and neural networks, data-driven approaches have gradually emerged as one of the primary methods for SOH prediction. This paper proposes a method based on Long Short-Term Memory (LSTM) neural networks, which combines theoretical analysis with neural network models, utilizing authentic battery aging data from NASA to predict the health status of lithium-ion batteries throughout their lifecycle. Experimental results demonstrate that the proposed method exhibits high accuracy.

Unveiling the Nexus: the crucial role of competitive advantage in bridging entrepreneurial marketing practices and sustainable firm performance in small and medium enterprises

Studies about entrepreneurial marketing linked with competitive advantage and sustainability in SMEs are limited in the existing literature. This study therefore aimed to investigate the relationship between entrepreneurial marketing dimensions, focusing on the mediating role of competitive advantage, in ensuring the sustainability of firm performance. Descriptive research design combined with explanatory research to examine the relationships; and stratified sampling with simple random sampling techniques to select the enterprises were used. Data from 387 SMEs in Oromia, Ethiopia were collected and analyzed using structural equation modeling (SEM). The findings indicated significant connections among entrepreneurial marketing practices, competitive advantage, and sustainability of firm performance, with competitive advantage partially mediating the relationships. The finding also underlined SMEs that are entrepreneurial, strategic, and socially responsible are best positioned for long-term success. Practical implications suggested aligning marketing practices with overall strategies, optimizing resource usage, and enhancing innovation to improve competitive advantage and sustain firm performance. From a social perspective, embracing sustainable practices could positively impact, employment, environmental responsibility, economic stability, and consumer perception. The study offers valuable insights for academia, policymakers, and practitioners.

A Local Search Algorithm with Vertex Weighting Strategy and Two-Level Configuration Checking for the Minimum Connected Dominating Set Problem.

The minimum connected dominating set problem is a combinatorial optimization problem with a wide range of applications in many fields. We propose an efficient local search algorithm to solve this problem. In this work, first, we adopt a new initial solution construction method based on three simplification rules. This method can reduce the size of the original graph and thus obtain a high-quality initial solution. Second, we propose an approach based on a two-level configuration checking strategy and a tabu strategy to reduce the cycling problem. Third, we introduce a perturbation strategy and a vertex weighting strategy to help the algorithm be able to jump out of the local optimum effectively. Fourth, we combine the scoring functions Cscore and Mscore with the aforementioned strategies to propose effective methods for selecting vertices. These methods assist the algorithm in selecting vertices that are suitable for addition to or removal from the current candidate solution. Finally, we verify the performance advantages of the local search algorithm by comparing it with existing optimal heuristic algorithms on two sets of instances. The experimental results show that the algorithm exhibits better performance on two sets of classical instances.

Effects of the oxyfluorination of needle coke-based anode materials on the capacities and stabilities of lithium-ion batteries

Needle cokes have emerged as an alternative anode material to graphite due to their high rate performance and cost advantages. Herein, we introduced oxygen and fluorine functional groups to increase the capacity and increase the cycling stability, respectively. Oxyfluorination was used to simultaneously introduce these two heterogeneous elements, and their effective introduction was quickly confirmed at room temperature. Compared with those of pristine needle coke-based lithium-ion batteries, the capacities of the lithium-ion batteries based on oxyfluorinated needle cokes increased by 17.9%, and the cycling stabilities of the modified batteries exceeded 92%. Therefore, introducing oxygen and fluorine functional groups into needle cokes through oxyfluorination is a viable strategy for producing anode materials for lithium-ion batteries with high rates and capacities.