Hybrid Strategy Research Articles

The trouble with TESSOC: the coming crisis in British and allied military counterintelligence doctrine

ABSTRACT This article examines the evolution of UK military doctrine on counterintelligence (CI), one of the more consistently troubled aspects of military doctrine in general and intelligence doctrine in particular. We argue that current UK and NATO CI doctrine are in thrall to a deeply problematic defining concept in TESSOC (Terrorism, Espionage, Sabotage, Subversion and Organised Crime) that conflates an intractably diverse assortment of security threats under CI. Furthermore, TESSOC is the latest embodiment of a slow, century-long oscillation between two different basic concepts of CI. The first focuses purely on human threat vectors (referred to here as Human Threat CI or HTCI) while the latter entails a more comprehensive, all-source range of adversary technical and open as well as human source intelligence activities (designated Multidisciplinary CI or MDCI in US doctrine). That oscillation is driven largely by the balance between conventional and asymmetrical operations in defence priorities and recent campaign experience. TESSOC is a legacy of the recent, pre-Russo-Ukraine War emphasis on counterterrorism (CT) and counterinsurgency (COIN) operations. Consequently, UK and allied military counterintelligence doctrine are entering the second quarter of the 21st Century fundamentally ill-equipped to cope with strategic peers and their use of full-spectrum and hybrid strategies.

Genetic introgression from commercial European pigs to the indigenous Chinese Lijiang breed and associated changes in phenotypes

BackgroundGene flow is crucial for enhancing economic traits of livestock. In China, breeders have used hybridization strategies for decades to improve livestock performance. Here, we performed whole-genome sequencing of a native Chinese Lijiang pig (LJP) breed. By integrating previously published data, we explored the genetic structure and introgression of genetic components from commercial European pigs (EP) into the LJP, and examined the impact of this introgression on phenotypic traits.ResultsOur analysis revealed significant introgression of EP breeds into the LJP and other domestic pig breeds in China. Using a haplotype-based approach, we quantified introgression levels and compared EP to LJP and other Chinese domestic pigs. The results show that EP introgression is widely prevalent in Chinese domestic pigs, although there are significant differences between breeds. We propose that LJP could potentially act as a mediator for the transmission of EP haplotypes. We also examined the correlation between EP introgression and the number of thoracic vertebrae in LJP and identified VRTN and STUM as candidate genes for this trait.ConclusionsOur study provides evidence of introgressed European haplotypes in the LJP breed and describes the potential role of EP introgression on phenotypic changes of this indigenous breed.

Optimized low carbon scheduling strategy of integrated energy sources considering load aggregators

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

ML162 derivatives incorporating a naphthoquinone unit as ferroptosis/apoptosis inducers: Design, synthesis, anti-cancer activity, and drug-resistance reversal evaluation

Activating apoptosis has long been viewed as an anti-cancer process, but recently increasing evidence has accumulated that induction of ferroptosis has emerged as a promising strategy for cancer therapeutics. Glutathione peroxidase 4 (GPX4) is one of the pivotal factors regulating ferroptosis that targeted inhibition or degradation of GPX4 could effectively trigger ferroptosis. In this study, a series of ML162-quinone conjugates were constructed by using pharmacophore hybridization and bioisosterism strategies, with the aim of obtaining more active anticancer agents via the ferroptosis and apoptosis dual cell death processes. Of these compounds, GIC-20 was identified as the most active one that exhibited promising anticancer activity both in vitro and in vivo via ferroptosis and apoptosis dual-targeting processes, without obvious toxicity compared with ML162. On one hand, GIC-20 could trigger ferroptosis in cells by inducing intracellular lipid peroxide and ROS accumulation, and destroying mitochondrial structure. In addition to GPX4 inhibition, GIC-20 can also trigger ferroptosis via proteasomal-mediated degradation of GPX4, suggesting GIC-20 may function as a molecule glue degrader. On the other hand, GIC-20 can also induce apoptosis via upregulating the level of apoptotic protein Bax and downregulating the level of anti-apoptotic protein Bcl-2 in HT1080 cells. Furthermore, GIC-20 also enhanced the sensitivity of resistant MIA-PaCa-2-AMG510R cells to AMG510, suggesting the great potential of GIC-20 in overcoming the acquired resistance of KRASG12C inhibitors. Overall, GIC-20 represents a novel dual ferroptosis/apoptosis inducer warranting further development for cancer therapeutics and overcoming drug resistance.

Collagen-Based Bailout Compared to Suture-Mediated Vascular Closure Alone During Transcatheter Aortic Valve Replacement

BackgroundHemostasis for transfemoral transcatheter aortic valve replacement (TAVR) is typically achieved using a suture-mediated vascular closure device (VCD) prior to large-bore sheath insertion (preclosure technique). Recently, the addition of a hybrid closure technique using a preclose technique with the addition of a collagen-plug VCD after sheath removal in cases of failed hemostasis has been utilized. MethodsData were collected from the Northwell TAVR registry, including 3 high-volume TAVR centers. We evaluated a preclose strategy with suture-mediated vascular closure alone (“legacy strategy”) and standard bailout techniques versus a contemporary hybrid strategy of suture-mediated closure with collagen-mediated closure bailout. The primary end point was major or minor vascular complications as defined by the VARC-3 criteria. ResultsA total of 1327 patients were included, of which 791 patients underwent TAVR with suture-mediated closure alone and 536 with contemporary strategy. The primary end point (major or minor vascular complication) was lower in the contemporary strategy (5.44% vs 1.31%; P < .001). Both minor (3.92% vs 1.12%; P = .002) and major (1.14% vs 0.19%; P = .0196) vascular complications were reduced and the total length of stay was less in the contemporary strategy (median of 3 days vs 2 days; P < .0001). Using multivariable analysis, we observed that vascular management strategy significantly improved the composite primary outcome when adjusted for sheath size, peripheral artery disease, carotid disease, and site of procedure. In the contemporary group, bailout collagen-plug VCD with an Angio-Seal (Terumo Medical) was used in 68 patients (12.69%) and bailout MANTA (Teleflex) was required in 4 patients (0.75%). There were no major or minor vascular complications among the patients who received bailout collagen-plug VCD. ConclusionsA contemporary hybrid strategy of suture-mediated closure with collagen-mediated closure bailout reduces the risk of vascular complications among patients undergoing transfemoral TAVR.

The Role of AI in Hybrid Warfare: A Case Study of Pakistan's Cybersecurity Landscape

Technological advancements driven by artificial intelligence (AI) impact social, economic, and military sectors. This research paper explores the relationship between AI and cybersecurity in hybrid warfare. While AI boosts economic development and social betterment, its militarization poses significant risks, especially in asymmetric warfare, where AI-guided attacks can undermine targeted states' economies, infrastructure, and institutions. The paper emphasizes the role of neural network technologies in cyber operations and defense strategies. The absence of legal norms for AI in military actions exacerbates the threat of cyber-terrorism. Focusing on Pakistan's weak cyberinfrastructure, the paper highlights the challenges and opportunities for nuclear-armed states. To strengthen the argument this study implies the case study of Pakistan which faces serious cyber-attacks and cyber operations. Pakistan is known as a theater of Hybrid warfare. So these cyber-attacks and other hybrid strategies can be used against Pakistan to paralyze it.

Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security

Cloud computing represents a transformative approach to delivering IT services via an interconnected network of servers, collectively referred to as the "Cloud." This virtualized environment seamlessly integrates networks, servers, applications, storage, and services, facilitating convenient access for users with minimal administrative overhead. This comprehensive review article centers on two fundamental pillars of cloud computing: virtualization and containerization. It examines their groundbreaking influence on resource management and deployment efficiency. Additionally, the paper explores upcoming trends and challenges expected to shape the cloud computing landscape from 2025 to 2030.An emphasis is placed on the anticipated adoption of hybrid and multi-cloud strategies, providing organizations with tailored solutions while reducing the risks associated with vendor lock-in. The emergence of edge computing is highlighted as a key solution to address latency concerns and foster a competitive environment for the Internet of Things (IoT). Furthermore, the integration of artificial intelligence (AI) and machine learning within cloud frameworks is poised to unlock new avenues of innovation and optimization, propelling digital transformation. The article underscores the critical need for enhanced security measures to protect sensitive data and ensure user privacy. Ongoing price competitions among cloud providers and heightened regulatory scrutiny are also examined, underscoring the dynamic nature of cloud computing. By offering insights into the past, present, and future trajectory of cloud computing, this article affirms its pivotal role in driving digital innovation and empowering organizations to thrive in an interconnected world. In conclusion, the article provides recommendations for businesses to leverage emerging technologies and effectively navigate evolving challenges in the realm of cloud computing.

Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security

Cloud computing represents a transformative approach to delivering IT services via an interconnected network of servers, collectively referred to as the "Cloud." This virtualized environment seamlessly integrates networks, servers, applications, storage, and services, facilitating convenient access for users with minimal administrative overhead. This comprehensive review article centers on two fundamental pillars of cloud computing: virtualization and containerization. It examines their groundbreaking influence on resource management and deployment efficiency. Additionally, the paper explores upcoming trends and challenges expected to shape the cloud computing landscape from 2025 to 2030.An emphasis is placed on the anticipated adoption of hybrid and multi-cloud strategies, providing organizations with tailored solutions while reducing the risks associated with vendorlock-in. The emergence of edge computing is highlighted as a key solution to address latency concerns and foster a competitive environment for the Internet of Things (IoT). Furthermore, the integration of artificial intelligence (AI) and machine learning within cloud frameworks is poised to unlock new avenues of innovation and optimization, propelling digital transformation. The article underscores the critical need for enhanced security measures to protect sensitive data and ensure user privacy. Ongoingprice competitions among cloud providers and heightened regulatory scrutiny are also examined, underscoring the dynamic nature of cloud computing. By offering insights into the past, present, and future trajectory of cloud computing, this article affirms its pivotal role in driving digital innovation and empowering organizations to thrive in an interconnected world. Inconclusion, the article provides recommendations for businesses to leverage emerging technologies and effectively navigate evolving challenges in the realm of cloud computing.

Fusion of AI Techniques: A Hybrid Approach for Precise Plant Leaf Disease Classification

Classification of plant leaf diseases is an important step in protecting the world's food supply and agricultural yields. There has been encouraging progress in improving the efficiency and accuracy of plant leaf disease diagnosis via the combination of deep learning methods with artificial intelligence (AI) in recent years. This research introduces a new hybrid strategy, CNN+SVM and CNN+RF, that uses deep learning techniques like Convolutional Neural Networks (CNN) in conjunction with more traditional machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM). Moreover, two hybrid variants, CNN + RF and CNN + SVM, are proposed to exploit the strengths of both paradigms synergistically. To further improve classification accuracy, the study employs Particle Swarm Optimization (PSO) as a feature selection technique. PSO optimizes the feature subset for each classification model, facilitating the extraction of the most informative features, which leads to better discrimination between healthy and diseased plants. The dataset used for experimentation consists of a comprehensive collection of plant leaf images representing various diseases across multiple plant species. Experimental results demonstrate the efficacy of the proposed hybrid approach compared to individual classification methods. The hybrid models achieve higher accuracy rates and improved generalization performance, showcasing the synergistic benefits of combining AI and deep learning techniques. Furthermore, the feature selection process through PSO contributes significantly to enhancing the classification outcomes, providing insights into the discriminative power of selected features. This research contributes to the advancement of plant leaf disease classification methodologies by offering an innovative hybrid approach that leverages the complementary strengths of AI, deep learning, and feature selection techniques. The study's findings underscore the potential for improving plant leaf disease management strategies, ultimately leading to enhanced crop productivity and sustainable agriculture. The proposed hybrid framework can serve as a blueprint for similar classification tasks in other domains, demonstrating the broader impact of synergizing different AI techniques for improved accuracy and performance. CNN+RF gives 95% accuracy, 93% precision, 96% recall and 94% F1 score, whereas CNN+SVM gives 93% accuracy, 91% precision, 94% recall and 92% F1 score.

Auto encoders with Cellular Automata for Anomaly Detection

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.

Multi-dimensional hybridized TPMS with high energy absorption capacity

Lattice structures based on triply periodic minimal surfaces (TPMSs) have received extensive research attention due to their lightweight, tremendously high ductility, and excellent energy absorption. In this study, the advantages of several types of TPMSs-based lattice structures were integrated by proposing a multi-dimensional hybrid design for the first time. Based on the P, IWP, and FRD structures, one-dimensional (1D), 2D, and 3D hybridizations were successfully achieved by using the Sigmoid function. The technique of powder bed fusion of metal using a laser beam (PBF–LB/M) was utilized to fabricate the designed TPMS structures with Al–Si10–Mg powder. Their mechanical properties under different loading directions were investigated both experimentally and numerically. The results demonstrate that the hybridization strategy effectively modifies the mechanical behaviour of the TPMS structures. When the hybridization direction aligns parallel to the compression direction, the hybrid structure exhibits greater specific energy absorption, while vertical alignment results in a higher Young's modulus. Notably, the 2D hybrid P–FRD structure possesses the most excellent mechanical properties under parallel compression. Moreover, the 1D and 2D hybrid structures exhibit superior specific energy absorption than the uniform structure, with maximum energy absorption of 45.02% and 52.81%, respectively. The proposed hybrid design strategies are expected to accelerate the application of energy-absorbing structures in practical engineering.

Hybrid Strategies Based Seagull Optimization Algorithm for Solving Engineering Design Problems

The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.

A review of integrated battery thermal management systems for lithium-ion batteries of electric vehicles

Lithium-ion batteries have emerged as a promising choice for electric vehicle applications. However, thermal runaway and related catastrophic issues perplex the research community when batteries are subjected to varying charging/discharging and different ambient temperatures. In order to keep the batteries under a safe zone of temperature, battery thermal management occupies utmost importance and hence researchers are switching over to a combination of either two or three strategies since single stragaty could not meet effective thermal management. In a combined strategy, the use of phase change materials and is highly essential due to their inherent thermo-physical properties; both organic and inorganic types have been explored. To enhance the heat dissipation, the phase change materials, regarded as composite phase materials, are being added with graphite powder, nanomaterials, metal foams, and fins are being arranged to the battery cells. The present review enumerates the recent progress made in achieving good thermal performance with hybrid/integrated battery thermal systems with an emphasis on the use of composite phase change materials. The review revealed that the hybrid strategy is performing well, machine learning and advanced optimization methods are being applied to understand the state of health of batteries. Few works are focussed on the mitigation of thermal runaway propagation serving the composite phase change materials as effective flame retardants. The current status and challenges being faced in the use of LIBs is also briefed. It is essential to develop economical integrated battery thermal management systems with parasitic power losses that are compact and safe to attract many city dwellers to adopt pure electric vehicles besides meeting the mandate of sustainable development goals. The review is an attempt to provide a ready reckoner in the area of integrated battery thermal management involving composite phase materials.

Efficiency at Heart: Navigating the Hybrid Single-Ventricle Pathway

BackgroundSingle-ventricle cardiac defects (SVCDs) are among of the most health care resource–intensive congenital diseases. Although SVCDs are traditionally palliated using the Norwood pathway, in the last 2 decades select programs have used the hybrid strategy, which redistributes the operative and interstage risks. This study sought to characterize resource use for a cohort of patients with hybrid-palliated SVCD. MethodsAll patients with SVCDs who underwent palliation with the hybrid strategy and who were followed up exclusively at our institution from January 2008 to December 2021 were included. End points were death, Norwood conversion, orthotopic heart transplantation, 6 months post-Fontan status, or 4 years of age in those patients who had not completed staged palliation. Primary end points included total days hospitalized, number of cardiology visits, echocardiograms, catheterizations, and advanced imaging performed. ResultsOf 135 patients with a diagnosis of SVCD, 72 survived for 6 months after the Fontan procedure. By patient-year for the entirety of the pathway, patients had a median hospital length of stay of 16 days (interquartile range [IQR], 12-25 days), 7 cardiology visits (IQR, 6-9), 8 echocardiograms (IQR, 7-10), and 0.7 catheterizations (IQR, 0.4-1.1). The interstage 1 period had the heaviest resource burden requiring intense cardiology follow-up and echocardiography surveillance. Cardiac catheterizations and advanced imaging were most prevalent during interstage 2 period, with a median of 2 (IQR, 1-2) catheterizations and 36 (40%) patients undergoing advanced imaging. The total median number of hospital days per patient was 63 days (IQR, 47-98.5 days). ConclusionsResource use for the care of patients with SVCDs is significant. The intensity of surveillance decreases as patients progress through the pathway. In comparison with published Norwood pathway data, resource intensity and use patterns in hybrid palliation are comparable.

A Hybrid Strategy Guided Multi-Objective Artificial Physical Optimizer Algorithm

Artificial physical optimizer (APO), as a new heuristic stochastic algorithm, is difficult to balance convergence and diversity when dealing with complex multi-objective problems. This paper introduces the advantages of R2 indicator and target space decomposition strategy, and constructs the candidate solution of external archive pruning technology selection based on APO algorithm. A hybrid strategy guided multi-objective artificial physical optimizer algorithm (HSGMOAPO) is proposed. Firstly, R2 indicator is used to select the candidate solutions that have great influence on the convergence of the whole algorithm. Secondly, the target space decomposition strategy is used to select the remaining solutions to improve the diversity of the algorithm. Finally, the restriction processing method is used to improve the ability to avoid local optimization. In order to verify the comprehensive ability of HSGMOAPO algorithm in solving multi-objective problems, five comparison algorithms were evaluated experimentally on standard test problems and practical problems. The results show that HSGMOAPO algorithm has good convergence and diversity in solving multi-objective problems, and has the potential to solve practical problems.

Curcumin-Piperlongumine Hybrid Molecule Increases Cell Cycle Arrest and Apoptosis in Lung Cancer through JNK/c-Jun Signaling Pathway.

The instability of curcumin's structure and the toxic side effects of piperlongumine have limited their potential applications in cancer treatment. To overcome these challenges, we designed and synthesized a novel curcumin-piperlongumine hybrid molecule, 3-[(E)-4-hydroxy-3-methoxybenzylidene]-1-[(E)-3-(3,4,5-trimethoxyphenyl)acryloyl]piperidin-2-one (CP), using a molecular hybridization strategy. CP exhibited enhanced structural stability and safety compared with its parent compounds. Through in vitro and in vivo biological activity screenings, CP effectively inhibited cell proliferation, caused cell cycle arrest in the G2/M phase, and induced apoptosis. Mechanistically, CP-induced apoptosis was partially mediated by cell cycle arrest. Furthermore, we discovered that CP induces cell cycle arrest and apoptosis through the regulation of JNK signaling. These findings highlight the potential of CP as a promising therapeutic agent for lung cancer treatment.

RNAscope: a novel method for the detection of Heterobilharzia americana ova in canine liver.

Canine schistosomiasis caused by Heterobilharzia americana can lead to severe morbidity and eventual mortality, in part due to the deposition of fluke ova in the liver and gastrointestinal tract, which promotes an influx of peri-ova inflammatory cells. Although fluke eggs can be identified in H&E-stained histologic sections, cases exist in which only fragments of the ova persist, or the egg is obscured by inflammatory infiltrates, which can confound definitive histologic diagnosis. Unfortunately, antibodies specific to Heterobilharzia are not commercially available for immunohistochemical labeling. Therefore, we aimed to use an RNA in situ hybridization strategy to fluorescently label Heterobilharzia ova. Using the H. americana 18S rRNA sequence, we developed an RNA probe and validated its performance on archival formalin-fixed, paraffin-embedded canine tissue. A positive signal was observed for all identifiable ova, fragmented and whole. Use of this methodology could aid understanding of the pathogenesis of H. americana infection in dogs. This technique augments standard diagnostic methodology, enabling spatial colocalization of fluke ova and inflammatory infiltrates when using fluorescent techniques.

Optimizing mycelial protein yield in Pleurotus djamor via ARTP mutagenesis and hybridization strategies

With the world’s population rapidly increasing, the demand for high-quality protein is on the rise. Edible fungi breeding technology stands as a crucial avenue to obtain strains with high yield, high-quality protein, and robust stress resistance. To address the protein supply gap, Atmospheric and Room Temperature Plasma (ARTP) mutagenesis, and spore hybridization techniques were employed to enhance Pleurotus djamor mycelium protein production. Beginning with the original strain Pleurotus djamor JD-1, ARTP was utilized to mutate spore suspension. The optimal treatment time for Pleurotus djamor spores, determined to achieve optimal mortality, was 240 s. Through primary and secondary screenings, 6 mutant strains out of 39 were selected, exhibiting improved protein yield and growth rates compared to the original strain. Among these mutagenic strains, 240S–4 showcased the highest performance, with a mycelial growth rate of 9.5±0.71 mm/d, a biomass of 21.45±0.54 g/L, a protein content of 28.75±0.92%, and a remarkable protein promotion rate of 128.03±7.29%. Additionally, employing spore hybridization and breeding, 7 single-nuclei strains were selected for pin-two hybridization, resulting in 21 hybrid strains. The biomass and protein content of 9 hybrid strains surpassed those of the original strains. One hybrid strain, H-5, exhibited remarkable mycelial protein production, boasting a mycelial growth rate of 26.5±0.7 mm/d, a biomass of 21.70±0.46 g/L, a protein content of 28.44±0.22%, and a protein promotion rate of 128.02±1.73%. Notably, both strains demonstrated about a 28% higher mycelial protein yield than the original strains, indicating comparable effectiveness between hybrid breeding and mutagenesis breeding. Finally, we analyzed the original and selected strains by molecular biological identification, which further proved the effectiveness of the breeding method. These findings present novel insights and serve as a reference for enhancing edible fungi breeding, offering promising avenues to meet the escalating protein demand.

Fast hybrid islanding detection for DGs with inverters using maximum likelihood-based ROCOF and SFS

The present study introduces an unconventional and fast hybrid strategy for islanding detection in Distributed Generators (DGs) based on inverters. The active method utilized in this work is Sandia Frequency Shift (SFS), while the passive method is the Rate Of Change Of Frequency (ROCOF) Relay. The key contribution of this paper is the use of Maximum Likelihood to estimate the ROCOF as opposed to the conventional Phase-Locked-Loop (PLL) method. The objective of this paper is to improve the responsiveness of detection across various conditions by providing fast detection without compromising accuracy. Several simulation investigations, adhering to the anti-islanding test standards of IEEE 1547 and UL1741, have been carried out on a system integrated with the suggested hybrid method. These studies aim to showcase the high sensitivity and specificity of the proposed technique for detecting islanding. In addition, pertinent comparisons are made between the proposed technique and the PLL-based method. The simulation results illustrate that the proposed control system effectively complies with the DG islanding protection requirements. In Furthermore, compared to the PLL, the proposed technique mitigates the common drawbacks associated with other islanding detection techniques by reducing the Non-Detection Zone, enhancing the power quality of the system, and, most significantly, producing fast detection under different conditions. The proposed approach exhibits a response time nearly three times faster than the PLL method, while in less demanding conditions, it provides an instantaneous response.

In Situ Hybridization Strategy Constructs Heterogeneous Interfaces to Form Electronically Modulated MoS2/FeS2 as the Anode for High-Performance Lithium-Ion Storage.

The interfacial effect is important for anodes of transition metal dichalcogenides (TMDs) to achieve superior lithium-ion storage performance. In this paper, a MoS2/FeS2 heterojunction is synthesized by a simple hydrothermal reaction to construct the interface effect, and the heterostructure introduces an inherent electric field that accelerates the de-embedding process of lithium ions, improves the electron transfer capability, and effectively mitigates volume expansion. XPS analysis confirms evident chemical interaction between MoS2 and FeS2 via an interfacial covalent bond (Mo-S-Fe). This MoS2/FeS2 anode shows a distinct interfacial effect for efficient interatomic electron migration. The electrochemical performance demonstrated that the discharge capacity can reach up to 1217.8 mA h g-1 at 0.1 A g-1 after 200 cycles, with a capacity retention rate of 72.9%. After 2000 cycles, the capacity retention is about 61.6% at 1.0 A g-1, and the discharge capacity can still reach 638.9 mA h g-1. Electrochemical kinetic analysis indicated an enhanced pseudocapacitance contribution and that the MoS2/FeS2 had sufficient adsorption of lithium ions. This paper therefore argues that this interfacial engineering is an effective solution for designing sulfide-based anodes with good electrochemical properties.