Unique Hybrid Research Articles

Size-dependent guest-memory switching of the flexible and robust adsorption characteristics of layered metal-organic frameworks.

Flexible-robust metal-organic frameworks (MOFs), which exhibit unique hybrid nature comprising both flexible and rigid framework characteristics, exhibit high potential for hydrocarbon separations. However, no clear guidelines have been established to regulate their hybrid characteristics owing to limited understanding of their adsorption mechanism. This study investigates the effects of the particle size of a flexible-robust MOF on its adsorption and structural transition behaviors. The robust nature originates from the structural transition of a metastable guest-free structure, while its flexible nature arises from another guest-free structure. The type of guest-free structure is predominantly determined by the particle size; particles below the critical size are trapped in the metastable guest-free structure. Notably, the critical size varies with the type of guest molecule to be removed; consequently, the difference in critical size results in guest-memory characteristics, enabling guest-free structure switching. These results underscore the importance of controlling the particle size to fine-tune hybrid adsorption characteristics of flexible-robust MOFs.

Finding Home Beyond Borders: Sufism and Homelessness in Leila Aboulela’s Writing

The romantic urge for home, to which one belongs but where one can no longer be, recalling the longing for the lost Garden of Eden, may have different origins for different writers. Yet the impulse to write about one’s homeland is inevitable for immigrant writers, and Sudanese-British writer Leila Aboulela is no exception. While themes of exile, faith, identity, and belonging are central throughout Aboulela’s body of work, this analysis will focus specifically on her novels Minaret (2005), The Kindness of Enemies (2015), and Bird Summons (2019). By engaging in comparative analysis and tracing thematic threads and stylistic shifts across a broader spectrum of Aboulela’s writing, this paper explores how Sufism serves as both a thematic and structural foundation, offering a lens through which homelessness, belonging, and spirituality are reimagined. It examines how Aboulela creates spaces of fresh promise where both her protagonists and Aboulela herself feel at home again, highlighting the progression of Sufi influences across her three novels. The analysis demonstrates how her engagement with Sufi principles deepens over time, culminating in a unique hybrid narrative mode—situated at the intersection of novel and masnavi traditions—that employs magical realism to destabilize Western logic and bridge Eastern and Western literary traditions.

Conjugated carbon network mediated - Graphdiyne based Cu2MoS4 homojunction for enhanced charge transfer dynamics

The preparation of stable and environment-friendly catalysts with high photocatalytic performance is of practical significance for the application of photocatalytic hydrogen evolution technology. Graphdiyne has a unique sp and sp2 hybrid structure, making it a potential photocatalyst due to its high surface carrier mobility and porous network structure. In this study, a Cu2MoS4 homojunction composite catalyst with conjugated carbon networks as electron transport channels was prepared by in situ solvothermal growth. Graphdiyne provides an additional pathway for photogenerated electron transfer, improving the efficiency of the interface charge separation. Simultaneously, the built-in electric field at the interface of Cu2MoS4 provides a strong driving force for electron transfer. Owing to the significant visible light absorption and effective multichannel transmission of photogenerated electrons, the Cu2MoS4/graphdiyne composite catalyst exhibited astonishing photocatalytic hydrogen evolution activity (11000 μmol g-1), which is 10 times that of Cu2MoS4 homojunction. Further experimental research and theoretical calculations provided effective evidence for the charge transfer pathway. This work provides a new strategy for constructing efficient hydrogen evolution photocatalysts and regulating the migration of photogenerated electrons.

Promising Inhibitors of Endocannabinoid Degrading Enzymes Sharing a Carbamate Scaffold.

Carbamate has been extensively used as a scaffold in the recent era of drug discovery and is a common structural motif of many approved drugs. The carbamate moiety's unique amide-ester hybrid (-O-CO-NH-) feature offers the designing of specific drug-target interactions. Despite the discovery of numerous carbamate derivatives that act on the endocannabinoid system (ECS), the development of clinically effective carbamates remains a challenge. In this review, we highlight the therapeutic potential of carbamate inhibitors of endocannabinoid degrading enzymes as a breakthrough in discovering neurotherapeutic drugs. We discuss the design strategies and medicinal chemistry aspects involved in developing carbamate-based molecular architectures that modulate the endocannabinoid signaling pathway by interfering with fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MAGL), and α/β-Hydrolase domain-containing 6 (ABHD6). Additionally, we highlight the dual activity profile of carbamates against FAAH and MAGL, FAAH and cholinesterase, and FAAH and TRPV1 channels. Furthermore, we illustrate the pharmacophores of O-functionalized carbamates and N-cyclic carbamates that are crucial for FAAH and MAGL inhibitory activities, respectively.

Polypyrrole employed interfacial engineering of porous NiFe2O4/Ti3C2Tx hybridized triphasic freestanding films for high-performance flexible pseudocapacitors

Rational design of a unique hybrid derived from transition-metal oxides, possessing high capacity but poor electronic conductivity, with two-dimensional (2D) MXenes, owning metallic conductivity but limited capacity and instability in aqueous electrolytes, is expected to produce innovative electrode materials for supercapacitor. In this study, we fabricated a free-standing triphasic composite film of NiFe2O4 pillared Ti3C2Tx encapsulated with polypyrrole (MNFx@PPy). This composite combined merits from large redox capacity of NiFe2O4 and high conductivity of Ti3C2Tx to operate within a voltage window of 1.2 V in 2.0 M H2SO4 electrolyte. The MNF10@PPy electrode had a specific capacity of 706.6 mAh·g−1 at 1.0 A·g−1, with 81.13 % retention at a high current density of 20 A·g−1. The integration of PPy enhanced interfacial contact of the components which leads to upsurge in electrochemical performance and stability of the tri-component system. When the fabricated asymmetric flexible supercapacitor (MXene@PPy//MNF10@PPy) was assessed with broad 1.6 V, a complimentary potential window of both electrodes, the device offered 37.49 Wh·kg−1 energy density at a power density of 3879 W·kg−1. This study underscores the synergetic potential of the MNF10@PPy composite to improve energy storage pseudocapacitive electrodes for flexible devices.

Hybrid-DC: A Hybrid Framework Using ResNet-50 and Vision Transformer for Steel Surface Defect Classification in the Rolling Process

This study introduces Hybrid-DC, a hybrid deep-learning model integrating ResNet-50 and Vision Transformer (ViT) for high-accuracy steel surface defect classification. Hybrid-DC leverages ResNet-50 for efficient feature extraction at both low and high levels and utilizes ViT’s global context learning to enhance classification precision. A unique hybrid attention layer and an attention fusion mechanism enable Hybrid-DC to adapt to the complex, variable patterns typical of steel surface defects. Experimental evaluations demonstrate that Hybrid-DC achieves substantial accuracy improvements and significantly reduced loss compared to traditional models like MobileNetV2 and ResNet, with a validation accuracy reaching 0.9944. The results suggest that this model, characterized by rapid convergence and stable learning, can be applied for real-time quality control in steel manufacturing and other high-precision industries, enhancing automated defect detection efficiency.

Lantern-Shaped Structure Induced by Racemic Ligands in Red-Light-Emitting Metal Halide with Near 100 % Quantum Yield and Multiple-Stimulus Response.

Organic-inorganic metal halides (OIMHs) have become a research hotspot in recent years due to their excellent luminescent properties and tunable emission wavelengths. However, the development of efficient red-light-emitting OIMHs remains a significant challenge. This work reports three Mn-based OIMHs derived from 1-methyl-1,2,3,4-tetrahydroisoquinoline hydrobromide: racemic one (Rac-TBM) and chiral ones (R-TBM and S-TBM). As a result of the synergism of chiral organic ligands inducing a unique lantern-shaped hybrid structure containing both tetrahedra and octahedra, Rac-TBM exhibits red-light emission with near-unity luminescence quantum yield. In comparison, the chiral counterparts R/S-TBM display strong green emission and circularly polarized luminescence (CPL) with a glum value up to ±2.5×10-2. Interestingly, a mixture of R- and S-TBM can transform into Rac-TBM, successfully achieving a sensitive and reversible switch between red light of octahedra and green light of tetrahedra under external stimuli. The outstanding luminescent properties allow Rac-TBM to be utilized not only for X-ray radioluminescence with a detection limit down to 46.29 nGys-1, but also for advanced information encryption systems to achieve leak-proof decryption.

Simplex-lattice design and decision tree optimization of endophytic Trichoderma-multi-walled carbon nanotube composite for enhanced methylene blue removal

This study investigates a novel approach for enhancing methylene blue (MB) removal from water using a composite of endophytic Trichoderma mate and multi-walled carbon nanotubes (MWCNTs). For the first time, a unique combination of simplex-lattice design and decision tree learning algorithm was employed to optimize MB removal. This innovative approach effectively identified the optimal composite ratio of hyphal mate (0.5354 g/L) and MWCNTs (0.4646 g/L) for maximizing MB removal, which achieved remarkable removal efficiency ranging from 63.50 to 95.78 % depending on the combination used. The DT model further demonstrated promising potential for predicting MB removal efficiency. SEM revealed a unique hybrid material formed by the intertwining or entrapment of MWCNTs within the hyphal network of Trichoderma mate. FT-IR analysis confirmed the presence of novel functional groups on the MWCNTs' surface at 2438.79 and 528.25 cm−1, likely due to interactions with the endophytic fungi's biomolecules. These functional groups presumably act as reducing and stabilizing agents, promoting efficient MB adsorption. This research paves the way for utilizing the combined biological and chemical approach (fungal biomass and MWCNTs) in bioremediation applications. The findings suggest significant potential for practical applications in wastewater treatment, providing an eco-friendly and cost-effective method for dye removal. Furthermore, the proposed method shows promise for scaling up to industrial wastewater treatment and applicability in resource-limited settings, offering a sustainable solution for global water pollution challenges. Further investigations with larger datasets incorporating additional influencing factors are necessary to refine the predictive power of the DT model for practical applications.

Exploring the link between foresight and artificial intelligence methods to strengthen collective future-building in contexts of social instability

Purpose The study explores the connection between foresight and Artificial Intelligence (AI) methods in a community within an environment of social instability in Colombia. It aims to contribute to research on aligning these methods for future-shaping, with the goal of enhancing shared governance, peer learning and collective learning among traditional decision-makers and local communities in emerging countries. The study seeks to foster a community of social actors who are likely to engage constructively in strategic dialogues. To enhancing shared governance and learning a hybrid model is synthesized, combining foresight and computational intelligence. Design/methodology/approach The case study explores the integration of computational intelligence and foresight through Gaston Bachelard's (Bachelard, 1936) phenomenology concept of ante-perception. The mathematical representation of the cone of scenarios provides a structured way to explore multiple future pathways, allowing communities to visualize and compare different trajectories and make informed decisions amid uncertainty. The model facilitates critical reflections on present assumptions, deepening insights into future scenarios. Ante-perception challenges traditional approaches to foresight by encouraging a break from established experiences, allowing for novel insights into possible futures. When enriched by computational intelligence, this reflective process is further strengthened by quantitative approach scenario modeling. Findings This research develops and tests a proposal that includes the logic and methods for constructing a mathematical representation of the cone of scenarios. This process, which is interactive and deliberative, is driven by anticipation and combines qualitative and quantitative approaches within a context of high uncertainty. By combining the critical reflection facilitated by ante-perception with the predictive power of computational intelligence, the model allows communities to transcend established thought patterns and explore innovative future possibilities. This integrative approach helps them envision and work toward social self-transformation. Research limitations/implications The article aims to identify the creation of scenarios in contexts of high uncertainty, to respond to the needs of communities in emerging countries to manage change. Practical implications This article explores a novel approach to using foresight for address collective intelligence by developing a shared future vision in high-uncertainty contexts within local communities in emerging countries. The application of the hybrid model demonstrates that foresight is a key innovative social tool for developing long-term strategic reflection and planning for territories. Social implications In developing long-term reflective processes, explaining phenomena, mechanisms and correlations requires the use of value judgments. This set of value judgments requires a representation that facilitates their treatment, helps to account for their behavior during the inference process to form a shared future vision. Consequently, ensuring the recognition of the opinions of local communities through participatory discussion spaces and their subsequent refinement, from a technical perspective, aims to illustrate the development of this social construction process. While similarities exist, differences add value through a transfer process, often subconscious. This process stimulates collective learning and builds capacities as knowledge is developed through inquiry, evaluation, interpretation and generalization. Originality/value This research provides a unique hybrid model that fosters collective learning and engagement by integrating local community perspectives with advanced computational intelligence methods. By facilitating both reflective and quantitative approach future-shaping, it offers a practical framework for addressing uncertainty while empowering communities to shape their own futures. It underscores the importance of recognizing local community views through bottom-up participatory discussions, thereby widening the stakeholder community to active engagement in addressing broader societal issues. The case study focuses on community collaboration in Puerto Gaitán, a Colombian municipality.

Porphyrin-engineered nanoscale metal-organic frameworks: enhancing photodynamic therapy and ferroptosis in oncology.

Photodynamic therapy and ferroptosis induction have risen as vanguard oncological interventions, distinguished by their precision and ability to target vulnerabilities in cancer cells. Photodynamic therapy's non-invasive profile and selective cytotoxicity complement ferroptosis' unique mode of action, which exploits iron-dependent lipid peroxidation, offering a pathway to overcome chemoresistance with lower systemic impact. The synergism between photodynamic therapy and ferroptosis is underscored by the depletion of glutathione and glutathione peroxidase four inhibitions by photodynamic therapy-induced reactive oxygen species, amplifying lipid peroxidation and enhancing ferroptotic cell death. This synergy presents an opportunity to refine cancer treatment by modulating redox homeostasis. Porphyrin-based nanoscale metal-organic frameworks have unique hybrid structures and exceptional properties. These frameworks can serve as a platform for integrating photodynamic therapy and ferroptosis through carefully designed structures and functions. These nanostructures can be engineered to deliver multiple therapeutic modalities simultaneously, marking a pivotal advance in multimodal cancer therapy. This review synthesizes recent progress in porphyrin-modified nanoscale metal-organic frameworks for combined photodynamic therapy and ferroptosis, delineating the mechanisms that underlie their synergistic effects in a multimodal context. It underscores the potential of porphyrin-based nanoscale metal-organic frameworks as advanced nanocarriers in oncology, propelling the field toward more efficacious and tailored cancer treatments.

Enhancing compressive behaviour of seawater sea sand concrete filled hybrid carbon-glass FRP tubes exposed to seawater: Effect of thickness

This study presents a novel investigation into the impact of tube thickness on the residual compressive strength of sea water sea sand concrete (SWSSC) filled filament wound hybrid fibre-reinforced polymer (HFRP) tubes, positioning them as innovative alternatives to traditional concrete-filled steel tubes in corrosive marine environments. The research explores tube thicknesses of 2 mm, 3 mm, and 4 mm, employing a unique hybrid configuration of 50 % carbon fibre reinforced polymer (CFRP) internal layers and 50 % glass fibre reinforced polymer (GFRP) external layers. This study examines the effect of these configurations on compressive mechanical properties, microstructural characteristics, and damage progression under alkaline and seawater conditions. Additionally, the research evaluates cross-ply and hoop orientations as variables in filament winding fibre orientation. A comprehensive set of 108 hybrid tubes were filled with an alkaline solution to simulate the SWSSC environment and exposed to seawater at varying temperatures and durations. Accelerated aging experiments were conducted in the laboratory to assess the long-term compressive performance of SWSSC-filled HFRP tubes exposed to seawater. Long-term compressive strength predictions were made using the Arrhenius theory, based on short-term accelerated aging data. The accelerated test data revealed maximum compressive strength reductions of 33 %, 15 %, and 17 % for 2 mm, 3 mm, and 4 mm cross-ply HFRP tubes, respectively, after 150 days of exposure at 60 °C. For hoop HFRP tubes, the corresponding reductions were 19 %, 18 %, and 17 %. Thicker tubes (3 mm and 4 mm) with sufficient internal CFRP layers protect external GFRP layers from alkaline damage, akin to pure CFRP tubes. Thinner tubes (2 mm) exhibit similar performance to GFRP tubes as alkaline damage spreads from their less substantial internal CFRP layers to the outer GFRP layers. For cross-ply tubes, the recommended strength reduction factors are 0.6 for 2 mm, 0.8 for 3 mm, and 0.8 for 4 mm thicknesses. For hoop tubes, the suggested factors are 0.7 for 2 mm, 0.7 for 3 mm, and 0.8 for 4 mm thicknesses. This study shows the innovative potential of tailored hybrid HFRP tubes in enhancing the durability and performance of marine infrastructure.

Entangled S-doped LiFePO4 with carbon network for self-supporting cathode of flexible energy storage devices

This paper describes the development of a highly flexible self-supporting cathode incorporating a network of entangled carbon nanofibers (CNFs) and well-dispersed sulfur-doped lithium iron phosphate (SLFP) particles. Through a spin-on-dopant process, sulfur atoms were successfully incorporated into both the surface carbon layer and inner LFP particles. The resulting S-doped carbon@LFP (SLFP-CNF) flexible electrode featured a unique hybrid composite architecture in which the interconnecting CNF framework considerably enhanced both the physical and electrochemical properties. Notably, the SLFP-CNF electrode maintained its structural integrity even after 5000 flexibility cycles, demonstrating robust mechanical stability. The electrode exhibited a high ionic diffusion rate of 9.14 × 10–13 cm²/s, which is attributed to the expanded lattice constant of the S-doped LFP particles, which facilitates favorable Li-ion pathways. This contributes to a high specific capacity of 135.82 mAh/g and superior rate performance of 74.16 mAh/g after 1500 cycles (92.4 % retention rate) at a current density of 2000 mA/g. This enhanced performance is further supported by the conductive S-doped carbon layer, which enables rapid electron transport and ensures excellent cyclability. These results indicate that the SLFP-CNF flexible cathode is a promising candidate for next-generation flexible lithium-ion batteries.

CHOICE OF DISINFECTION METHOD FOR TRITICALE GRAIN IN THE PROCESS OF SPROUT PRODUCTION

The growing demand for food resources has led to increased interest in using unconventional cereal crops in the production of various food and beverage products. One crop that has attracted significant attention from researchers is triticale, a unique hybrid. An important area in the production of functional foods is the use of raw materials such as sprouted grains. Sprouted grains are highly nutritious and rich in vitamins, minerals, and antioxidants, making them functional foods. However, during the sprouting process, there is active growth of all types of epiphytic bacteria, microscopic fungi as well as phytopathogenic fungi found in the germ zone, which can produce mycotoxins. Therefore, ensuring the microbiological safety of sprouted grains is particularly important. The aim of this study was to select an effective method for disinfecting organic triticale grain during sprouting. Potassium permanganate and hydrogen peroxide solutions were used as disinfectants. The research found that ensuring the microbiological safety of sprouted triticale grain can be achieved by treating it with a 5% hydrogen peroxide solution.

Olfactory ensheathing cells from adult female rats are hybrid glia that promote neural repair.

Olfactory ensheathing cells (OECs) are unique glial cells found in both central and peripheral nervous systems where they support continuous axonal outgrowth of olfactory sensory neurons to their targets. Previously we reported that following severe spinal cord injury, OECs transplanted near the injury site modify the inhibitory glial scar and facilitate axon regeneration past the scar border and into the lesion. To better understand the mechanisms underlying the reparative properties of OECs, we used single-cell RNA-sequencing of OECs from adult rats to study their gene expression programs. Our analyses revealed five diverse OEC subtypes, each expressing novel marker genes and pathways indicative of progenitor, axonal regeneration, secreted molecules, or microglia-like functions. We found substantial overlap of OEC genes with those of Schwann cells, but also with microglia, astrocytes, and oligodendrocytes. We confirmed established markers on cultured OECs, and localized select top genes of OEC subtypes in olfactory bulb tissue. We also show that OECs secrete Reelin and Connective tissue growth factor, extracellular matrix molecules which are important for neural repair and axonal outgrowth. Our results support that OECs are a unique hybrid glia, some with progenitor characteristics, and that their gene expression patterns indicate functions related to wound healing, injury repair and axonal regeneration.

Genome sequencing and mining expand the naturalproduct repertoire of Lysobacter.

Compounds produced by living organisms serve as an important source of inspiration for the development of pharmaceuticals. A potential source of new natural products are bacteria from a genus with species that are known to produce bioactive natural products, but are relatively understudied. Lysobacter is a genus of bacteria that have attracted attention as possible biocontrol agents and are known to produce antibiotic natural products. To further explore the biosynthetic potential of Lysobacter, we sequenced the genomes of two species and performed genome mining studies on those and publicly available genomes. In this study we produced draft genome sequences for Lysobacter firmicutimachus and Lysobacter yananisis. We additionally examined 113 publicly available Lysobacter genomes and found that biosynthetic potential of individual species ranges broadly, with species having between 1 and nearly 20 biosynthetic gene clusters. Filtering for more complete genome assemblies and 9 or more biosynthetic gene clusters, we performed genome mining on 24 Lysobacter genomes. Within these genomes we identified 21 unique nonribosomal peptide, 11 unique hybrid polyketide/nonribosomal peptide, 4 unique polyketide, and 27 unique lanthipeptide biosynthetic gene clusters that produce uncharacterized compounds. Additionally, we tentatively identified the biosynthetic gene cluster in L. rmicutimachus responsible for producing plusbacins, which has not been previously identified. This study demonstrated that Lysobacter have a large repertoire of natural products that remain to be characterized. Additionally, we found that some Lysobacter species are substantially more biosynthetically gifted than others and that strains of the same species of Lysobacter have similar biosynthetic capacities.

Waste management and water quality evaluation prediction in urban environments through advanced robust hybrid machine learning algorithms

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

Hybrid modelling of dynamic softening using modified Avrami kinetics under Gaussian processes

This paper presents a new method of modelling that combines several approaches to anticipate the softening of nickel-niobium alloys during dynamic recrystallization (DRX). The study employs an extensive dataset obtained from hot torsion deformation tests conducted on high-purity nickel and six nickel-niobium alloys. The niobium concentration in these alloys varies from 0.01 to 10 wt % (Matougui et al., 2013). The hybrid technique integrates the Avrami model to provide early predictions about the kinetics of recrystallization and then uses mechanistic modelling to assess the progression of softening caused by dynamic recrystallization (DRX). The integrated technique is improved by using Gaussian process regression analysis, which investigates the softening properties and offers useful insights into the effects of niobium additions on dynamic softening behaviour. This unique hybrid framework combines multiple modelling tools to reveal intricate connections impacted by solute addition, therefore enhancing our comprehension of the physical events that take place during the hot deformation of superalloys. The use of empirical, mechanistic, and machine learning methods in this hybrid model provides a more thorough and detailed investigation of DRX processes in these alloys.

Piezoelectrically and Capacitively Transduced Hybrid MEMS Resonator with Superior RF Performance and Enhanced Parasitic Mitigation by Low-Temperature Batch Fabrication

This study investigates a hybrid microelectromechanical system (MEMS) acoustic resonator through a hybrid approach to combine capacitive and piezoelectric transduction mechanisms, thus harnessing the advantages of both transducer technologies within a single device. By seamlessly integrating both piezoelectric and capacitive transducers, the newly designed hybrid resonators mitigate the limitations of capacitive and piezoelectric resonators. The unique hybrid configuration holds promise to significantly enhance overall device performance, particularly in terms of quality factor (Q-factor), insertion loss, and motional impedance. Moreover, the dual-transduction approach improves the signal-to-noise ratio and reduces feedthrough noise levels at higher frequencies. In this paper, the detailed design, complex fabrication processes, and thorough experimental validation are presented, demonstrating substantial performance enhancement potentials. A hybrid disk resonator with a single side-supporting anchor achieved an outstanding loaded Q-factor higher than 28,000 when operating under a capacitive drive and piezoelectric sense configuration. This is comparably higher than the measured Q-factor of 7600 for another disk resonator with two side-supporting anchors. The hybrid resonator exhibits a high Q-factor at its resonance frequency at 20 MHz, representing 2-fold improvement over the highest reported Q-factor for similar MEMS resonators in the literature. Also, the dual-transduction approach resulted in a more than 30 dB improvement in feedthrough suppression for devices with a 500 nm-thick ZnO layer, while hybrid resonators with a thicker piezoelectric layer of 1300 nm realized an even greater feedthrough suppression of more than 50 dB. The hybrid resonator integration strategy discussed offers an innovative solution for current and future advanced RF front-end applications, providing a versatile platform for future innovations in on-chip resonator technology. This work has the potential to lead to advancements in MEMS resonator technology, facilitating some significant improvements in multi-frequency and frequency agile RF applications through the original designs equipped with integrated capacitive and piezoelectric transduction mechanisms. The hybrid design also results in remarkable performance metrics, making it an ideal candidate for integrating next-generation wireless communication devices where size, cost, and energy efficiency are critical.

Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network

Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar

Regulatory politics and hybrid governance: the case of Brazil’s Amazon Soy Moratorium

This paper analyses the unique hybrid governance of Brazil’s Amazon Soy Moratorium (ASM) in regulating soybean production in the Amazon, where private actors have created a state-like ban on commodity production to reduce deforestation that goes beyond national law. Despite existing research regarding impact assessment, the study aims to fill knowledge gaps in explaining the ASM’s alliance-building processes, its longstanding maintenance, and its potential for regulatory replicability. Informed by the application of the Baptist and Bootlegger political economic theory of regulation and empirical data from qualitative interviews and document analysis, we provide an actor-centered explanation of the design, adoption, and maintenance of the ASM over a 19-year timeframe. Our results show how NGOs and businesses had opposite motivations and negotiated their roles to form a successful strategic alliance, reinforced by the inclusion of third parties (e.g., technical and governmental actors) to assist in its monitoring and transparency. Developed as an exclusive private market regulation, the ASM agreement, however, relies on a policy mix: private and public actors play a role in implementation, which includes assisting and relying on existing public policies, instruments, and official data. This policy mix was necessary for the ASM’s noteworthy hybrid and long-term governance. Its successful formation in 2006 was enabled by factors including an economic crisis, foreign pressure linked with national enforcement failure, and, most importantly, the Amazon scope. Our analysis shows who gains or loses from the regulatory design. Furthermore, we shed light on the biggest regulatory spillover, to the Cerrado, where the failed attempt at replicability emphasizes the regulatory uniqueness of the ASM. The study concludes with a discussion of what will help or hinder the ASM’s longevity, providing lessons for similar regulatory mechanisms on forest-risk agricultural production, such as EU’s recent Regulation on Deforestation-free Products.