Versatile Design Research Articles

Oxidation-resistant self-adhesive flexible transparent electrodes based on Ag–Au core-shell nanowires and heterogeneous microarchitectures

Flexible transparent electrodes (FTEs) are essential for advancing flexible electronics, energy systems, and biomedical devices. Conventional FTEs, which use silver nanowire coatings on flexible substrates, face limitations due to poor oxidation resistance and difficulties in forming reliable mechanical and electrical interconnections with other device components. In this study, we propose a versatile FTE design that integrates Ag–Au core-shell nanowires and self-adhesive microstructures into a regular grid pattern. This electrode exhibits robust self-adhesion, enabling precise mechanical and electrical contacts across various substrates without additional adhesives. The optical and electrical properties can be finely tuned by manipulating the microstructures and nanowire coatings. Notably, the electrode demonstrates remarkable oxidation resistance, even under exposure to oxidizing agents, elevated temperatures, and high-humidity environments. Our findings provide practical pathways for implementing FTEs in a wide range of emerging optoelectronic devices, leveraging their exceptional chemical and thermal stability.

Gesture speak: Hands-Free Computer Control with Hand Gestures and Voice Commands

In an era of advancing human-computer interaction, "GestureSpeak" emerges as a pioneering project that facilitates hands-free control over computing devices through intuitive hand gestures and voice commands. Leveraging the power of MediaPipe and OpenCV in Python, this innovative system enables users to seamlessly navigate their digital environments without the need for traditional input devices. By harnessing the capabilities of computer vision and machine learning, GestureSpeak interprets and responds to users' gestures and vocal instructions, opening new frontiers in accessibility and user experience. This abstract offers a glimpse into the transformative potential of GestureSpeak in revolutionizing the way we interact with computers, making technology more accessible and intuitive for all users. Advancements in human-computer interaction have led to the development of innovative projects that redefine how users interact with technology. This paper introduces a novel integration of two projects: GestureSpeak and VoiceRobot, which together enable intuitive and hands-free control over computing devices using hand gestures and voice commands. VoiceRobot complements GestureSpeak by adding voice command capabilities to the interaction model. Powered by speech recognition technology, VoiceRobot enables users to control devices and launch applications using natural language. With VoiceRobot, initiating the GestureSpeak project is as simple as speaking a command. The combined system offers unique benefits, including improved accessibility for users with mobility impairments and a more natural way to interact with computing devices. By integrating gesture recognition and voice commands, the project enhances user engagement and efficiency, paving the way for future advancements in human-computer interaction. Launching the Project with VoiceRobot:A notable feature of this integrated system is the ability to launch the GestureGenie project using VoiceRobot. By speaking a predefined command, such as "Activate GestureGenie," users can initiate the hand gesture control system effortlessly. This capability highlights the seamless integration of gesture recognition and voice command technologies, showcasing the project's versatility and user-friendly design. In conclusion, the integration of GestureGenie and VoiceRobot represents a significant advancement in human-computer interaction, offering a comprehensive solution for hands-free device control. This abstract provides insight into the combined capabilities of these projects and their potential impact on accessibility, user experience, and the future of interactive computing.

Bifunctional glycosphingolipid (GSL) probes to investigate GSL-interacting proteins in cell membranes

Glycosphingolipids (GSLs) are abundant glycolipids on cells and essential for cell recognition, adhesion, signal transduction, and so on. However, their lipid anchors are not long enough to cross the membrane bilayer. To transduce transmembrane signals, GSLs must interact with other membrane components, whereas such interactions are difficult to investigate. To overcome this difficulty, bifunctional derivatives of II3-β-N-acetyl-D-galactosamine-GA2 (GalNAc-GA2) and β-N-acetyl-D-glucosamine-ceramide (GlcNAc-Cer) were synthesized as probes to explore GSL-interacting membrane proteins in live cells. Both probes contain photoreactive diazirine in the lipid moiety, which can crosslink with proximal membrane proteins upon photoactivation, and clickable alkyne in the glycan to facilitate affinity tag addition for crosslinked protein pull-down and characterization. The synthesis is highlighted by the efficient assembly of simple glycolipid precursors followed by on-site lipid remodeling. These probes were employed to profile GSL-interacting membrane proteins in HEK293 cells. The GalNAc-GA2 probe revealed 312 distinct proteins, with GlcNAc-Cer probe-crosslinked proteins as controls, suggesting the potential influence of the glycan on GSL functions. Many of the proteins identified with the GalNAc-GA2 probe are associated with GSLs, and some have been validated as being specific to this probe. The versatile probe design and experimental protocols are anticipated to be widely applicable to GSL research.

Zinc–cobalt bimetallic metal-organic framework (Zn/Co-MOF) nanoparticles as potent pH stimuli anti-corrosive agent for development of a self-healable epoxy composite coating

The impressive BET-surface area and the design versatility have made Z67(ZIF-67) MOF a potent nanomaterial for anti-corrosion applications. Despite excellent anti-corrosion features the Z67(ZIF-67) suffers poor stability in water media, making it improper for application in polymeric systems. One strategy to enhance the stability of Z67 is its structure modification via a mixed-metal organic framework approach. This study involved the synthesis of a series of Z67 nanoparticles through the incorporation of zinc cation (Zn) at different percentages. Specifically, three different compositions were synthesized: Zn10-Z67, Zn20-Z67, and Zn30-Z67 denoting the addition of 10 %, 20 %, and 30 % molar percent of Zn, respectively. FTIR, FE-SEM, XRD, TEM, ICP-OES, and XPS, as well as BET analyses, were conducted for the Zinc–Cobalt bimetallic MOF structure. Subsequently, the properties attributed to the particles' active and barrier anti-corrosion feature in solution (NaCl) as well as coating (epoxy) phases were investigated. The EIS test performed in the solution phase, after 72h of metal exposure to Zn30-Z67 containing saline media, revealed a 590 % and 30 % increase in total resistance, respectively, compared to the uninhibited sample. Due to the increased stability of Zn10-Z67 in a saline environment, the active corrosion inhibition of the scratched coating containing these particles was increased by approximately 140 % compared to the Z67-containing composite. The incorporation of zinc cations into the structure of Z67 has enhanced the stability and control release features in aqueous environments, thereby enhancing the barrier properties of the epoxy coating. The reduction rate of log|Z|0.01Hz of the Zn10-Z67/EPC was 9 % which was lower than that of the Neat EPC (epoxy coating) (24 %).

Valence can control the nonexponential viscoelastic relaxation of multivalent reversible gels.

Gels made of telechelic polymers connected by reversible cross-linkers are a versatile design platform for biocompatible viscoelastic materials. Their linear response to a step strain displays a fast, near-exponential relaxation when using low-valence cross-linkers, while larger supramolecular cross-linkers bring about much slower dynamics involving a wide distribution of timescales whose physical origin is still debated. Here, we propose a model where the relaxation of polymer gels in the dilute regime originates from elementary events in which the bonds connecting two neighboring cross-linkers all disconnect. Larger cross-linkers allow for a greater average number of bonds connecting them but also generate more heterogeneity. We characterize the resulting distribution of relaxation timescales analytically and accurately reproduce stress relaxation measurements on metal-coordinated hydrogels with a variety of cross-linker sizes including ions, metal-organic cages, and nanoparticles. Our approach is simple enough to be extended to any cross-linker size and could thus be harnessed for the rational design of complex viscoelastic materials.

Compact auto-aligning interferometers with picometer precision

This research introduces a compact, auto-aligning interferometer engineered for measuring translations with a wide angular working range and picometer precision above 1Hz. It presents a design ensuring automatic beam alignment during movement through secondary reflection from a corner reflector. The sensor head, a 20×10×10mm3 all-glass quasi-monolithic structure, exhibits a displacement sensitivity below 1pm/Hz1/2 above 1Hz and a wide angular working range of ±200mrad. This versatile optical design holds promise to improve the sensitivity in applications such as laser ranging, optical seismometers, precision manufacturing, and metrology.

Nucleic acid-loaded poly(beta-aminoester) nanoparticles for cancer nano-immuno therapeutics: the good, the bad, and the future

Immunotherapy has emerged as a promising approach to cancer treatment, offering improved survival rates and enhanced patients’ quality of life. However, realizing the full potential of immunotherapy in clinical practice remains a challenge, as there is still plenty of room for modulating the complexity of the human immune system in favor of an antitumor immunogenicity. Nanotechnology, with its unique properties, holds promise in augmenting the efficacy of cancer immunotherapies in biotherapeutic protection and site- and time-controlled delivery of the immune modulator biologicals. Polymeric nanoparticles are promising biomaterials among different nanocarriers thanks to their robustness, versatility, and cost-efficient design and production. This perspective paper overviews critical concepts in nanometric advanced delivery systems applied to cancer immunotherapy. We focus on a detailed exploration of the current state of the art and trends in using poly(beta-aminoester) (pBAE) polymers for nucleic acid-based antitumor immunotherapies. Through different examples of the use of pBAE polymers reported in the literature, we revise the main advantages these polymers offer and some challenges to overcome. Finally, the paper provides insights and predictions on the path toward the clinical implementation of cancer nano-immunotherapies, highlighting the potential of pBAE polymers for advancements in this field.Graphical abstract

A versatile surface micro structure design strategy for porous-based pressure sensors to enhance electromechanical performance

Pressure sensors based on porous structures offer comfortable user experiences and can adapt to the flexible movement of human limbs, which makes them attractive options for human–computer interaction and health monitoring. However, ascribed to the limitation of porous surfaces, foam-based pressure sensors are difficult to achieve accurate electric responses and stable signal output, limiting their great potential in wide application fields. Herein, a surface structure design strategy is proposed for improving the performance of porous foam-based pressure sensors. The coupling of microstructures and the collaboration of dual sensing mechanisms endow the sensor with high performance, achieving a ∼ 12 times signal fluctuation stability when compared to the sensor without any surface design. Even when working under complex vibration conditions, the developed sensor can also maintain a ∼ 7 times more stable output. An intelligent insole based on the algorithm model of a sensor array and multi-layer perceptron was developed for the diagnosis of human foot arch health. The sensors with surface structure could capture and differentiate signals more accurately, which results in a diagnostic accuracy of 99.75 % for the smart insole. The strategy for enhancement of sensor performance proposed in this work can be applied to foam sensors with multiple material systems, which is expected to broaden the potential application of porous flexible devices in fields like the Internet of Things, medical monitoring and brain-computer interface.

Integrated Microwave Photonic Sensors Based on Microresonators

AbstractSensors stand as pivotal cornerstones of technology, driving progress across a spectrum of industries through their ability to precisely capture and interpret an extensive array of physical phenomena. Among these advancements, microwave photonic (MWP) sensing has emerged as a new sensing technique, elevating sensing speed and resolution for practical applications. Integrated MWP sensors exhibit unparalleled capabilities in ultra‐sensitive, label‐free nanoscale detection, offering the potential to synergize with advanced integration techniques for a compact footprint and versatile designs. This paper reviews and summarizes the development and recent advances in integrated MWP sensing, focusing on the schemes based on microresonators. The diverse array of existing schemes is systematically categorized, elucidating their operational principles and performance demonstration. Furthermore, the assistance of machine learning and deep learning in integrated MWP sensors is explored, highlighting the potential of intelligent sensing paradigms. Finally, current challenges and opportunities aimed at further advancing MWP sensors are discussed.

Engineering Small HOMO-LUMO Gaps in Polycyclic Aromatic Hydrocarbons with Topologically Protected States.

Topological phases in laterally confined low-dimensional nanographenes have emerged as versatile design tools that can imbue otherwise unremarkable materials with exotic band structures ranging from topological semiconductors and quantum dots to intrinsically metallic bands. The periodic boundary conditions that define the topology of a given lattice have thus far prevented the translation of this technology to the quasi-zero-dimensional (0D) domain of small molecular structures. Here, we describe the synthesis of a polycyclic aromatic hydrocarbon (PAH) featuring two localized zero modes (ZMs) formed by the topological junction interface between a trivial and nontrivial phase within a single molecule. First-principles density functional theory calculations predict a strong hybridization between adjacent ZMs that gives rise to an exceptionally small HOMO-LUMO gap. Scanning tunneling microscopy and spectroscopy corroborate the molecular structure of 9/7/9-double quantum dots and reveal an experimental quasiparticle gap of 0.16 eV, corresponding to a carbon-based small molecule long-wavelength infrared (LWIR) absorber.

A generic design motif for metamaterials with controllable nonlinearity & guided deformation

Structure design motifs take an essential role in creating materials or metamaterials with superior mechanics or special functions. However, a fundamental question naturally arising and remaining unknown is related to how to effectively design an engineered material structure with “adjustable and predictable” nonlinear mechanic properties conveniently. Here we propose a very simple but versatile design motif, named as bq-structure, which can provide superior flexibility in achieving various nonlinear properties, including 200 % larger and designable ranges of quasi-zero stiffness, negative stiffness, and multi-stable stiffness, etc, that original material or traditional beam-based design motifs cannot provide. The proposed design motif has sufficient reliability in nonlinear manipulation with a “guided & controllable” manner instead of “trail-and-error attempts” existing in most existing ones. A prototyped vibration isolation unit can achieve a resonant frequency less than 1 Hz easily which cannot be done with many other methods of similar size and payload. Given the large elastic deforming capacity and ductility in structural scale, the superb nonlinearity regime is generally designable with various structure parameters, uniquely providing a superior optimization tool with this novel design motif considering various real application requirements for advanced materials design, energy storage or conversion, mechanical vibration mitigation, and robotic systems etc.

Optimizing Neural Network Inference in Edge Robotics by Harnessing FPGA Hardware Acceleration

This research explores advancements in neural network inference for edge robotics through the implementation of hardware acceleration using Field-Programmable Gate Arrays (FPGA). Leveraging FPGA's versatility in custom hardware design, the research optimizes performance and energy efficiency by utilizing features such as Digital Signal Processors (DSP), Multiply-Accumulate (MAC) arrays, parallel processing units, Convolutional Neural Networks (CNN), and custom accelerators. The computational power of FPGA-based architectures is harnessed to significantly enhance the speed and efficiency of neural network inference tasks in edge robotics applications. Key aspects include the utilization of DSP blocks for efficient signal processing, MAC arrays for high-speed matrix operations crucial in neural network computations, and parallel processing units for concurrent execution of multiple tasks, improving overall system throughput. The integration of CNN directly onto FPGA hardware allows for minimal-latency inference, making it ideal for real-time applications in edge robotics scenarios. Custom accelerators tailored to specific neural network operations further optimize performance and resource utilization. The research involves the meticulous design and synthesis of hardware modules tailored to the target FPGA platform. Through rigorous optimization and parallelization techniques, the goal is to maximize resource utilization while minimizing latency and power consumption. The experimental findings showcase remarkable results, including a substantial increase in throughput, with values reaching up to 150 images/sec, and impressive accuracy rates exceeding 95% in classification tasks.

Boosting Second Harmonic Generation Efficiency and Nonlinear Susceptibility via Metasurfaces Featuring Split-Ring Resonators and Bowtie Nanoantennas.

This work investigates a metasurface design to achieve remarkable second harmonic generation (SHG) conversion efficiency and enhance effective nonlinear susceptibility using the finite element method. The elements of the designed structure are composed of a rectangular split-ring resonator Ag film, a bowtie-shaped Ag nanoantenna, and a pair of Bi bars that induce nonlinear optical phenomena due to the nonuniform distribution of the electric and magnetic fields within the device surface. The simulation results agree perfectly with the theory and demonstrate outstanding achievements in terms of SHG conversion efficiency (η) and effective nonlinear susceptibility (χeff(2)). Specifically, the metasurface reaches a peak η value of 4.544×10-8 and an effective nonlinear susceptibility of 3.4×104 pm/V. This work presents a novel and versatile design to achieve high η and χeff(2) in an SHG metasurface.

Leveraging distributed AI for multi-occupancy prediction in Cognitive Buildings

Cognitive Buildings are autonomous smart environments capable of setting themselves according to some self-learned rules. Such rules are inferred according to, e.g., the inhabitants’ behaviors, users’ needs, and specific policies for optimizing security, energy, and comfort management. To do this, it is of foremost importance to gather information about users’ habits like room occupancy. Indeed, Cognitive Buildings can effectively exploit information about sensors in the different rooms, thus being able to detect, learn, and forecast the presence of users in the buildings and act in accordance with these predictions. In this direction, this paper proposes an innovative approach for multi-occupancy prediction in Cognitive Buildings, incorporating a multi-layer hierarchy for Federated Learning, the utilization of IoT devices at the Edge, the implementation of long short-term memory neural network models, and the exploitation of Edge Computing. The approach also introduces a versatile design template for developing real distributed systems for occupancy prediction. The proposed approach uses a distributed paradigm to safeguard data privacy so that the collected data is used to train separate local deep learning models, which are then merged in the Cloud. The paper validates the approach by providing a preliminary prototype realized at ICAR-CNR, Rende Italy, and presents a performance analysis, which shows that the occupancy is predicted with an 84.5% accuracy.

A Flexible One‐dimensional Woven Supercapacitor for Low‐power Smart Electronic Textiles

Flexible energy storage devices are attracting a great attention nowadays in the rapidly growing area of implantable/wearable electronics. This bloom has generated great demands on flexible wire‐shaped supercapacitors (WSC) as power sources in wearable electronic systems that can offer tunable sizes, shapes, and versatile designs. Herein, a flexible WSC is fabricated using MgMn2O4 nanofibers (MMO‐NFs) over a flexible carbon yarn substrate via the electrophoretic deposition method in the first report. The binder‐free deposition of highly porous and one‐dimensionally aligned MMO‐NFs over carbon yarn offers excellent ionic/charge transport into the fabricated device. The assembled WSC device at 0.1 mA/cm current rate shows capacitances of 386 mF/cm2 and 45.5 mF/cm. A capacitance retention of 88% for a WSC device after 10,000 cycles indicates its good cyclability. Further, the WSC exhibits good flexibility, retaining 94% of capacitance at 0.5 mA/cm after 2000 bending cycles. Furthermore, the flexible WSC device shows power and energy density of 744 μW/cm and 14.2 μWh/cm, respectively. Additionally, WSC devices demonstrate their practical energy storage capabilities in a video that shows three similar devices being knitted into a wearable glove and glowing a red light‐emitting diode (LED) for more than five minutes in various bending positions.

Enhanced Fano resonances in a silicon nitride photonic crystal nanobeam-assisted micro ring resonator for dual telecom band operation

Silicon-based Micro Ring Resonators (MRR) are a powerful tool for the realization of label free optical biosensors. The sharp edge of a Fano resonance in a Silicon Nitride (Si3N4) platform can boost photonic sensing applications based on MRRs. In this work, we demonstrate enhanced Fano resonance features for a Si3N4 Micro Ring Resonator assisted by a Photonic Crystal Nanobeam (PhCN-MRR) operating in the TM-like mode at the O-band wavelengths. Our findings show that the fabricated PhCN-MRR results in increased asymmetric resonances for TM-like mode compared with TE-like mode operation in the C-band. As a result, a versatile and flexible design to realize Fano resonance with polarization dependent asymmetry in the C and O telecom bands is presented.

Versatile Triblock Peptide Self-Assembly System to Mimic Collagen Structure and Function.

The construction of collagen mimetic peptides has been a hot topic in tissue engineering due to their attractive advantages, such as virus-free nature and low immunogenicity. However, all of the reported self-assembled peptides rely on the inclusion of risky elements of potential safety concerns or lack the capability of incorporating critical functional motifs. A versatile self-assembly design of pure synthetic peptides that can mimic the collagen structure and function remains an insurmountably challenging target. We have herein created a type of triblock peptide consisting of a central triple helical block and N-terminal/C-terminal blocks with oppositely charged amino acids. Favorable electrostatic interactions between the two terminal blocks have been demonstrated to trigger the triblock peptides to form collagen-like nanofibers with a distinct D-banding pattern. A length of 3 or above charged amino acid pairs as well as the maintenance of the triple helical conformation are required for the self-assembly of triblock peptides. Notably, integrin and discoidin domain receptor (DDR) binding sequences GFOGER and GVMGFO have been well demonstrated as vivid examples of convenient incorporation of functional motifs into the triblock peptides without interfering with their self-assembly. These triblock peptides provide a robust and versatile strategy to create next-generation peptide-based biomaterials that can recapitulate the structure and function of collagen, which have promising applications in the fields of tissue engineering and regenerative medicine.

Light- and Solvent-Responsive Bilayer Hydrogel Actuators with Reversible Bending Behaviors.

Light-responsive hydrogel systems have gained significant attention due to their unique ability to undergo controlled and reversible swelling behavior in response to light stimuli. Combining light-responsive hydrogels with nonresponsive polymers offers a unique self-folding feature that can be used in soft robotic actuator designs. However, simple formulation of such systems with rapid response time is still a challenging task. Herein, we demonstrate a simple but versatile bilayer polymeric design combining light-responsive spiropyran-polyacrylamide (SP-PAAm) with polyacrylamide (PAAm) hydrogels. The photochromic spiropyran in our polymer design is a closed-ring, hydrophobic compound and turns into an open-ring, hydrophilic merocyanine isomer under light irradiation. The swelling degree of SP-PAAm and PAAm hydrogels was evaluated using LED lights with different wavelengths and solvent media (e.g., water, ethanol, DMF, and DMSO). We observed that SP-PAAm hydrogels reached a swelling ratio of ∼370% with the illumination of the blue LED in the DMF medium. By combining light-responsive SP-PAAm hydrogels with nonresponsive PAAm, a proof-of-concept demonstration was performed to demonstrate the applicability of our fabricated platforms. Although fabricated one-armed bilayer hydrogels possessed self-folding ability with a folding angle of ∼40° in 30 min, the four-armed bilayer platforms demonstrated more efficient and rapid folding behavior and reached a folding angle of ∼75° in ∼15 min. Given their simplicity and efficiency, we believe that such polymeric designs may offer new avenues for the fields of polymeric actuators and soft robotic systems.

The challenge of downstream processing of spray dried amorphous solid dispersions into minitablets designed for the paediatric population – A sustainable product development approach

Poorly water-soluble drugs present a significant challenge in the development of oral solid dosage forms (OSDs). In formulation development the appropriate use of excipients to adjust solubility, and the choice of manufacturing method and pharmaceutical processes to obtain a dosage form to meet the needs of the patient group, is crucial. Preparing an amorphous solid dispersion (ASD) is a well-established method for solubility enhancement, and spray drying (SD) a common manufacturing method. However, the poor flowability of spray dried materials poses a significant challenge for downstream processing. Promoting sustainability in OSD development involves embracing a versatile formulation design, which enables a broader spectrum of patients to use the product, as opposed to altering existing dosage forms retrospectively. The objective of the current study was to develop a formulation of spray dried indomethacin ASD suited to the production, by direct compression, of instant release paediatric minitablets. Excipients evaluated were PVP or HPMCAS in solid dispersions at the preformulation phase, and MCC and lactose as a filler in direct compression. From the studied formulations, a 3:1 ratio blend of Vivapur 200/Pharmatose 200 M (MCC/lactose) with 0.5% (w/w) magnesium stearate was found to be the most promising in tableting, and minitablets containing a 6.22% content of spray-dried ASD of indomethacin/PVP K 29–32 could be obtained with desired tablet hardness and pharmaceutical quality, complying with tests of weight variation and fast disintegration in an aqueous environment. As a case example, this study provides a good foundation for further studies in harnessing a sustainable approach to the development of pharmaceutical formulations that can appropriately serve different patient sub-populations.

Pflow: An end-to-end heterogeneous acceleration framework for CNN inference on FPGAs

Field-Programmable Gate Arrays (FPGAs), renowned for their high performance per watt, are extensively utilized to accelerate Convolutional Neural Networks (CNNs) in edge computing environments, primarily employing dataflow-based and instruction set-based approaches. Compared to the instruction set-based approach that features fast and versatile circuit design, the dataflow-based approach can significantly enhance performance at the expense of design versatility. Nevertheless, edge computing environments require both high energy efficiency and adaptability to various scenarios. This paper proposes a novel end-to-end heterogeneous acceleration framework for CNN inference on FPGAs, named Pflow. The basic idea is to decouple network deployment and hardware details with a hardware–software co-design approach. First, a dataflow accelerator with an adaptive scheduling strategy is proposed. The adaptive scheduling strategy, along with a scalable design, maximizes hardware utilization in terms of computing resources and bandwidth. Secondly, we design a novel operator-perception method to automate the processes of network reconstruction and operator fusion. Thirdly, we integrate Pflow into the industrial-grade deep learning framework Paddle-Lite. We evaluate Pflow by implementing several networks on two representative FPGA platforms. Experimental results demonstrate that Pflow achieves energy efficiencies of 46.5 GOPS/W on Xilinx Zynq Ultrascale+ MPSoC 3EG and 59.4 GOPS/W on Virtex UltraScale+ XCVU13P. It also reaches a throughput of up to 255.7 GOPS on the former and 3.686 TOPS on the latter.