Hardware Limitations Research Articles

Optimum-preserving QUBO parameter compression

Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware acceleration. However, due to limited precision and hardware noise, the effective set of feasible parameter values is severely restricted. As a result, otherwise solvable problems become harder or even intractable. In this work, we study the implications of solving QUBO problems under limited precision. Specifically, it is shown that the problem’s dynamic range has a crucial impact on the problem’s robustness against distortions. We show this by formalizing the notion of preserving optima between QUBO instances and explore to which extend parameters can be modified without changing the set of minimizing solutions. Based on these insights, we introduce techniques to reduce the dynamic range of a given QUBO instance based on the theoretical bounds of the minimal energy value. An experimental evaluation on random QUBO instances as well as QUBO-encoded BinClustering and SubsetSum problems show that our theoretical findings manifest in practice. Results on quantum annealing hardware show that the performance can be improved drastically when following our methodology.

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.

Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics. This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging. A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios. The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P<.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P<.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P<.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness. This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap between images from different devices. By processing nonregistered image pairs, the model not only enhances visual quality but also ensures the preservation of essential anatomical structures, crucial for accurate diagnosis. This approach holds the potential to democratize high-quality ultrasound imaging, making it accessible through low-cost portable devices, thereby improving health care outcomes, particularly in resource-limited settings. Future research will focus on further validation and optimization for clinical use.

Melt pool super solution reconstruction based on dual path deep learning for laser powder bed fusion monitoring

Melt pool monitoring in laser powder bed fusion (L-PBF) is an important foundation for process control and melting dynamics research. However, due to hardware limitations and the intense metallurgical phenomena during the melting process, the quality of the melt pool monitoring images cannot be guaranteed. This paper proposes a deep learning-based melt pool image super-resolution (SR) reconstruction method based on the particularity of melt pool images. It is an innovative dual-path structure. The first branch utilizes residual-in-residual structures and the efficient channel attention-Net attention mechanism to achieve the adaptive feature extraction of high-frequency information, thereby capturing effective melt pool boundary information. The second branch uses the U-Net structure to address the low utilization of overall characteristics of the melt pool in the chain-based SR network. In addition to the universal peak signal-to-noise ratio and structural similarity index measure metrics, the reconstruction accuracy of melt pool contour features is used to measure model performance, which intuitively reflects the significance of this work for melt pool monitoring. The results demonstrate that the proposed SR reconstruction method enhances the resolution and clarity of melt pool images. Furthermore, SR reconstruction of the melt pool effectively reduces errors in extracting melt pool features. This method provides a network paradigm for high-precision L-PBF monitoring. It integrates important boundary information and overall morphology features of the melt pool through a dual path structure, thereby achieving reliable SR reconstruction. This research will contribute to low-cost in situ monitoring of L-PBF and subsequent process control.

Agent swarms: Cooperation and coordination under stringent communications constraint.

Here we consider the communications tactics appropriate for a group of agents that need to "swarm" together in a challenging communications environment. Swarms are increasingly important in a number of applications, including land, air, sea and space exploration, and their constituent agents could be satellites, drones, or other autonomous vehicles. A particularly difficult problem is to autonomously connect a swarm of agents together in a situation where stringent communication constraints are present, whether due to a need for stealth, restricted on-board power, external requirements to avoid certain broadcast directions, or equipment & hardware limitations. Here we present a novel, discrete, geometry-free model applicable to multi-agent swarm communications where a group of agents need to connect together and where the constraints on the communications dominate the algorithmic outcomes. No global knowledge of the agent locations is held and hence our framework proposes agent-centric performance metrics. We demonstrate our model using a set of candidate connectivity tactics and we show how simulated outcome distributions, risks and connectivity depend on the ratio of information gain to information loss. We also show that checking for excessive round-trip-times can be an effective minimal-information filter for determining which agents to no longer target with messages. The framework and algorithms that are presented here have wider application in testing efficient communication tactics across agent swarms in designated scenarios and testing the connectivity outcomes for future systems and missions.

Efficient hardware implementation of ROM-free LFM waveform generation for real-time reconfigurable software-defined radar

Abstract This paper presents a novel method for generating Linear Frequency Modulation (LFM) waveforms. Conventional Look-Up Table (LUT)-based method require large storage resources, especially in high-resolution, multi-mode radar systems. To address this issue, we propose a ROM-free, real-time LFM waveform generator. It uses the fourth-order Runge-Kutta (RK-4) method to solve differential equations for the in-phase and quadrature components. The proposed approach dynamically generates LFM waveforms using only parameters such as pulse width, bandwidth, and sampling rate, eliminating the need for pre-calculated waveforms and LUTs. This method completely removes storage requirements, enabling faster synthesis times and achieving minimal latency. The generator’s performance is validated through simulation and FPGA implementation using a full hardware description language. Results confirm its effectiveness, making it a strong candidate for advanced radar systems that demand high performance with limited hardware resources.

An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare

In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 FPGA, designed specifically for accelerating the classification of cytotoxicity in human kidney cells. Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. The CNN, quantized to 8-bit precision and tailored for the FPGA’s resource constraints, significantly accelerates training by a factor of three while consuming only 1.33% of the power compared to a traditional software-based CNN running on an NVIDIA K80 GPU. The network architecture, composed of seven layers with excessive hyperparameters, processes downscale grayscale images, achieving notable gains in speed and energy efficiency. A cornerstone of our methodology is the emphasis on parallel processing, data type optimization, and reduced logic space usage through 8-bit integer operations. We conducted extensive image pre-processing, including histogram equalization and artefact removal, to maximize feature extraction from the augmented dataset. Achieving an accuracy of approximately 91% on unseen images, this FPGA-implemented CNN demonstrates the potential for rapid, low-power medical diagnostics within a broader IoT ecosystem where data could be assessed online. This work underscores the feasibility of deploying resource-efficient AI models in environments where traditional high-performance computing resources are unavailable, typically in healthcare settings, paving the way for and contributing to advanced computer vision techniques in embedded systems.

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Research on Enhanced Belief Propagation List Decoding Algorithm for Polar Codes in UAV Communications for 6G

The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited hardware resources. To meet the high demands of 6G, advanced channel coding techniques need to be adopted. Polar codes, due to their theoretically achievable Shannon limit performance, have potential applications in UAV communication systems. Constructing reliable polar decoding schemes is currently a research hotspot in the field of communications. The Belief Propagation List (BPL) decoding algorithm for polar codes can effectively enhance the accuracy of polar code BP decoding. However, existing BPL decoding algorithms for polar codes face issues such as high hardware resource consumption and unsatisfactory decoding accuracy. Addressing the aforementioned issues, this paper proposes a BPL decoding algorithm for polar codes based on information geometry. An information geometry framework is constructed, where the soft information output by the BP decoder is treated as points on a statistical manifold, and their geometric properties are calculated. By introducing the concept of the soft information centroid and a path selection criterion based on the soft information centroid, combined with geometric distance as a weight, the decoding performance is improved, and hardware overhead is reduced. Simulation results show that under the conditions of a maximum of 60 iterations and 5 decoders, the proposed algorithm reduces the bit error rate by 16.2–74.9% compared to the classic BPL algorithm, providing strong technical support for the application of polar codes in scenarios such as UAV communications.

A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking.

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution. Generally speaking, it is challenging to employ these architectures in real-world applications due to many hardware and environmental restrictions, such as processing and computational capabilities. Therefore, this survey investigates the most efficient methodologies to ensure sub-optimal estimation performances. More in detail, four efficient categories will be analyzed: compact architecture, pruning, knowledge distillation, and quantization strategies. Moreover, a new metric called Efficient Error Rate has been introduced in order to normalize and compare models' features that affect hardware devices at inference time, such as the number of parameters, bits, FLOPs, and model size. Summarizing, this paper first mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios. Toward the end of this paper, we also discuss open challenges and promising research directions.

Edge computing: Revolutionizing data processing for IoT applications

Edge computing is emerging as a transformative solution for managing the vast amounts of data generated by the Internet of Things (IoT). By decentralizing data processing and bringing computation closer to the data source, edge computing addresses critical limitations of traditional cloud computing, including latency, bandwidth constraints, and security vulnerabilities. This review explores the key benefits of edge computing, such as reduced latency, bandwidth optimization, improved reliability, enhanced data privacy, and scalability. It discusses the architecture and components of edge computing, highlighting the roles of edge devices, edge nodes, and fog computing. The review also examines various use cases across sectors, including autonomous vehicles, smart cities, healthcare, industrial IoT, and retail. Finally, the review considers the challenges facing edge computing, including hardware limitations, network security, interoperability, and cost considerations. The future outlook suggests that advancements in 5G technology and artificial intelligence will further enhance the potential of edge computing in driving IoT innovations.

Didactic Game Based on the Unreal Engine and the Effectiveness of its Implementation in Mathematics Education

This study investigates the integration of a didactic game developed using Unreal Engine into elementary school mathematics education, specifically focusing on the teaching of fractions. The research aims to answer the following questions: (1) How do students react to the didactic game? (2) What is the difference between experimental and control group of students? The study involved 46 seventh-grade students from a school in the South Moravian Region of the Czech Republic, randomly divided into two groups: one using the didactic game and one following traditional teaching methods. The research employed both qualitative and quantitative methods, including participatory observation, questionnaires, and didactic tests. The findings reveal that while both groups showed significant improvement in their understanding of fractions, the group using the didactic game demonstrated a more considerable progression, with an average increase in test scores from 38.07% to 71.36%. In contrast, the traditional group improved from 54.17% to 79.68%. Students' reactions to the game were mixed, with some expressing enthusiasm despite technical difficulties, while others were less motivated due to hardware limitations.

Droplet-based bioprinting for the tailored fabrication of bacteria-laden living materials.

Droplet-based bioprinting (DBB) allows for high precision, noncontact, and on-demand distribution of bioinks, hence it has been widely utilized in the preparation of bacteria-laden living materials (BLMs). Nonetheless, discontinuous ink deposition makes it challenging to fabricate large-sized intact living structures via this technique. Herein, we explore the way of using DBB to construct centimeter-scale BLMs with bespoke geometries, and further demonstrate its potential applicability in sensing-responsive device by integrating engineered bacteria. We first established a DBB method based on printing-path design, which does not require hardware modification. This strategy was able to produce customized 3D-hydrogel structures with high shape fidelity. Then, we confirmed the excellent biocompatibility of the above biofabrication approach. The Escherichia coli survived 93% ± 4.0% in printed BLMs, with uniform distribution throughout the structure. As a proof-of-concept, we finally manufactured a test strip-like heavymetal biosensor capable of plug-and-play detecting mercury (II) in water using the aforesaid approach. To our knowledge, this is the first study to employ 3D bioprinted BLMs for the detection of prevalent heavymetal pollutants. Our research shed light on the versatility of DBB in BLMs construction, which is not restricted to two-dimensional patterns. Moreover, our results are expected to innovate heavymetal biodetection and improve detection efficiency and sensitivity.

Federated Learning-Assisted Coati Deep Learning-Based Model for Intrusion Detection in MANET

MANET is a set of self-arranged, wirelessly connected nodes. Each mobile ad hoc network node acts as a router to send the packet from the source node to the destination node. MANET nodes’ random movements and decentralized architecture pose security challenges, making them vulnerable to various attacks like node selfishness, network partition, black hole, and DoS due to limited hardware resources. In this paper, a novel Hybrid Intrusion DEtection for MANet (HIDE-MAN) technique has been proposed to detect intrusion like DDoS and MitM attacks in MANET. The proposed HIDE-MAN framework initiates by preprocessing malicious data packets through data cleaning and data transformation resulting in the creation of high-dimensional vectors. The intrusion detection system then makes use of the CO-BiLSTM model, which is based on the actions of Coati and BiLSTM. It categorizes outputs into DDoS attacks, MitM attacks, or the absence of any attacks. Federated learning with GAN networks allows for the aggregation of updates from multiple local models distributed across MANET. Assessment metrics such as accuracy, precision, F1 score, detection rate, recall, and security rate have been utilized to assess the efficacy of the proposed HIDE-MAN method. The comparative analysis shows that the detection rate of the proposed HIDE-MAN is greater by 18.9%, 18.07%, and 4.03% than that of the current KBIDS, WOA-DNN, and MSA-GCNN techniques, respectively.

Hardware-tailored diagonalization circuits

A central building block of many quantum algorithms is the diagonalization of Pauli operators. Although it is always possible to construct a quantum circuit that simultaneously diagonalizes a given set of commuting Pauli operators, only resource-efficient circuits can be executed reliably on near-term quantum computers. Generic diagonalization circuits, in contrast, often lead to an unaffordable SWAP gate overhead on quantum devices with limited hardware connectivity. A common alternative is to exclude two-qubit gates altogether. However, this comes at the severe cost of restricting the class of diagonalizable sets of Pauli operators to tensor product bases (TPBs). In this article, we introduce a theoretical framework for constructing hardware-tailored (HT) diagonalization circuits. Our framework establishes a systematic and highly flexible procedure for tailoring diagonalization circuits with ultra-low gate counts. We highlight promising use cases of our framework and – as a proof-of-principle application – we devise an efficient algorithm for grouping the Pauli operators of a given Hamiltonian into jointly-HT-diagonalizable sets. For several classes of Hamiltonians, we observe that our approach requires fewer measurements than conventional TPB approaches. Finally, we experimentally demonstrate that HT circuits can improve the efficiency of estimating expectation values with cloud-based quantum computers.

Proton-free induction decay MRSI at 7 T in the human brain using an egg-shaped modified rosette K-space trajectory.

Proton (1H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency because of their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time. Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in 6 min in a 2D slice in the brain. An elliptical phase-encoding sequence was measured in one volunteer in 22 min, and a 3D sequence was measured in one volunteer within 19 min. The SNR per-unit-time, linewidth, Cramer-Rao lower bounds (CRLBs), lipid contamination, and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory. Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an SNR per-unit-time increase of up to 12% compared to rosette's and 23% compared to elliptical phase-encoding, dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in vivo, and phantom data. The CRLBs of γ-aminobutyric acid and N-acetylaspartylglutamate improved non-significantly for the modified rosette trajectory, whereas the linewidths and lipid contamination remained similar. By optimizing the shape of the rosette trajectory, the modified rosette trajectories achieved higher SNR per-unit-time and enhanced data quality at the same scan time.

Based on Improved Lightweight YOLOv8 for Vehicle Detection

In urban road traffic, vehicle detection in intelligent transportation system can effectively improve road traffic operation efficiency and road safety. Based on this goal, this paper is based on the YOLOv8 framework, using the improved lightweight YOLOv8 model to realize vehicle detection, the method replaces the Backbone network in the YOLOv8n model with a more efficient and lightweight EfficientViT network, and further adds the CBAM attention mechanism in Neck to enhance the detection precision, and later, Conv is replaced with the GhostConv model in Head to reduce the number of parameters and increase the speed of detection. This model greatly improves the detection precision and efficiency. We validate our approach on the UA-DETRAC vehicle detection dataset, and the experimental results show that the proposed detection model Precision reaches 91.17%, mAP@0.5 reaches 75.45%, and Recall reaches about 70.01%. Compared with the original YOLOv8n model, Precision improves by 4.6%, mAP@0.5 improves by about 4.3%, and Recall improves by about 9.1%. Through experimental validation, the model performs well in detecting various complex road traffic scenarios, and this innovative approach can provide strong support for the development of intelligent transportation systems, such as deployed on devices with limited mobile hardware resources, and is expected to make further breakthroughs in future applications.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

A switched full duplex MIMO architecture with digital linear and nonlinear cancellation

Enabling full duplex (FD) in MIMO systems is challenging due to increased hardware complexity and increased training overhead required for canceling not only self-interference (SI) but also cross-link-interference (CLI) signals, considering both linear and nonlinear effects on each stream. In this paper, we propose switched FD MIMO (FD-SW-MIMO) architecture as a low-complexity, low-overhead solution, which enables stream-based nonlinear estimation to be performed independently from channel estimation, so that those nonlinear reference signals are fed to linear SI and CLI cancellation stages. For improved performance at high transmit power levels, the Random Fourier Features - Least Mean Squares (RFF-LMS) algorithm is employed on the residual SI and CLI signals per stream. Our experiments conducted on a software-defined radio based 2x2 FD MIMO test setup reveal that the proposed FD-SW-MIMO architecture can provide up to 12 dB enhancement over linear only digital cancellation. The proposed architecture requires only minor hardware modification(s), avoiding active analog cancellation circuitry and extra Tx/Rx chains. Requiring the same training overhead as linear only cancellation, FD-SW-MIMO architecture can quadruple the rate of HD SISO for low to moderate transmit power levels, and for high transmit power levels, the HD SISO rate is tripled due to slightly increased overhead.

Multi‐Material Gradient Printing Using Meniscus‐enabled Projection Stereolithography (MAPS)

AbstractLight‐based additive manufacturing methods are widely used to print high‐resolution 3D structures for applications in tissue engineering, soft robotics, photonics, and microfluidics, among others. Despite this progress, multi‐material printing with these methods remains challenging due to constraints associated with hardware modifications, control systems, cross‐contamination, waste, and resin properties. Here, a new printing platform coined Meniscus‐enabled Projection Stereolithography (MAPS) is reported, a vat‐free method that relies on generating and maintaining a resin meniscus between a crosslinked structure and bottom window to print lateral, vertical, discrete, or gradient multi‐material 3D structures with no waste and user‐defined mixing between layers. MAPS is compatible with a wide range of resins shown and can print complex multi‐material 3D structures without requiring specialized hardware, software, or complex washing protocols. MAPS's ability to print structures with microscale variations in mechanical stiffness, opacity, surface energy, cell densities, and magnetic properties provides a generic method to make advanced materials for a broad range of applications.

Analisis Pelayanan Publik Berbasis Inovasi (E-Government) di Kantor Wilayah Kementrian Agama Provinsi Gorontalo

This study aims to analyze how public service based on innovation (E- Government) is implemented at the Regional Office of the Ministry of Religious Affairs of Gorontalo Province, focusing on 1) support, 2) capacity, and 3) benefits. To achieve this objective, qualitative methods were used, with descriptive data analysis. Data collection techniques included observation, interviews, and documentation, employing qualitative descriptive analysis. The findings indicate that the E-Government-based public service innovation carried out by the Regional Office of the Ministry of Religious Affairs of Gorontalo Province involves: 1) support in the form of training for staff on system usage and community outreach to ensure understanding of the SISKOHAT system. Additionally, coordination with system developers was conducted to improve and update the application according to user needs. 2) Capacity refers to several IT tools and systems that support electronic public services; however, frequent technical issues, such as system disruptions or hardware limitations, hinder electronic service delivery. 3) The benefits of SISKOHAT include efficient and integrated management of congregation data, which enhances service delivery to congregants by providing more accurate information.