Local Processing Research Articles

Photoactivatable Xanthone (PaX) Dyes Enable Quantitative, Dual Color, and Live-Cell MINFLUX Nanoscopy.

The single-molecule localization concept MINFLUX has triggered a reevaluation of the features of fluorophores for attaining nanometer-scale resolution. MINFLUX nanoscopy benefits from temporally controlled fluorescence ("on"/"off") photoswitching. Combined with an irreversible switching behavior, the localization process is expected to turn highly efficient and quantitative data analysis simple. The potential in the recently reported photoactivable xanthone (PaX) dyes is recognized to extend the list of molecular switches used for MINFLUX with 561nm excitation beyond the fluorescent protein mMaple. The MINFLUX localization success rates of PaX560 , PaX+560, and mMaple are quantitatively compared by analyzing the effective labeling efficiency of endogenously tagged nuclear pore complexes. The PaX dyes prove to be superior to mMaple and on par with the best reversible molecular switches routinely used in single-molecule localization microscopy. Moreover, the rationally designed PaX595 is introduced for complementing PaX560 in dual color 561nm MINFLUX imaging based on spectral classification and the deterministic, irreversible, and additive-independent nature of PaX photoactivation is showcased in fast live-cell MINFLUX imaging. The PaX dyes meet the demands of MINFLUX for a robust readout of each label position and fill the void of reliable fluorophores dedicated to 561nm MINFLUX imaging.

Universities and human rights in cities

Human rights globalization is, counterintuitively, largely a process of localization. We focus on the role of universities in their localities as a way of gaining insight into the complex ways in which they shape human rights outcomes both on and beyond campus. This topic has, to our knowledge, received no explicit scrutiny in the human rights literature, where the presumption of academic workers producing and disseminating knowledge in pursuit of justice—and often on behalf of the downtrodden—prevails. We have no wish to deny or dismiss the importance of that work; our purpose is to paint a broader and more detailed picture that comprises not only this work but also the role of universities as institutions in the political economy of human rights of their communities and the institutional and political dynamics that shape and constrain human rights scholarship.

Localizing human rights through the Sustainable Development Goals: The case of Los Angeles

The United Nations Sustainable Development Goals (SDGs) have achieved widespread influence among various state and nonstate actors globally. Although the UN General Assembly intended the SDGs for national governments, local-level actors have emerged as key stakeholders in defining and implementing the goals. Human rights movements have long advocated for the inclusion of local voices in international law. I bridge these discourses to investigate how the SDGs framework can socialize government actors to incorporate human rights principles in local governance. Specifically, I explore how local-level actors have advanced human rights through the localization—local adoption and adaptation—of the 2030 Agenda for Sustainable Development in the case of Los Angeles. The iterative process of localization in Los Angeles illustrates the dialectical process of human rights globalization and emerging institutions that can enable rights realization locally. The case highlights the (1) global and local factors that shape localization, (2) how partnerships lead to more explicit attention to human rights over time, and (3) the ways in which Los Angeles is now shaping localization transnationally. I discuss the critical role of local government actors as agents of norm diffusion, and the need to expand community engagement mechanisms in pursuit of SDG localization and human rights globalization.

Efficient Neural Networks on the Edge with FPGAs by Optimizing an Adaptive Activation Function.

The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as a real-valued time-delay neural network (RVTDNN), an augmented real-valued time-delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no block RAMs compared to the DNN with similar accuracy. We experimentally validated this computationally efficient and memory-saving FPGA implementation of the SSCNN for digital predistortion of radio-frequency (RF) power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states

By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the availability of all the network states for the upcoming time slot, not applicable in practical MECO with differently-aged network states (e.g., fast-changing practical wireless channels and instantly observable local task arrivals). This paper proposes a novel hybrid learning approach that optimizes the instantaneous local processing and predictive computation offloading decisions. We establish a new hybrid learning approach for instantaneous local processing and predictive computation offloading decisions by integrating the learning techniques of stochastic gradient descent (SGD) and online convex optimization (OCO). By decomposing the primal problems, the proposed approach can be implemented in a decentralized manner. We prove that the optimality loss resulting from the differently-aged network states can diminish with the decreasing stepsizes of SGD and OCO. Simulation results validate the asymptotic optimality and superiority of the proposed hybrid learning approach, as compared to state-of-the-art (e.g., based on OCO techniques).

Localization of RNAs to the mitochondria-mechanisms and functions.

The mammalian mitochondrial proteome comprises over 1000 proteins, with the majority translated from nuclear-encoded messenger RNAs (mRNAs). Mounting evidence suggests many of these mRNAs are localized to the outer mitochondrial membrane (OMM) in a pre- or cotranslational state. Upon reaching the mitochondrial surface, these mRNAs are locally translated to produce proteins that are cotranslationally imported into mitochondria. Here, we summarize various mechanisms cells use to localize RNAs, including transfer RNAs (tRNAs), to the OMM and recent technological advancements in the field to study these processes. While most early studies in the field were carried out in yeast, recent studies reveal RNA localization to the OMM and their regulation in higher organisms. Various factors regulate this localization process, including RNA sequence elements, RNA-binding proteins (RBPs), cytoskeletal motors, and translation machinery. In this review, we also highlight the role of RNA structures and modifications in mitochondrial RNA localization and discuss how these features can alter the binding properties of RNAs. Finally, in addition to RNAs related to mitochondrial function, RNAs involved in other cellular processes can also localize to the OMM, including those implicated in the innate immune response and piRNA biogenesis. As impairment of messenger RNA (mRNA) localization and regulation compromise mitochondrial function, future studies will undoubtedly expand our understanding of how RNAs localize to the OMM and investigate the consequences of their mislocalization in disorders, particularly neurodegenerative diseases, muscular dystrophies, and cancers.

Enhancing Energy Efficiency in Telehealth Internet of Things Systems Through Fog and Cloud Computing Integration: Simulation Study.

The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency. This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems. The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency. Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings. The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.

Dual defect detection and generation system in planar surface

The main problem of applying neural networks in the task of defect detection is that there is a limitation in the size of the set of annotated defect images compared to the set of defect-free images. This imbalance in the dataset poses a challenge for the model to effectively learn and generalize patterns associated with defects. The scarcity of annotated defect samples hinders the model's ability to capture the diverse characteristics of anomalies, making it prone to misdetection and reduced performance on real-world data. To solve this problem, the authors have proposed a neural network architecture based on two neural networks U-Net++ and PatchCore trained in the student-teacher paradigm. In this paradigm, the student model undergoes initial training exclusively on defect-free images. Consequently, disparities emerge in the learned features between the student and teacher models, facilitating the computation of an anomaly metric and streamlining the anomaly localization process. The aim of the research is to improve the efficiency of defect detection by creating methods and algorithms, and dataset augmentation systems, as an option to solve the problem of defect-free images. The overarching goal of this research is to enhance the efficacy of defect detection through the development of methodologies, algorithms, and dataset augmentation systems. This serves as a strategic approach to address the inherent challenge posed by the scarcity of annotated defect images in comparison to their defect-free counterparts. By creating methods and augmenting datasets, we aim to mitigate the limitations imposed by the uneven distribution of defect and defect-free samples, ultimately advancing the robustness and performance of defect detection models. A dual system of defect detection based on the U-Net++ pre-trained network and feature estimation of individual fragments based on the PatchCore network is presented. The research incorporates computational experiments designed to assess the effectiveness of the proposed method in comparison to existing counterparts. An in-depth efficiency analysis has been conducted, focusing on dataset enrichment using the generative-adversarial network ResStyleGAN. This examination aims to provide empirical evidence of the method's performance and its potential advantages over established approaches in the context of defect detection. The method developed, centered around the dual system PatchNet++ and preliminary generation of new defects using ResStyleGAN, is positioned for practical application in real production scenarios. Subsequent stages involve a detailed analysis to evaluate its performance and suitability in real-world settings.

Analyzing the Diversity of MYB Family Response Strategies to Drought Stress in Different Flax Varieties Based on Transcriptome Data.

The MYB transcription factor family has numerous members, and is involved in biological activities, such as ABA signaling, which plays an important role in a plant's resistance to abiotic stresses such as drought. However, the diversity of MYB members that respond to drought stress and their regulatory mechanisms in different flax varieties were unclear. In this study, we obtained 855.69 Gb of clean data from 120 flax root samples from 20 flax (Linum usitatissimum L.) varieties, assembled 92,861 transcripts, and identified 434 MYB family members in each variety. The expression profiles of the MYB transcription factor family from 20 flax varieties under drought stress were analyzed. The results indicated that there are four strategies by which the MYB family responds to drought stress in these 20 flax varieties, each of which has its own specific processes, such as development, reproduction, and localization processes. The four strategies also include common biological processes, such as stimulus responses, metabolic processes, and biological regulation. The WGCNA method was subsequently employed to identify key members of the MYB family involved in response strategies to drought stress. The results demonstrated that a 1R-MYB subfamily gene co-expression network is significantly related to the gibberellin response and cytokinin-activated signaling pathway processes in the 'Strategy 4' for MYB family response to drought, identifying core genes such as Lus.scaffold70.240. Our results showed a diversity of MYB family responses to drought stress within flax varieties, and these results contribute to deciphering the mechanisms of the MYB family regulation of drought resistance. This will promote the more accurate breeding development of flax to adapt to agricultural production under drought conditions.

Understanding Acharya’s Concept on Norms Localization: A Lesson Learned from Localization of Transnational Norms on Banning Female Genital Mutilation (FGM) in Gorontalo, Indonesia

This paper is a critical review of Amitav Acharya's concept related to the process of global norm adaptation. Acharya wrote that norm internalization leads to three possible outcomes, i.e., the norms being accepted (norm displacement), adapted and modified (localization), or rejected (resistance rejection). This paper will analyze the localization of transnational norms to the domestic sphere, both the process, and its result. To understand Acharya's concept, the authors use a case study of global norm localization on anti Female Genital Mutilation (FGM) in Gorontalo, Indonesia. In Gorontalo, the global norm of FGM was initially accepted by the people and was implemented as government policies. However, later on there was rejection by the people which pressured for the modification of the regulation. According to Acharya, this was made possible by the role of key actor of norm adaptation, which are the local agents.This paper tries to answer the question "How is the process of localization of transnational norms in the case study of female genital mutilation in Gorontalo, Indonesia?" which then contributes to the identification of the different factors that facilitate local agents to successfully modify the transnational norms imposed to the local settings.

Localizing sounds revolving at very high velocities: An auditory wagon-wheel effect

Localizing sound sources as they move around us is a critical function of the auditory system. Yet most research focuses on static sound sources or sources moving at slow velocities. The present work explores circular trajectories at very high velocities well above the velocity at which we lose the sense of direction (∼2.5 rot/s with white noise). As the number of rotations per second approaches the fundamental frequency of the spinning sound, a sense of direction re-emerges. This creates what has been described informally as the auditory equivalent to the wagon-wheel effect: the sound appears to move in one direction when the velocity is below the fundamental frequency, and it appears to move in the opposite direction when the velocity is above the fundamental frequency. We report on two experiments testing this effect with a 200-Hz complex sound using adaptive VBAP spatialization on a 16-loudspeaker array. Experiment 1 (N = 15) confirmed that participants perceived opposite directions when the velocity was below or above the fundamental frequency. Experiment 2 (N = 7) explores the relationship between this effect at very high velocities and the ability to track sound at slower velocities. Preliminary findings suggest some overlap between localization processes at slow and high velocities.

Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults

AbstractWe discuss data of three laboratory stick‐slip experiments on Westerly Granite samples performed at elevated confining pressure and constant displacement rate on rough fracture surfaces. The experiments produced complex slip patterns including fast and slow ruptures with large and small fault slips, as well as failure events on the fault surface producing acoustic emission bursts without externally‐detectable stress drop. Preparatory processes leading to large slips were tracked with an ensemble of ten seismo‐mechanical and statistical parameters characterizing local and global damage and stress evolution, localization and clustering processes, as well as event interactions. We decompose complex spatio‐temporal trends in the lab‐quake characteristics and identify persistent effects of evolving fault roughness and damage at different length scales, and local stress evolution approaching large events. The observed trends highlight labquake localization processes on different spatial and temporal scales. The preparatory process of large slip events includes smaller events marked by confined bursts of acoustic emission activity that collectively prepare the fault surface for a system‐wide failure by conditioning the large‐scale stress field. Our results are consistent overall with an evolving process of intermittent criticality leading to large failure events, and may contribute to improved forecasting of large natural earthquakes.

Automated anatomical landmark detection on 3D facial images using U-NET-based deep learning algorithm.

Facial anthropometry based on 3-dimensional (3D) imaging technology, or 3D photogrammetry, has gained increasing popularity among surgeons. It outperforms direct measurement and 2-dimensional (2D) photogrammetry because of many advantages. However, a main limitation of 3D photogrammetry is the time-consuming process of manual landmark localization. To address this problem, this study developed a U-NET-based deep learning algorithm to enable automated and accurate anatomical landmark detection on 3D facial models. The main structure of the algorithm stacked 2 U-NETs. In each U-NET block, we used 3×3 convolution kernel and rectified linear unit (ReLU) as activation function. A total of 200 3D images of healthy cases, acromegaly patients, and localized scleroderma patients were captured by Vectra H1 handheld 3D camera and input for algorithm training. The algorithm was tested to detect 20 landmarks on 3D images. Percentage of correct key points (PCK) and normalized mean error (NME) were used to evaluate facial landmark detection accuracy. Among healthy cases, the average NME was 1.4 mm. The PCK reached 90% when the threshold was set to the clinically acceptable limit of 2 mm. The average NME was 2.8 and 2.2 mm among acromegaly patients and localized scleroderma patients, respectively. This study developed a deep learning algorithm for automated facial landmark detection on 3D images. The algorithm was innovatively validated in 3 different groups of participants. It achieved accurate landmark detection and improved the efficiency of 3D image analysis.

Role of Qufeng Tongqiao Prescription in the protection of cerebral ischemia and associated molecular network mechanism.

To explore the of Qufeng Tongqiao Prescription in the treatment of cerebral ischemia-reperfusion (CIR) and associated molecular network mechanism. Venny diagram, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis, protein-protein interaction (PPI), hub genes mining, molecular docking, combined with animal experiments and Nissl stain were performed to determine the molecular network mechanism of Qufeng Tongqiao Prescription for CIR treatment. Fifty three intersecting genes between Qufeng Tongqiao Prescription and cerebral ischemia reperfusion were acquired from Venny analysis. GO analysis showed that the main biological process (BP) was response to lipopolysaccharide, and the main cell localization (CC) process was membrane raft, while the most important molecular function (MF) process is Cytokine receptor binding. Moreover, AGE-RAGE signaling pathway in diabetic complications is the most important signaling pathway in KEGG pathway. Through molecular docking, it was found that Astragalus membranaceus was docked with MAPK14, IL4, FOS, IL6, and JUN; pueraria membranaceus was directly docked with JUN and IL4; Acorus acorus was linked to JUN and MAPK14; Ganoderma ganoderma and human were involved in JUN docking, and Ligusticum chuanqi and pueraria could not be docked with MAPK14, respectively. The results of animal experiments showed that Qufeng Tongqiao Prescription significantly improved behavioral performance and reduced the number of neuronal deaths in rats subjected to CIR, and molecular mechanisms are associated with FOS, IL-6, IL4, JUN, and MAPK14, of there, IL-6, as a vital candidator, which has been confirmed by immunostaining detection. Together, Qufeng Tongqiao Prescription has positive therapeutic effect on CIR, and the underlying mechanism is involved MAPK14, FOS, IL4, and JUN network, while IL-6 may be as a vital target.

Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three significant advantages: First, it does not rely on the microphone channel order and does not need to be calibrated in advance, which simplifies the localization process; Second, it can fulfill high accuracy requirements, especially suitable for the scene of multiple fixed targets; Third, it has the advantage of incremental learning, as the times of localization rises, the training set is continuously enriched and the localization results become more precise.

Leveraging Fog Computing, Blockchain, and IoT for Enhanced Healthcare Monitoring Services

Abstract: With the rapid evolution of technology, smart cities have emerged as geographic areas powered by advanced information and communication technologies. These advancements have been significantly influenced by integrating smart technologies, such as the Internet of Things (IoT), blockchain, and fog computing. This paper aims to explore the impact and potential of these technologies on healthcare services within the context of smart cities. Fog computing, as a solution to the challenges faced by integrated Cloud Computing (CC) in IoT applications, offers a promising approach by bringing computation closer to the edge devices. Unlike traditional cloud-based approaches, fog computing enables local data processing and storage, resulting in improved performance, security, and reduced latency. Furthermore, fog computing acts as a middle layer between IoT sensors/devices and cloud data centers, facilitating efficient resource provisioning and service administration. In smart cities, IoT, blockchain, and fog computing are pivotal in enhancing healthcare services. While IoT is widely integrated into healthcare systems, it faces challenges such as cost efficiency, data privacy, and data interoperability. In contrast, blockchain technology offers solutions for safeguarding private data, establishing decentralized databases, and enhancing data interoperability.

Assistance process blood cockle (Anadara granosa) based meatballs as local food to prevent anemia in adolescent girls

Assistance in processing local food based on blood cockle (Anadara granosa) can support the anemia prevention program for young women towards a stunting-free Gorontalo. The number of participants included was 20, it's made up of two craft teachers and two school managers. The participants came from SMAN 1 Tilango, SMAN 1 Batudaa Pantai, SMAN 1 Biluhu, SMAN 1 Dungaliyo, and SMAN 1 Pulubala. It involves 5 students from Gorontalo State University who participated in the MBKM research program. The purpose of this activity is for teachers and school canteen operators to gain knowledge about the processing of blood cockle meatballs and use them as potential for the development of local food products to prevent anemia in young women. The importance of innovation in local food processing, such as introducing new technology and developing healthier and more sustainable products. Activities are carried out using five methods, namely lecture, question-and-answer, discussion, practice, and evaluation methods. The result of this service activity was that the participants received the training material and received a positive response. Most participants stated that this activity helped them to innovate and develop their businesses in terms of diversifying local food processing.

Modeling the association between functional connectivity and lateralization with the activity flow framework

The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function. Recent years have seen growing interest in the association between lateralization and FC. However, FC metrics vary from spurious correlation to causal associations. If lateralization manifests local processing and causal network interactions, more causally valid FC metrics should predict lateralization index (LI) better than FC based on simple correlations. The present study directly investigates this hypothesis within the activity flow framework to compare the association between lateralization and four brain connectivity metrics: correlation-based FC, multiple-regression FC, partial-correlation FC, and combinedFC. We propose two modeling approaches: the one-step approach, which models the relationship between LI and FC directly, and the two-step approach, which predicts the brain activation and calculates the LI. Our results indicated that multiple-regression FC, partial-correlation FC, and combinedFC could significantly improve the model prediction compared to correlation-based FC, which was consistent in a spatial working memory task (typically right-lateralized) and a language task (typically left-lateralized). The one-step and two-step approach yielded similar conclusions. In addition, the finding was replicated in a clinical sample of schizophrenia (SZ), bipolar disorder (BP), and attention deficit hyperactivity disorder (ADHD). The present study suggests that the causal interactions among brain regions help shape the lateralization pattern.

Dynamic Interfaces in Self-Healable Polymers.

It is well-established that interfaces play critical roles in biological and synthetic processes. Aside from significant practical applications, the most accessible and measurable quantity is interfacial tension, which represents a measure of the energy required to create or rejoin two surfaces. Owing to the fact that interfacial processes are critical in polymeric materials, this review outlines recent advances in dynamic interfacial processes involving physics and chemistry targeting self-healing. Entropic interfacial energies stored during damage participate in the recovery, and self-healing depends upon copolymer composition and monomer sequence, monomer molar ratios, molecular weight, and polymer dispersity. These properties ultimately impact chain flexibility, shape-memory recovery, and interfacial interactions. Self-healing is a localized process with global implications on mechanical and other properties. Selected examples driven by interfacial flow and shape memory effects are discussed in the context of covalent and supramolecular rebonding targeting self-healable materials development.

Pelatihan Pengolahan Makanan Lokal untuk Peningkatan Gizi dan Pendapatan Keluarga di Desa Pantai Labu

Pantai Labu Pekan Village, Pantai Labu District, Deli Serdang Regency, is one of the villages whose area is located on the coast. According to the research conducted, there are still toddlers who experience stunting and malnutrition, the low coverage of poverty, and the low knowledge of pregnant women in toddlers causes the growth and development of toddlers to be disrupted and this can affect their health status broadly. The purpose of this research is to apply science and technology to the community to increase knowledge and skills in the health sector, form/develop community groups in the health sector, apply research results to improve public health for the welfare of public health. This research is observational research with a descriptive research design. The research was conducted in June 2023 in Pantai Labu Pekan Village, Pantai Labu District. In this Community Service activity, the methods carried out were training in local pagan-based snack food processing for pregnant women and WUS and conducting demonstrations on making sukahitu cookies, moringa leaves, fish nuggets, fish sandwiches, rebon shrimp crackers and satay donuts. This program to increase the knowledge of pregnant women and wus starts from 4 stages, namely licensing, preparation, implementation and evaluation. The results of community service activities are that there is an increase in the knowledge of pregnant women and WUS about snack foods which can help in efforts to deal with stunting and there is an increase in the knowledge of pregnant women and WUS about snack foods. From snack foods that are highly nutritious to prevent stunting and snacks containing moringa leaves as a way to overcome anemia and breast milk production.