Local Approach Research Articles

Remote sensing of backward reflection from stimulated axion decay

We propose a method for remotely detecting backward reflection via induced decay of cold dark matter such as axion in the background of a propagating coherent photon field. This method can be particularly useful for probing concentrated dark matter streams by Earth’s gravitational lensing effect. Formulae for the stimulated reflection process and the expected sensitivities in local and remote experimental approaches are provided for testing eV scale axion models using broad band lasers. The generic axion-photon coupling is expected to be explorable up to O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(10−12) GeV−1 and O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(10−22) GeV−1 for the idealized local and remote setups, respectively.

Uncovering the key working mechanisms of a complex community-based obesity prevention programme in the Netherlands using ripple effects mapping

BackgroundMuch remains unknown about how complex community-based programmes can successfully achieve long-term impact. More insight is needed to understand the key mechanisms through which these programmes work. Therefore, we conducted an in-depth study in five communities that implemented the Healthy Youth, Healthy Future (JOGG) approach, a Dutch community-based obesity prevention programme. We aimed to identify perceived outcomes and long-term impacts among local stakeholders and explore potential causal pathways and working mechanisms.MethodsWe used ripple effects mapping (REM), a qualitative participatory method to map outcomes and identify causal pathways, in five communities. We involved 26 stakeholders, professionals and policy-makers affiliated with the local JOGG approach, spread over eight REM sessions and conducted individual interviews with 24 additional stakeholders. To uncover working mechanisms, we compared outcomes and causal pathways across communities.ResultsOver 5–9 years of implementation, participants perceived that JOGG had improved ownership of local stakeholders, health policies, intersectoral collaboration and social norms towards promoting healthy lifestyles. Causal pathways comprised small initial outcomes that created the preconditions to enable the achievement of long-term impact. Although exact JOGG actions varied widely between communities, we identified five common working mechanisms through which the JOGG approach contributed to causal pathways: (1) creating a positive connotation with JOGG, (2) mobilizing stakeholders to participate in the JOGG approach, (3) facilitating projects to promote knowledge and awareness among stakeholders while creating successful experiences with promoting healthy lifestyles, (4) connecting stakeholders, thereby stimulating intersectoral collaboration and (5) sharing stakeholder successes that promote healthy lifestyles, which gradually created a social norm of participation.ConclusionsThe JOGG approach seems to work through activating initial stakeholder participation and bolstering the process towards ownership, policy change, and intersectoral collaboration to promote healthy lifestyles. Key working mechanisms can inform further development of JOGG as well as other complex community-based prevention programmes.

Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.

Effects of oil shocks on markets in G7 countries

This paper aims to assess the effects of oil price shocks on the stock markets of the G7 countries. We develop an oil sentiment indicator that measures the volatility of oil prices. We analyze oil shocks on the stock market of the G7 countries, using a structural VAR and Local Projection approach. Our results suggest that oil shocks explain 8% of the US stock market, 10% in Germany, and 7% in the UK. These results point out the possibility of using this sentiment variable for forecasting the stock market’s volatility.

Neural force functional for non-equilibrium many-body colloidal systems

Abstract We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.

Disparities in access to water, sanitation, and hygiene (WASH) services and the status of SDG-6 implementation across districts and states in India

IntroductionAccess to affordable and improved Water, Sanitation, and Hygiene (WASH) facilities is essential for people's daily lives, and it is the primary goal of Sustainable Development Goal 6 (SDG-6). However, achieving this goal is a significant challenge for many countries, including India. The aim of this study is to assess the progress made towards achieving SDG-6 targets in Indian districts, states, and Union Territories (UTs) and to identify clusters by measuring spatial inequality of WASH coverage in India. Aim and objectiveThe primary objective of this study is to measure the progress made towards achieving the SDG-6 targets for WASH facilities in Indian districts, states, and UTs. To fulfill this objective, the study used the household data of the National Family Health Survey-5 (NFHS-5) conducted from 2019 to 21. Data and methodsThe study used the household data of NFHS-5, which is a nationally representative survey that provides information on household and individual-level characteristics related to health and nutrition. The study identified the variables associated with WASH and created a composite index to measure WASH coverage separately and combined. The study used Gini coefficient to show WASH inequality, and Moran's statistics were used to show spatial dependency. ResultThe study found that the inequality of improved water coverage sources in Indian districts was high. Western and northeastern districts need to catch up in terms of achieving the SDG-6 targets. The value of the Gini coefficient (0.29) indicates that inequality in sanitation coverage is also high. All states have reached close to SDG-6 achievement in hygiene indicators. Goa, Sikkim, Andaman & Nicobar Islands, and Lakshadweep are close to the overall WASH coverage achievements of SDG-6. However, Jharkhand, Orissa, Tripura, Assam, and Rajasthan are behind in meeting the goal of SDG-6. ConclusionThe study suggests that more government initiatives and investments are needed to increase the availability, accessibility, and affordability of WASH facilities to improve WASH conditions in western and northeastern Indian districts. The localization or bottom-up approach by giving responsibility to rural and urban local bodies can also help enforce the achievement of SDG-6. The findings of this study can be used to guide policymakers in developing targeted interventions to improve WASH conditions and reduce inequality in India.

Redesigning heat exchanger channels: A local entropy generation approach

This paper proposes a redesign technique for heat exchanger channels based on a Local Entropy Generation (LEG) analysis and numerical simulations. The approach first divides the computational domain into zones and then identifies the zones responsible for increasing irreversibilities due to viscous and heat transfer effects. By evaluating different geometric features and flow conditions, the wall shape is modified (zone by zone) with the configurations that offer the lowest local entropy generation rates. The redesigned channels demonstrate that sudden changes in geometry along the streamwise direction reduce the size of regions with high thermal entropy generation and promote the creation of vortices, thereby reducing the magnitude of thermal irreversibilities. Smooth wall shape transitions might not be desirable when the goal is to improve heat transfer. The redesign process enhances the thermal performance of the system without significantly increasing power demand. The proposed methodology is evaluated using three wavy channels with a non-uniform amplitude-to-wavelength ratio (β1,β2,andβ3) since they allow for better management of wall geometry. As demonstrated for case β1, the LEG analysis enables the construction of new channels with a thermal performance 3.9 % superior to those with a uniform amplitude-to-wavelength ratio of α = 0.16 (best-case scenario), but with approximately 20 % less pumping power. While this study employs wavy channels as a case study, the methodology is broadly applicable to various heat exchanger configurations, including trapezoidal, triangular, and zigzag channels, making it a versatile tool for improving thermodynamic performance across different designs.

KAJIAN KONSEPTUAL SILABEL RITME GANDANG MINANGKABAU

This article tryes to initiate the concept of Minangkabau rhythm syllables, which is a method in music learning related to audiation in rhythm learning, the urgency in this study explores the basic elements of the audiation system that can be applied to music learning related to rhythm syllables based on local approaches, taking into account previous concepts that have been popular in recent schools. In West Sumatra, the concepts of syllable rhythm such as Zoltan Kodaly, Kannokol, American Style Syllables, and Edwin Gordon are not so popular, but environments such as schools and art studios have their own ways of approaching learning music related to pronouncing sounds, so that in this study it is addressed as an interesting and crucial thing to study and formulate further into formulations. The method used in this research uses a qualitative paradigm with a literature review approach. Where the concept of previous rhythmic syllables is used as a reference in analyzing, bringing up findings, and juxtaposing the findings with the initiated Minangkabau rhythmic syllable formula, so that the Minangkabau rhythmic syllables can be generalized and adapted, but still referring to the habits of the local community material. Based on the results of the analysis, like Gordon’s concept, it is found that syllables or audiation appear after the formation of rhythms, patterns and sound colors from an instrument, which can then strengthen the form of audiation into verbal form.

PENANAMAN NILAI PENDIDIKAN KARAKTER CERITA RAMAYANA PADA MATERI PENDIDIKAN AGAMA HINDU DI SD FAJAR HARAPAN

This study seeks to examine the process of instilling character education values through the Ramayana story in the context of Hindu Religious Education at SD Fajar Harapan. The Ramayana, a narrative rich in life values such as hard work, responsibility, social care, and creativity, serves as a key educational tool for fostering character development in students. Employing a descriptive qualitative approach, this research gathers data through observations, interviews, and document analysis. The results indicate that teachers play a pivotal role in guiding students to identify and comprehend the character values embedded in the Ramayana. Through the use of video-based learning strategies, students are encouraged to observe and analyze the characters within the story, followed by group discussions that facilitate the differentiation between positive and negative values. The internalization of these values occurs progressively, beginning with the teacher's interpretation and transitioning into interactive discussions that allow students to relate these values to their everyday lives. In conclusion, the Ramayana story proves to be an effective medium for character education, particularly within an educational framework that prioritizes cultural and local value approaches.

Development of an Ethnoscience-Based Teaching Module on Incung Kerinci Batik as a Learning Resource for Biology of Plant Material (Plantae)

Ethnoscience can be used and integrated into various learning models, one of which is the 21st century learning model in the form of a Project based learning (PjBL) learning model with an ethnoscience approach. This research was conducted in Kerinci Regency and Sungai Banyak City, precisely at SMA Negeri 1 Sungai Banyak and the Kerinci Incung batik studio, this study aims to produce ethnoscience-based teaching modules that can improve students' understanding of plants through a local cultural approach. The research method used is the ADDIE development model, the results of the material expert validation were 98.60% and the results of the media expert validation were 96.30% with a very valid category and feasible to be applied. Continued with product trials using random sampling data collection techniques and involving classes XA and % with practical and very practical categories. Practicality tests were also conducted on six biology teachers in class X obtained with percentages, namely: 75%; 100%; 91.25%; 93.75%; 89.16%; and 95.41% with an average category of very practical

Sexual harassment prevention application with a Moerija Cultural approach for students with special needs

In Indonesia, the government is trying to safeguard and protect children from sexual crimes. Sexual crimes are particularly vulnerable to students with special needs. These students have limitations and minimal access to sexual education, making them frequent targets of sexual harassment and abuse. Students with special needs are at a higher risk of becoming victims of sexual violence due to their unique vulnerabilities. The impact of such abuse includes inferiority, anxiety, panic, and even depression. This must be addressed immediately, as it can disrupt the psychological and emotional development of students with special needs, hindering their potential to thrive and compete in the increasingly competitive digital era. The aim of this community service activity is to provide digital media with a local Moerija cultural approach to introduce sexual and application-based education. The method used in these community service activities is participant active learning, ensuring engagement and practical application. The results obtained are the development of an application designed to prevent sexual violence, utilizing a culturally relevant Moerija approach to raise awareness and enhance protective measures.

Wirelessly Powered Visible Light-Emitting Implant for Surgical Guidance during Lumpectomy.

Achieving negative surgical margins, defined as no tumor found on the edges of the resected tissue, during lumpectomy for breast cancer is critical for mitigating the risk of local recurrence. To identify nonpalpable tumors that cannot be felt, pre-operative placements of wire and wire-free localization devices are typically employed. Wire-free localization approaches have significant practical advantages over wired techniques. In this study, we introduce an innovative localization system comprising a light-emitting diode (LED)-based implantable device and handheld system. The device, which is needle injectable and wire free, utilizes multiple wirelessly powered LEDs to provide direct visual guidance for lumpectomy. Two distinct colors, red and blue, provide a clear indication of tissue depth: blue light is absorbed strongly in tissue, visible within a close range of <1 cm, while red light remains visible through several centimeters of tissue. The LEDs, integrated with an impedance-matching circuit and receiver coil, are encapsulated in biocompatible epoxy for injection with a 12 G needle. Our findings demonstrate that the implant exhibits clearly perceivable depth-dependent color changes and remains visible through >2 cm of ex vivo chicken breast and bovine muscle tissue using less than 4 W of transmitted power from a handheld antenna. These miniaturized needle-injectable localization devices show promise for improving surgical guidance of nonpalpable breast tumors.

Localized delivery of Roxadustat via cubosomes-based thermosensitive in-situ hydrogel enhances diabetic wound healing by stabilizing HIF-1α in rats

Chronic non-healing wounds are a major complication among diabetic patients, significantly affecting their quality of life. The dysfunction of HIF-1, a crucial regulator of wound healing, contributes to impaired wound repair in diabetes. Roxadustat (ROX), an HIF-1α stabilizer, has emerged as a promising therapeutic candidate for addressing chronic wounds. Accordingly, a few studies have investigated the systemic administration of ROX for wound healing. However, concerns have arisen about potential side effects. Our study aimed to address these limitations by developing a localized delivery approach using ROX-loaded cubosomes (ROX-CUs) incorporated into a thermosensitive hydrogel. ROX-CUs were prepared and evaluated for particle size (125.6 ± 3.11 nm), zeta potential (−26.60 ± 2.08 mV), and entrapment efficiency (94.23 ± 2.93 %). ROX-CUs were then integrated into a poloxamer-based thermosensitive hydrogel with varying ratios of poloxamers 188 and 407. The selected formulation (F13) showed a phase transition temperature of 36.5 ± 0.4 °C and a drug content of 97.80 ± 1.60 %. In vitro drug release studies demonstrated a biphasic release for F13: an initial burst after 3 h (15.25 %) followed by sustained release up to 48 h (52.13 %), offering sustained release advantages. In vivo studies on diabetic rats revealed that F13 significantly accelerated wound closure (93 % reduction), with enhanced tissue characteristics, including neovascularization and increased collagen deposition. Additionally, F13 upregulated the HIF-1α and other tissue remodeling factors, creating an optimal environment for tissue repair. Consequently, ROX-CUs incorporated into a thermosensitive hydrogel hold significant promise as a novel therapeutic strategy for promoting wound healing in clinical settings.

Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation

Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.

Experimental and numerical determination of the fatigue notch factor in as‐built wire arc additive manufacturing steel components

AbstractParts manufactured by wire arc additive manufacturing (WAAM) are characterized by a peculiar surface morphology, namely, surface waviness, that negatively affects the fatigue performance. To exploit the full potential of WAAM and minimize the need for postproduction work, it is crucial to utilize the components in the as‐built state. This is because conventional machining techniques, typically employed for postprocessing operations, severely curtail the freedom of geometry of the components. This study focuses on an experimental and numerical characterization of the notch effect of the surface waviness for an AISI 308 LSi stainless steel. This is done by quantifying the fatigue notch factor in a probabilistic fashion, considering the results of ad‐hoc designed fatigue tests. A finite element model is developed by considering a 3D scan of the geometry of WAAMed plates, allowing to determine the theoretical stress concentration factor. The fatigue notch factor is also estimated from the numerical model by making use of the gradient correction according to the FKM guidelines. To validate the numerical approach, test data produced for different testing conditions are correlated by using the local stress approach, showing that classical methods are also applicable to additive manufactured parts.

A hound-inspired pre-hybridized genetic approach for router placement in wireless mesh networks

In the last years, wireless mesh networks (WMNs) have gained more and more popularity in many research and industrial applications thanks to their easy implementation, maintenance, and great reliability at a low cost. Nevertheless, for a large number of nodes, the performance of such networks is heavily influenced by the positioning of routers and gateways over the area to be covered. In this paper, we tackle the router placement problem, which is known to be NP-hard, and its approximate solution through a meta-heuristic approach. The proposed solution empowers the benefits offered by a genetic algorithm pre-hybridized with a local search approach inspired by the behavior of hound dogs. The basic idea is to exploit the dogs’ capabilities in moving throughout the solution space to effectively explore it by placing themselves in areas that are more favorable for achieving a high-quality approximate solution in a reasonable time. Experimental results on several benchmarking instances and comparisons with the most effective state-of-the-art algorithms have demonstrated the potential of the proposed approach. This is evidenced by very high connectivity and coverage, a low number of generations, and a small GA population required for convergence. This results in low computational effort and significant time savings, which are of paramount importance in IoT and edge scenarios. We remark that, although offering potential, at the current state, our proposal is not able to adapt to areas with obstacles or irregular shapes.

A low-complexity parallel local remote microphone technology for multichannel narrowband active noise control systems

Narrowband active noise control (NANC) systems can effectively eliminate periodic disturbances at its error sensors, but sometimes the error sensors are inconvenient to be placed at target locations for a long period. Remote microphone technology (RMT) can tackle this problem by moving quiet zones to the target areas. Nevertheless, the RMT-NANC system has significantly higher computational complexity than conventional NANC systems, particularly for multichannel systems. This hinders their implementation in real-time systems with limited computational resources. The additional computational burden stems from multiple convolutions involving the estimated global high-order secondary paths and observation filters when estimating the virtual error signals. To address this problem, a low-complexity parallel local RMT is proposed based on the narrowband filtered-x least mean square (PLRMT-NFxLMS) algorithm in this paper. Using a two-step local modeling approach, multiple local modeling low-order filters for both secondary paths and observation filters are constructed during the training stage. In the subsequent control stage, virtual error signals are estimated in parallel for different frequency components using these filters instead of global modeling filters, thereby alleviating the substantial computational cost arising from convolutions between lengthy vectors. Moreover, a filtered-error structure, termed the PLRMT-NFeLMS algorithm, is introduced in the proposed algorithm to further reduce the computational complexity. A comprehensive analysis of computational complexity is provided to demonstrate the superiority of the two proposed algorithms. Extensive simulations and real-time experiments were conducted to validate the feasibility and practicability of these proposed methods.

MBHFuse: A multi- branch heterogeneous global and local infrared and visible image fusion with differential convolutional amplification features

Fusing infrared and visible imagery aims to harness their complementary spectral data, enhancing output image quality, sharpness, and content. However, convolutional neural networks (CNNs) are not capable of capturing long-range dependencies, while the Transformer architecture faces the challenge of consuming huge computational resources. To address these critical challenges, this paper proposes a novel framework named MBHFuse, which employs a multi-branch heterogeneous global and local image fusion approach. To effectively extract global and local features, we design a multi-branch heterogeneous encoder module and introduce a differential convolution amplification module (DCAM) to further extract complementary information. Additionally, we devise a new loss function, incorporating multi-branch feature decomposition loss, intensity loss, gradient loss, mean squared error loss, and structural similarity loss, for training the proposed MBHFuse model. Through extensive experiments on public datasets, we demonstrate that the proposed framework outperforms other state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations. Our code will be available at https://github.com/sunyichen1994/MBHFuse.

An extensive replication study of the ABLoTS approach for bug localization

An extensive replication study of the ABLoTS approach for bug localization

Interval-Censored Linear Quantile Regression

Censored quantile regression has emerged as a prominent alternative to classical Cox’s proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While quantile regression has been extensively studied for right-censored survival data, methodologies for analyzing interval-censored data remain limited in the survival analysis literature. This article introduces a novel local weighting approach for estimating linear censored quantile regression, specifically tailored to handle diverse forms of interval-censored survival data. The estimation equation and the corresponding convex objective function for the regression parameter can be constructed as a weighted average of quantile loss contributions at two interval endpoints. The weighting components are nonparametrically estimated using local kernel smoothing or ensemble machine learning techniques. To estimate the nonparametric distribution mass for interval-censored data, a modified EM algorithm for nonparametric maximum likelihood estimation is employed by introducing subject-specific latent Poisson variables. The proposed method’s empirical performance is demonstrated through extensive simulation studies and real data analyses of two HIV/AIDS datasets. Supplementary materials for this article are available online.