Local Approach Research Articles

Upaya Penyelesaian Non Litigasi Terhadap Kumpul Kebo Di Desa Naiola Kecamatan Bikomi Selatan Kabupaten Timor Tengah Utara

The widespread social phenomenon of "cohabitation" often occurs because there is a legal space that allows this practice to develop. The local government has made various efforts to minimize this phenomenon, but without adequate legal force, these efforts often do not have the desired deterrent effect. However, the local customary law approach can provide a legal basis that is stronger and relevant to local values, so that the community can be more responsive to the regulations implemented. Thus, it is hoped that through this approach, the phenomenon of "cohabitation" can be minimized and overcome more effectively. This research aims to complete a non-litigation resolution of cohabitation in Naiola Village before Law Number 1 of 2023. The research method used in this research is empirical legal research, namely legal research that obtains data from primary data or data obtained directly from the community. Non-litigation resolution of cohabitation often relies on customary law-based approaches and customary deliberation that utilize local values and traditional mechanisms to achieve a harmonious resolution that is accepted by the local community. Thus, this research provides a clear picture of the importance of non-litigation resolution of cohabitation by utilizing customary law and traditional deliberation approaches to achieve a harmonious solution that is accepted by the local community.

Unpacking The Contributing And Mitigating Factors of External Debt In Pakistan: An Ardl And Local Projection Approach

Unpacking The Contributing And Mitigating Factors of External Debt In Pakistan: An Ardl And Local Projection Approach

Strong Consistency of Incomplete Functional Percentile Regression

This paper analyzes the co-fluctuation between a scalar response random variable and a curve regressor using quantile regression. We focus on the situation wherein the output variable is observed with random missing. For this incomplete functional data situation, we estimate the quantile regression by combining two principal nonparametric methods: the local linearity approach (LLA) and the kernel nearest neighbor (KNN) algorithm. We study the asymptotic properties of the constructed estimator by establishing, under general assumptions, uniform consistency over the number of neighborhoods. This asymptotic result provides good mathematical support for the selection of the optimal neighborhood. We examine the feasibility of the constructed estimator using artificially generated data. Moreover, we apply the quantile regression technique in food quality by predicting the riboflavin quantity in yogurt using spectrometry data.

Pengembangan Green Tourism Dengan Penerapan Green Financing Berbasis Kearifan Lokal Di Wilayah Kabupaten Mandailing Natal

This research aims to examine the potential for sustainable tourism development by implementing the Green Tourism and Green Financing approaches based on local wisdom in the Mandailing Natal Regency area. The primary focus of this study is to formulate strategies and implementation models that can enhance the positive impact of tourism on the environment and the local community. The research employs a qualitative methodology with a case study approach. Data is collected through in-depth interviews, field observations, and analysis of relevant documents. Additionally, a local potential mapping and sustainability analysis approach is used to identify opportunities and challenges in developing green tourism in the Mandailing Natal Regency. The findings indicate significant potential for developing green tourism in this area by leveraging local wisdom. The implementation of Green Financing has proven to provide the necessary financial support for sustainable tourism projects. Moreover, the integration of local wisdom in tourism management can create harmonious interactions between tourists and the local community, enhancing the tourist experience and positively impacting local economic development. The results of this research offer practical guidance for local governments, tourism stakeholders, and financial institutions in building sustainable green tourism in the Mandailing Natal Regency

Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma

PurposeThis study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT. MethodsA total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators. ResultsA strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 μm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799–0.921). ConclusionsWe established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.

Photo-uncaging Triggers on Self-Blinking to Control Single-Molecule Fluorescence Kinetics for Super-resolution Imaging.

Super-resolution imaging, especially a single-molecule localization approach, has raised a fluorophore engineering revolution chasing sparse single-molecule dark-bright blinking transforms. Yet, it is a challenge to structurally devise fluorophores manipulating the single-molecule blinking kinetics. In this pursuit, we have developed a triggering strategy by innovatively integrating the photoactivatable nitroso-caging strategy into self-blinking sulfonamide to form a nitroso-caged sulfonamide rhodamine (NOSR). Our fluorophore demonstrated controllable self-blinking events upon phototriggered caging unit release. This exceptional blink kinetics improved the super-resolution imaging integrity on microtubules compared to self-blinking analogues. With the aid of paramount single-molecule fluorescence kinetics, we successfully reconstructed the ring structure of nuclear pores and the axial morphology of mitochondrial outer membranes. We foresee that our synthetic approach of photoactivation and self-blinking would facilitate rhodamine devising for super-resolution imaging.

Real-Time Tool Localization for Laparoscopic Surgery Using Convolutional Neural Network.

Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.

A Landmark Selection Method for Object-Based Visual Outdoor Localization Approaches of Automated Ground Vehicles

A Landmark Selection Method for Object-Based Visual Outdoor Localization Approaches of Automated Ground Vehicles

Terahertz nondestructive layer thickness measurement and delamination characterization of GFRP laminates

Three-dimensional nondestructive location of defects, such as delaminations, in glass fiber-reinforced polymer (GFRP) laminates remains a challenge. Terahertz techniques have shown promise, but their success relies on advanced signal-processing techniques applied to the raw data. The current work presents an advance in the quantitative three-dimensional nondestructive location of delaminations in GFRP laminates. Namely, terahertz time-of-flight tomography, together with adaptive sparse deconvolution based on a two-step iterative shrinkage-thresholding algorithm, as well as the Canny edge-detection operator, are employed in nondestructive measurement of layer thicknesses and to extract the edges of delaminations in GFRP laminates. Compared with the commonly used frequency wavelet-domain deconvolution method or previous implementations of sparse deconvolution, the adaptive sparse deconvolution approach provides a clearer and rapid stratigraphic reconstruction of GFRP laminates while yielding accurate thickness information for each resin layer and low sensitivity to noise. In addition, the proposed edge-detection algorithm presents better performance in estimating the transverse size of delaminations, compared to the common −6 dB drop approach. Finally, our experiments verify the effectiveness of the proposed signal and image processing approaches for three-dimensional localization of delamination defects in GFRP laminates and the quantitative characterization of layer thickness.

Fatigue Strength Assessment of Friction Welds under Consideration of Residual Stress.

A reliable local-fatigue assessment approach for rotary friction-welded components does not yet exist. The scope of this paper is to present test results for the fatigue behaviour of rotary friction-welded solid shafts made of structural steel S355J2G3 (1.0570) and an approach to fatigue assessment considering residual stress. In contrast to fusion-welded joints, components made by rotary friction welding usually contain compressive residual stress near the weld, which can significantly affect the fatigue strength. For this purpose, specimens were welded and characterised, including metallographic micrographs, hardness measurements, and residual stress measurements. The fatigue tests were performed with a constant amplitude loading in tension/compression or torsion with R = -1. All specimens were investigated without machining of the weld flash, either in the as-welded state or after a post-weld stress-relief heat treatment. In addition, the friction welding process and the residual stress formation were analysed using numerical simulation. The characterisation results are integrated into a fatigue assessment approach. Overall, the specimens perform comparatively well in the fatigue tests and the experimentally observed fatigue behaviour is well described using the proposed local approaches.

Spin polarization and band alignments in the KCaN[formula omitted]/KCl(001) interfaces: First-principles calculations

The electronic and magnetic properties of KCaN2/KCl(001) heterojunction have been studied within the framework of density functional theory using the full-potential linear augmented plane waves plus local orbital approach. There are two possible interface layers depending on the crystal structure of KCaN2. The results demonstrate that the half-metallicity nature remains in both possible interfaces, i.e., KCl/KCa and KCl/NN. Spin polarization in both interfaces is complete, %100, and the interface effects do not affect half-metallicity. The Schottky barrier height, band offsets, valence band maximum, and conduction band minimum to these interfaces were calculated and compared with the other half-metal/semiconductor heterostructures.

Combining biotelemetry and genetics provides complementary insights relevant to the management and conservation of a freshwater predator (Esox lucius) living in brackish lagoons

To inform the management of wild fish populations, it is equally important to understand both the ecological connectivity of habitat patches, apparent at annual and seasonal scales, and the genetic connectivity, emerging at evolutionary scales across generations. Ecological connectivity indicates the potential for rapid recolonization upon local depletion, while genetic connectivity informs about the conservation needs related to the evolution of subpopulations and ecotypes in metapopulations. We combined acoustic biotelemetry and pooled-genome sequencing to study a northern pike (Esox lucius) population as a model of a freshwater piscivore that inhabits a network of shallow brackish lagoons in the southern Baltic Sea. We found limited ecological connectivity among genetically similar subpopulations of pike, suggesting a metapopulation structure characterized by discrete local subpopulations with infrequent migrations between them. Connectivity of different lagoons increased during spawning, suggesting directed spawning migrations to either freshwater rivers or low salinity patches in connected lake-like bays. Spawning site fidelity to either brackish or freshwater spawning sites was observed, further contributing to the reproductive isolation of certain subpopulations. The genetic population structure aligned with salinity gradients and geographical distance and was significant between pairs of rivers draining into the lagoon network, but it was unrelated to ecological connectivity. The results collectively suggest that local subpopulations may not rapidly replenish upon local depletion and that even weak connectivity among subpopulations was sufficient to maintain genetic homogeneity across lagoons with similar salinity levels. Effective management and conservation of species forming metapopulations, such as the coastal northern pike studied here, necessitate localized approaches that adapt fishing mortality to local abundance and promote access to specific habitats, especiallyrivers, during spawning to conserve the entire genetic biodiversity and foster resilience of the metapopulation.

A local approach to stability groups

In this short note we prove a local version of Philip Hall’s theorem on the nilpotency of the stability group of a chain of subgroups by only using elementary commutator calculus (Hall’s theorem is a direct consequence of our result). This provides a new way of dealing with stability groups.

A matheuristic-oriented iterated greedy algorithm for multi-mode resource-constrained project scheduling problem under uncertainty

Multi-mode resource-constrained project scheduling problem is one of the most important branches of combinatorial optimization problems. The aim is to find a feasible solution consisting of activity start time and execution mode so that the project makespan is minimized. However, there are a lot of uncertain events, that bring great challenges to project scheduling. We first propose a mixed integer linear programming model where the activity duration is uncertain. Then two uncertain parameters are introduced to describe the disturbance degree of uncertain activity duration and the allowable violation degree of constraints respectively, and the MILP model with uncertain activity duration is converted to its deterministic robust counterpart model. A matheuristic local optimization approach is proposed to balance computational time with the optimization of the computational result. A matheuristic-oriented iterated greedy algorithm that combines a matheuristic local optimization approach and an iterated greedy algorithm is proposed to solve this problem in this paper. Experimental results indicate that the robust counterpart model can obtain robust optimal solutions for small-scale instances in accepted computational time. Meanwhile, the proposed matheuristic-oriented iterated greedy algorithm can obtain robust near-optimal solutions for large-scale instances and is superior to other compared algorithms.

Wasserstein local slow feature analysis and its application to process monitoring

Abstract Complex industrial processes are commonly characterized by dynamics, which results from the fact that there exists compensation of the closed-loop control and complex reflux during process operations. In this work, we propose a new method termed Wasserstein local slow feature analysis approach (WLSFA) used for monitoring the dynamic process, which can learn slowly varying information to capture the trend of process variations and characterize dynamics. Specifically, Wasserstein graph embedding based on optimal transport is proposed to preserve the local geometrical structure of raw data so that the reduced-dimensional representations can more faithfully reveal the underlying information of data, enhancing monitoring performance. Moreover, the l_2 -norm orthogonal constraint is incorporated into the objective function to improve generalization ability and alleviate the false alarm rate. On the basis of WLSFA model, a contribution plot in the sense of reconstruction is developed to locate the fault variables, which is beneficial to regulate abnormal conditions towards normal levels. Finally, the efficiency of the proposed approach is illustrated by a benchmark process and the application to a real-world fractionation process.

LG-GNN: Local and Global Information-aware Graph Neural Network for default detection

Default detection, a crucial aspect of individual credit scoring, has attracted considerable attention in research. Previous approaches typically focus on classifying applicants using only explicit attributes, overlooking the importance of latent relations among them. Concurrently, graph-based techniques have emerged as promising tools for credit scoring. However, existing graph-based methods often need to be more accurate in aggregating information from limited neighbors, which can lead to misclassification when the target node has differently labeled neighbors. Motivated by these challenges, we propose a Local and Global Information-aware Graph Neural Network (LG-GNN) approach for default detection. By leveraging the loan applicant relation graph, LG-GNN dynamically learns the representation of the target node from both local and global perspectives. Furthermore, it adaptively fuses the information from these perspectives and employs contrastive learning to enhance feature variations. Extensive experiments demonstrate the superiority of LG-GNN over mainstream methods across several widely used default detection datasets. Specifically, LG-GNN achieves an average relative performance improvement of 47.9% compared to baselines. Moreover, compared to the most competitive default detection methods, LG-GNN exhibits an average performance improvement of 11.9%. Our code is publicly available at https://github.com/BERA-wx/LG-GNN.

Optimizing the preparation of multi-colored dye-tracer proppants: A potential approach for quantitative localization and volume assessment of proppant flowback in multistage fractured horizontal wells

Proppant flowback is a common phenomenon after hydraulic fracturing of oil and gas wells. Especially in the large-scale sanding mode of horizontal wells, sand production after fracturing is usually serious. Proppant flowback can not only clog wellbore and wear out equipment, add additional hauling and disposal costs, but also reduce conductivity of the propped fractures, severely affecting oil and gas production. However, there is still a lack of effective means for locating sand production in horizontal wells. Traditional tracer proppants mainly use radioactive elements to monitor the shape of fractures, but they cannot tell the location and amount of sand production. In this work, dye tracer proppant of eight different colors was prepared by dyeing quartz sand proppant. The results show that all the eight kinds of dyes can dye the quartz sand proppant effectively and evenly. The optimal ratio of proppant mass to MB solution volume was determined to be 1:0.015, and the ratio of proppant mass to fixing agent volume was 1:0.0125. The dyed tracer proppant showed good dyeing stability at pH values of 6–13 and surfactant concentrations of 0–200 mg/L. The salinity and fracturing fluid did not affect its stability, and temperature had a negative effect on the stain stability. However, even in the most severe case of decolorization in the experiment, that is, after the dyed proppant was soaked for 24 h at a high temperature of 120 °C, the color of the dried dyed proppant was still clearly distinguished from that of ordinary quartz sand. In theory, using these dyed proppants can potentially tell quantitively the location and volume of sand production during horizontal large-scale fracturing flowback. Since this method has not been used in hydraulic fracturing, it only provides a new way to monitor the sand production of horizontal wells after fracturing. It can provide valuable data for adjusting subsequent fracturing process and has important implications for improving efficiency of fracturing operation of unconventional oil and gas reservoirs.

Local Management for Diabetic Foot Ulcers: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials.

This study evaluated the efficacy of various local management strategies for diabetic foot ulcers (DFUs). Several surgical and non-surgical local interventional approaches are available for the treatment of DFUs. The comparative effectiveness of different treatments is unknown, and it remains unclear which approach is the optimal choice for DFUs treatment due to limited direct comparisons. We did a systematic review and meta-analysis to select the optimal approach to DFUs local management. We searched Medline, Embase, Web of Science, and ClinicalTrials.gov from inception to September 1, 2023, to identify relevant randomized controlled trials (RCTs). We analysed data by pairwise meta-analyses with a random-effects model. A network meta-analysis using the surface under the cumulative ranking curve (SUCRA) was performed to evaluate the comparative efficacy of different interventional approaches in the early (within 12wk) and late stages (over 12wk). 141 RCTs involving 14076 patients and exploring 14 interventional strategies were eligible for inclusion. Most studies (102/141) had at least one risk-of-bias dimension. Good consistency was observed during the analysis. Local pairwise comparisons demonstrated obvious differences in the early-stage healing rate and early- and late-stage healing times, while no significant difference in the late-stage healing rate or adverse events were noted. SUCRAs identified the standard of care (SOC) + decellularized dressing (DD), off-loading (OL), and autogenous graft (AG) as the three most effective interventions within 12 weeks for both healing rate (97%, mean rank: 1.4; 90%, mean rank: 2.3; 80.8%, mean rank: 3.5, respectively) and healing time (96.7%, mean rank: 1.4; 83.0%, mean rank: 3.0; 76.8%, mean rank: 3.8, respectively). After 12 weeks, local drug therapy (LDT) (89.5%, mean rank: 2.4) and OL (82.4%, mean rank: 3.3) ranked the highest for healing rate, and OL (100.0%, mean rank: 1.0) for healing time. With respect to adverse events, moderate and high risks were detected in the SOC + DD (53.7%, mean rank: 7.0) and OL (24.4%, mean rank: 10.8) groups, respectively. The findings suggest that OL provided considerable benefits for DFU healing in both the early and late stages, but the high risk of adverse events warrants caution. SOC+DD may be the preferred option in the early stages, with an acceptable risk of adverse events.

Spatial downscaling of rainfall from general circulation models: A chaos theory-based framework

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region.

An empirical study of fault localization in Python programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java—the most common choices for fault localization research—carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first multi-family large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.’s recent large-scale empirical study of fault localization in Java (Zou et al. 2021) as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection (Widyasari et al. 2020). The results replicate for Python several results known about Java, and shed light on whether Python’s peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.