Local Approach Research Articles

Performance Potential for Locally Contacted Perovskite Solar Cells

In perovskite solar cells (PSCs), a common characteristic of highly effective interface passivation materials is low conductivity. Gains in voltage are thus often disproportionately offset by resistive losses. Local contact approaches can minimize this trade‐off and have a proven track record in conventional silicon photovoltaics. Indeed, recent record efficiencies for centimeter‐scale PSCs exploit architectures where the passivation layer partially covers the perovskite‐transport layer interface. Herein, a three‐dimensional numerical device model is used to determine practical performance limits to local contact geometries and consider both the optimum contact dimensions and the trade‐offs involved in relaxing these dimensions for ease of fabrication. It is observed that the potential for substantial power conversion efficiency (PCE) increases with local contacts. In devices where power loss occurs solely through recombination at the contacted interface, PCE can be enhanced by up to 10% absolute compared to a full‐area contact. However, optimum PCEs depend on contacts on the order of nanometers. It is shown that more fabrication‐friendly micrometer‐scale contacts still boost PCE, but the absolute enhancement falls short due to the relatively low bulk perovskite charge carrier diffusion length. This may ultimately motivate methods of interface engineering that produce “effective” local contact geometries at nanometer scales, such as via self‐forming layers.

Analysis and performance evaluation of computation models for node localization in deep sea using UWSN

SummaryUnderwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance.

Wellbeing, support and intention to leave: a survey of nurses, midwives and healthcare support workers in Wales

Background/Aims Low morale and burnout is a widely acknowledged problem among healthcare professionals, with implications for staffing levels and quality of care. This study aimed to provide insight into the wellbeing of nurses, midwives and healthcare support workers, and propose strategies to support a resilient workforce and organisational development. Methods An online survey was conducted with 462 nursing and midwifery staff at one NHS health board in Wales. Validated scales were used to measure key indicators, such as workload, bullying and burnout. Descriptive analysis was undertaken of the quantitative data and content analysis of the qualitative data. Results The survey found high levels of work intensification and burnout, with considerable proportions of respondents reporting intentions to leave their job and profession. Low levels of organisational support and trust in senior management were reported, which qualitative analysis indicated was contributing to intention to leave. Conclusions Healthcare leaders need to place more importance on the wellbeing of staff and consider the impact of workplace climate. An inclusive, localised approach to challenges in the working climate is recommended to effectively promote a resilient and sustainable workforce.

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Addressing unaccounted-for-gas (UFG): Proactive techniques for optimal management and control

This study addresses the critical issue of Unaccounted-for-Gas (UFG) in Pakistan's gas sector, a significant challenge undermining operational efficiency and sustainability. With a comprehensive approach, the research introduces a tailored UFG Control strategy focused on "Network Visibility," "Network Rehabilitation," "Theft Control," and "Research & Development." These components aim to enhance the accuracy of gas metering and distribution, reduce losses through advanced technological interventions, and mitigate theft via robust monitoring mechanisms. By analyzing the period from 2013 to 2019, findings reveal the effectiveness of the proposed strategy in significantly improving operational efficiencies across the sector. The study also identifies key indicators for each strategy component, facilitating ongoing performance evaluation and strategic adjustments. This localized approach to UFG management not only offers a pathway to reduce gas losses but also underscores the importance of integrating modern technologies with existing infrastructure to address the sector's unique challenges. The research contributes valuable insights into the adaptation of international methodologies for the Pakistani context, highlighting the potential for regional customization in addressing UFG issues. This study endeavors to furnish policymakers and industry stakeholders with practical strategies aimed at improving the management and sustainability of Pakistan's natural gas sector.

An Efficient Estimator for Source Localization in WSNs using RSSD and TDOA Measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Innovation in Preschool and Early School Education

The importance of innovation in early childhood learning emphasizes the need for a responsive approach to local cultural riches and values. The local wisdom approach emerges as a promising paradigm, creating space for innovation that has a positive impact on learning management. The aim of this research is to describe, analyze and offer learning innovation solutions through a local wisdom approach in a management context. The research method used is literature study, through collecting library data originating from articles, journals, electronic books and relevant document data related to the research focus. The data analysis process was carried out using descriptive methods to analyze the material collected. The research results show that innovative learning with local wisdom creates an interesting learning environment and supports children's development by enriching their experience of local values ​​and traditions. Innovative activities include learning methods, creative management, material development, and local community empowerment.

LITERASI SAINS PADA BAHAN AJAR ILMU PENGETAHUAN ALAM DAN SOSIAL (IPAS) SEKOLAH DASAR

Low scientific literacy is caused by learning activities that emphasize the aspect of remembering material and pay less attention on the processes and practices in real life, as well as learning that pays less attention on discovery. This research aims is to analyze and describe the content of science teaching materials based on indicators of scientific literacy ability. This research applied a qualitative approach with a content analysis design. The subjects of this research were teachers at SD Negeri Polokarto 03. Data collection techniques using triangulation of sources and techniques. Data collection tools are observation, interviews and documentation. The results of the content analysis show that overall of the teaching materials are in the fairly good category for containing scientific literacy; The content of teaching materials can also be integrated with natural potential in everyday life; and teaching materials that include elements of motivation and communication as well as local approaches are able to increase scientific literacy competence and science subjects.

Review of soil form and wetness indicators for wetland delineation in South Africa

Wetland delineation in South Africa incorporates soil form and soil wetness indicators, requiring formal soil classification and description of soil redox morphology. The current wetland definition used administratively in South Africa focuses on saturated (hydric) soil signatures within plant root zones. Saturated soil horizons deeper than plant root zones fall outside the 50 cm criterion in the local approach as well as the accepted zone in USA literature. The field of hydropedology accommodates the classification of the various hydrologically active horizons and provides a tool for the handling of horizons with ephemeral wetness. This approach has been variably accepted by mandated authorities in South Africa. The South African soil classification system has evolved through three editions over the past 50 years while retaining the same redox morphology understanding. However, despite the concepts and context of redox morphology having been thoroughly technically adopted by soil scientists, this is not the case within the wetland research and management environment. This especially because the classification system is structured differently from other international systems, and the South African landscape is geologically ancient with mature soils, introducing challenges to resource assessment specialists who rely on international norms and approaches for wetland assessment. This paper reviews the various components of soil classification and redox morphology based on Fe and Mn minerals within the context of the South African soil classification system, the field of hydropedology and wetland delineation indicators. We provide a qualitative correlation between the various diagnostic horizons and materials in the system and their related redox morphology contexts that are relevant to wetland assessment, delineation, and protection in South Africa. This paper therefore aims to serve as a reference point for the description and correlation of various soil hydrological parameters used in formal assessments.

A frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

Contribution of CT scan to patient's radiation exposure in parathyroid SPECT/CT scintigraphy

IntroductionDual phase technetium-99mTc-methoxy isobutyl isonitrile (MIBI) single-photon emission computed tomography with computed tomography (SPECT/CT) may be the most accurate conventional imaging approach for localization of enlarged parathyroid gland (EPG). The imaging is based on the radiopharmaceutical (RP) retention in EPG compared to washout from normal thyroid and normal parathyroid glands. This study aimed to estimate and optimize the contribution of computed tomography (CT) scan and scan range to effective dose (ED) in dual-phase MIBI SPECT/CT parathyroid scintigraphy. MethodsThe study included seventy-four patients; thirty-seven with reduced and thirty-seven with extended CT scan range. The ED caused by the CT scan was calculated using Dose Length Product (DLP) data and estimated using the Imaging Performance Assessment of CT scanners (ImPACT) calculator. ResultsFor all patients, the contribution of CT to the ED in a combined SPECT/CT examination was 2.62 ± 0.29 mSv (48%). The contribution of CT to the total ED was 1.8 ± 0.18 mSv (33%) when using reduced and 3.44 ± 0.23 mSv (64%) when using extended scan range. The DLP and ED were statistically significantly different between the reduced and extended CT scan range (p < 0.001) in the first and second phases. The individual organ dose was reduced from 8% to 94%. ConclusionThe hybrid SPECT/CT improves the interpretation of nuclear medicine images and also increases the radiation dose to the patient. An adequately defined CT scan range on SPECT/CT imaging, can significantly reduce a patient's ED. Implications for practiceThe research findings showed that knowledge of anatomy, pathology and technology can provide optimising diagnostic procedures and reduce patient ED after SPECT/CT scans.

Kearifan Lokal Dan Pendekatan Dalam Pemanfaatan Sumber Daya Alam Untuk Ekonomi Desa Sesaot

Local wisdom is a cultural heritage that has been acquired and passed down from generation to generation in a community or society. Local wisdom includes knowledge, values, technology, practices and skills possessed by the community in utilizing the natural resources around them. Wise and sustainable use of natural resources is important in the context of village economic development, especially in Indonesia which has abundant natural wealth. Research Method Observer as an instrument (human instrument) This means that the researcher himself has the function of determining the focus of the research, selecting informants as data sources, carrying out data collection, analyzing data, and making conclusions about his findings. The research results show that local wisdom in the use of natural resources for village economic development, especially in Sesaot Village, West Lombok Regency, West Nusa Tenggara Province, Indonesia. Local wisdom is a cultural heritage that includes knowledge, values, technology, practices and skills passed down from generation to generation in a community. Wise and sustainable use of natural resources is important in the context of village economic development in Indonesia which has abundant natural wealth. The local wisdom approach in the use of natural resources includes the knowledge, practices and expertise possessed by local communities. In Sesaot Village, the community has long developed local wisdom in utilizing the natural resources around them, including in managing forests using a customary system that has existed since their ancestors. By optimizing local wisdom, communities are able to utilize natural resources sustainably while preserving nature through environmentally friendly practices. Utilization of natural resources for village economic development provides many benefits, including increasing income, creating jobs, developing industry, strengthening the local economy, and improving the quality of life of village communities. Political ecology approaches, human welfare ecology, and anthropological perspectives are used to understand and apply local wisdom in environmental management and village economic development

Examination of Drone Usage in Estimating Hardwood Plantations Structural Metrics

Planting hardwood trees on retired marginal agricultural land is one of the main strategies used to restore forested wetlands. Evaluating effectiveness of wetland restoration requires efficient monitoring to evaluate recovery trajectories and desired conditions. Recent advancements in unmanned aerial system (UAS) technologies have prompted wide-scale adoption of UAS platforms in providing a range of ecological data. In this study, we examined the use of UAS Structure from Motion (SfM) derived point clouds in estimating tree density, canopy height, and percent canopy cover for bottomland hardwood plantations within four wetland reserve easements. Using a local maxima approach for individual tree detection produced plantation level estimates with mean absolute errors of 150 trees per hectare, 0.5 m, and 18.4% for tree density, canopy height, and percent canopy cover, respectively. At the plot level, UAS-derived tree counts (r = 0.53, p < 0.01) and canopy height (r = 0.57, p < 0.01) were significantly correlated with ground-based estimates. We demonstrate that UAS-SfM is a viable method of assessing bottomland hardwood plantations for applications that require precision levels congruent with the mean absolute errors reported here. The accuracy of tree density estimates was reliant upon specific local maxima window parameters relative to stand conditions. Therefore, acquisition of leaf-off and leaf-on imagery may allow for better individual tree detection and subsequently more accurate tree density and other structural attributes.

An elemental ethics for artificial intelligence: water as resistance within AI’s value chain

AbstractResearch and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques the undermining of local and ancestral approaches to nature and reveals the vital and quotidian harms engendered by so-called intelligent systems. While this ethics is emerging from grassroots and Indigenous groups, it echoes recent calls from environmental philosophy to reconnect with the environment via the elements. In empirical terms, this article looks at groups in Chile resisting a Google data centre project in Santiago and lithium extraction (used for rechargeable batteries) in Lickan Antay Indigenous territory, Atacama Desert. As I show, elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction.

Long Period Rayleigh Wave Focal Spot Imaging Applied to USArray Data

AbstractWe demonstrate the effectiveness of seismic dense array surface wave focal spot imaging using USArray data from the western‐central United States. We study dispersion in the 60–310 s period range and assess the image quality of fundamental mode Rayleigh wave phase velocity maps. We apply isotropic spatial autocorrelation models to the time domain zero lag noise correlation wavefield data at distances of about one wavelength. Local estimates of the phase velocity, its uncertainty, and the regression quality imply overall better ZZ relative to ZR or RZ results. The extension of the depth resolution compared to passive surface wave tomography is demonstrated by the inversion of three clustered dispersion curves from different tectonic units. We observe anisotropic surface wave energy flux and the influence of body wave energy, but sensitivity tests at 60 s targeting the data range, correlation component, and processing choices show that the ZZ focal spots yield consistent high‐quality images compared to regional tomography results in the 60–150 s period range. In contrast, at 200–300 s the comparatively small scales of the imaged structures and the imperfect agreement with low‐resolution global tomography results highlight the persistent challenge to reconcile imaging results based on different data sources, theories, and techniques. Our study shows that surface wave focal spot imaging is an accurate, robust, local imaging approach. Better control over clean autocorrelation fields can further improve applications of this seismic imaging tool for increased resolution of the elastic structure below dense seismic arrays.

‘Innovating’ to promote equity and inclusion in early childhood education: a framework for documenting localised pedagogical approaches

ABSTRACT This paper presents a conceptual framework for understanding ‘innovation’ in early childhood education as a process for addressing issues around social justice and inclusion. The framework is used to analyse innovative aspects of two unique, localised early childhood programmes implemented to support ethnic minority children in Northern Thailand and children of migrant labourers in urban India. Drawing on contemporary critiques that point to the global predominance of universalised models of teaching and learning, the paper highlights these contextually-responsive programmes as crucial in demonstrating the importance of ‘emergent’ pedagogical approaches for achieving equity and inclusion. To promote greater acknowledgement of programmes such as these, the paper presents a case for shifting the current focus on ‘innovation’ in education away from an emphasis on effectiveness for supporting greater efficiency of standardised, pre-determined learning outcomes, to encompass small-scale, localised approaches that support children from marginalised backgrounds.

Selective antibacterial and antibiofilm activity of chlorinated hemicyanine against gram-positive bacteria

Antibiotic-free therapies are highly needed due to the limited success of conventional approaches especially against biofilm related infections. In this direction, antimicrobial phototherapy, either in the form of antimicrobial photothermal therapy (aPTT) or antimicrobial photodynamic therapy (aPDT), have appeared to be highly promising candidates in recent years. These are local and promising approaches for antibiotic resistant bacterial infections and biofilms. Organic small photosensitizers (PSs) are extensively preferred in antimicrobial phototherapy applications as they offer a great opportunity to combine therapeutic action (aPTT, aPDT or both) with fluorescence imaging on a single molecule. In this study, the bactericidal effect of cationic chlorinated hemicyanine (Cl-Hem)-based type I PS, which can function as a dual aPDT/aPTT agent, was investigated on both planktonic cells and biofilms of different gram-positive (E. faecalis and S. epidermidis) and gram-negative bacteria (P. aeruginosa and K. pneumoniae) with and without 640 nm laser irradiation. Cl-Hem was shown to induce a selective phototheranostic activity against gram-positive bacteria (E. faecalis and S. epidermidis). Cl-Hem exhibited both dose and laser irradiation time dependent bactericidal effect on planktonic and biofilms of S. epidermidis. These results clearly showed that highly potent Cl-Hem can treat resistant microbial infections, while allowing fluorescence detection at the same time. High biofilm reduction observed with combined aPDT/aPTT action of Cl-Hem together with its non-cytotoxic nature points out that Cl-Hem is a promising PS for antibacterial and antibiofilm treatments.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

Localization is a primary concern for wireless sensor networks as numerous applications rely on the precise position of nodes. This paper presents a precise deep learning (DL) approach for DV-Hop localization in the Internet of Things (IoT) using the whale optimization algorithm (WOA) to alleviate shortcomings of traditional DV-Hop. Our method leverages a deep neural network (DNN) to estimate distances between undetermined nodes (non-coordinated nodes) and anchor nodes (coordinated nodes) without imposing excessive costs on IoT infrastructure, while DL techniques require extensive training data for accuracy, we address this challenge by introducing a data augmentation strategy (DAS). The proposed algorithm involves creating virtual anchors strategically around real anchors, thereby generating additional training data and significantly enhancing dataset size, improving the efficacy of DNNs. Simulation findings suggest that the proposed deep learning model on DV-Hop localization outperforms other localization methods, particularly regarding positional accuracy.

The Local Regression Approach as a Tool to Improve Place-Based Policies: The Case of Molise (Southern Italy)

The implementation of place-based policies entails the construction of intervention areas (spatially contiguous areas in which the policies are adopted). Many approaches can be adopted for the definition of such areas. This paper reflects on the use of geographically weighted regression (GWR) models as a tool capable of supporting the definition process. The case study concerns Molise, a region in Southern Italy particularly affected by persistent and deep-rooted processes of depopulation. The dependent variable is the average annual rate of population change of municipalities of Molise across the 2011–2019. The independent variables are related to socio-economic profiles of each municipality. The results, contextualised using a broad overview of the Italian case, show that a key variable in the demographic dynamics of the municipalities of Molise is the labour market activity rate of women and that this variable drives a spatial instability that cannot be detected using global approaches and models. This proves the urgent need to expand the use of local thinking for the benefit of both applied demography and society.