Development Of System Research Articles

Light-Adapted Optoelectronic-Memristive Device for the Artificial Visual System.

With the development of artificial intelligence systems, it is necessary to develop optoelectronic devices with photoresponse and storage capacity to simulate human visual perception systems. The key to an artificial visual perception system is to integrate components with both sensing and storage capabilities of illumination information. Although module integration components have made useful progress, they still face challenges such as multispectral response and high energy consumption. Here, we developed a light-adapted optoelectronic-memristive device integrated by an organic photodetector and ferroelectric-based memristor to simulate human visual perception. ITO/P3HT:PC71BM/Au as the light sensor unit shows a high on/off ratio (Iph/Id) reaching ∼5 × 104 at 0 V. The memristor unit, consisting of ITO/CBI@P(VDF-TrFE)/Cu, has a RON/ROFF ratio window of ∼106 under 0.05 V read voltage and ultralow power consumption of ∼1 pW. Moreover, the artificial visual perception unit shows stable light-adapted memory windows under different wavelengths of irradiation light (400, 500, and 600 nm; they meet the spectral range of human visual recognition) and can clearly identify the target image ("T" shape) because of the apparent contrast, which results from the high ROFF/RON ratio values. These results provide a potential design strategy for the development of intelligent artificial vision systems.

Increased Prevalence of Binge Eating Disorder and Bulimia Nervosa in Women With Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis.

Polycystic ovary syndrome (PCOS) is associated with disordered eating/eating disorders, but prior meta-analyses are limited by small numbers. To inform the 2023 International PCOS Guideline, we performed a systematic review and meta-analysis evaluating the prevalence of disordered eating/eating disorders among women with and without PCOS. Ovid MEDLINE, EMBASE, PsycInfo, and All EMB were searched from inception through February 1, 2024, for studies that compared prevalences of eating disordered/disordered eating in adolescent or adult women. Random effects meta-analyses were used to estimate the pooled odds ratios (OR) or standardized mean differences (SMD) of outcomes in women with PCOS compared to controls. Methodological quality was assessed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system, and included studies were assessed for risk of bias. Of 1352 articles identified, 20 were included, with a total of 28 922 women with PCOS and 258 619 controls. Individuals with PCOS had higher odds of any eating disorder (OR: 1.53 [1.29, 1.82], 8 studies), which persisted in studies where PCOS was diagnosed by Rotterdam criteria (OR: 2.88 [1.55, 5.34], 4 studies). Odds of bulimia nervosa, binge eating disorder, and disordered eating, but not anorexia nervosa, were increased in PCOS. Mean disordered eating scores were higher in PCOS (SMD: 0.52 [0.28, 0.77], 13 studies), including when stratified by normal and higher weight body mass index. Most included studies were of moderate quality, with no evidence of publication bias. Our study informs the 2023 PCOS Guideline recommendations for consideration of the risk of disordered eating/ eating disorders in care of women with PCOS, regardless of weight, especially during providing lifestyle counseling.

Improving the evidential value of low-quality face images with aggregation of deep neural network embeddings

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal proceedings. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve the performance in forensic comparison of facial images. We observe significant performance improvements, especially for low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of common-source likelihood ratio systems and report improvements in Cllr both for CCTV images and for social media images.

Microporous tungsten oxide spheres coupled with Ti3C2Txnanosheets for high-volumetric capacitance supercapacitors.

In the contemporary landscape of technological advancements, the burgeoning demand for portable electronics and flexible wearable devices has necessitated the development of energy storage systems with superior volumetric performance. Tungsten oxide (WO3), known for its high density and theoretical capacitance, is a promising electrode material for supercapacitors. However, low conductivity and poor cycling stability are still the key bottlenecks for its application. Herein, a novel composite comprising hollow porous WO3 spheres (HPWS) derived by template method was electrostatic self-assembled on the surface of the Ti3C2Tx nanosheets. The resulting electrodes exhibited ultra-high volumetric capacitance of 1930 F cm-3 at 1 A g-1 and rate capability of 46% at 50 A g-1, attributed to enhanced ion accessibility from microporous structure and electron transport from conductive network of Ti3C2Tx even at a high packing density of 3.86 g cm-3. Utilizing HPWS/Ti3C2Tx as the negative electrode and porous carbon as the positive electrode, the assembled asymmetric supercapacitor achieved an energy density of 31 Wh kg-1 at a power density of 650 W kg-1 with over 107% capacitance retention after 5000 cycles. This work provides a promising approach for developing next-generation supercapacitors with ultra-high volumetric capacitance.

A Gait Imagery-Based Brain-Computer Interface with Visual feedback for Spinal Cord Injury Rehabilitation on Lokomat.

Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery-based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms. As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to μ (8-12 Hz) and β (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms. The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI. The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz. This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.

Development of a Web-Based Decision Support Nurse Care Management System: Decision Support-N-Care.

Development of a Web-Based Decision Support Nurse Care Management System: Decision Support-N-Care.

Deep reinforcement learning-based attitude control for spacecraft using control moment gyros

This paper addresses the development of an attitude control system that steers control moment gyros (CMGs) based on deep reinforcement learning (DRL) for agile spacecraft. The proposed DRL-based attitude control system learns CMG steering strategies to achieve the desired attitude, thus potentially bypassing the singularity issues inherent in the CMG cluster. In particular, it is designed in two-phases to apply the DRL technique efficiently. In the first phase, the attitude control is performed based on DRL up to a certain tolerance, after which it switches to conventional control and steering law for stabilization in the second phase. The rapid pointing capability of the proposed DRL-based attitude control system is demonstrated for an agile spacecraft equipped with pyramid-type single gimbal control moment gyros. Additionally, in realistic scenarios of pointing multiple targets on the ground, the momentum vector recovery that the CMG system needs to consider is also briefly discussed.

Draft genome of akame (Lates japonicus) reveals possible genetic mechanisms for long-term persistence and adaptive evolution with low genetic diversity.

It is known that some endangered species have persisted for thousands of years despite their very small effective population sizes and low levels of genetic polymorphisms. To understand the genetic mechanisms of long-term persistence in threatened species, we determined the whole genome sequences of akame (Lates japonicus), which has survived for a long time with extremely low genetic variations. Genome-wide heterozygosity in akame was estimated to be 3.3-3.4 × 10-4/bp, one of the smallest values in teleost fishes. Analysis of demographic history revealed that the effective population size in akame was around 1,000 from 30,000 years ago to the recent past. The relatively high ratio of nonsynonymous to synonymous heterozygosity in akame indicated an increased genetic load. However, a detailed analysis of genetic diversity in the akame genome revealed that multiple genomic regions, including genes involved in immunity, synaptic development, and olfactory sensory systems, have retained relatively high nucleotide polymorphisms. This implies that the akame genome has preserved the functional genetic variations by balancing selection, to avoid a reduction in viability and loss of adaptive potential. Analysis of synonymous and nonsynonymous nucleotide substitution rates has detected signs of positive selection in many akame genes, suggesting adaptive evolution to temperate waters after the speciation of akame and its close relative, barramundi (L. calcarifer). Our results indicate that the functional genetic diversity likely contributed to the long-term persistence of this species by avoiding the harmful effects of the population size reduction.

H3K36 methyltransferase SMYD2 affects cell proliferation and migration in Hirschsprung's disease by regulating METTL3.

The pathogenesis of Hirschsprung's disease (HSCR) is complex. Recently, it has been found that histone modifications can alter genetic susceptibility and play important roles in the proliferation, differentiation and migration of neural crest cells. H3K36 methylation plays a significant role in gene transcriptional activation and expression, but its pathogenic mechanism in HSCR has not yet been studied. This study aimed to elucidate its role and molecular mechanism in HSCR. Western blot analysis, immunohistochemistry (IHC) and reverse transcription-quantitative PCR (RT‒qPCR) were used to investigate H3K36 methylation and methyltransferase levels in dilated and stenotic colon tissue sections from children with. We confirm that SMYD2 is the primary cause of differential H3K36 methylation and influences cell proliferation and migration in HSCR. Subsequently, quantitative detection of m6A RNA methylation revealed that SMYD2 can alter m6A methylation levels. Western blot analysis, RT-qPCR, co-immunoprecipitation (co-IP), and immunofluorescence colocalization were utilized to confirm that SMYD2 can regulate METTL3 expression and affect m6A methylation, affecting cell proliferation and migration. These results confirm that the H3K36 methyltransferase SMYD2 can affect cell proliferation and migration in Hirschsprung's disease by regulating METTL3. Our study suggested that H3K36 methylation plays an important role in HSCR, confirming that the methyltransferase SMYD2 can affect m6A methylation levels and intestinal nervous system development by regulating METTL3 expression.

Identification of differentially expressed tumour-related genes regulated by UHRF1-driven DNA methylation

Ubiquitin-like with PHD and RING finger domains 1 (UHRF1) is an epigenetic regulator that plays critical roles in tumours. However, the DNA methylation alteration patterns driven by UHRF1 and the related differentially expressed tumour-related genes remain unclear. In this study, a UHRF1-shRNA MCF-7 cell line was constructed, and whole-genome bisulfite sequencing and RNA sequencing were performed. The DNA methylation alteration landscape was elucidated, and DNA methylation-altered regions (DMRs) were found to be distributed in both gene bodies and adjacent regions. The DMRs were annotated and categorized into 488 hypermethylated/1696 hypomethylated promoters and 1149 hypermethylated/5501 hypomethylated gene bodies. Through an integrated analysis with the RNA sequencing data, 217 methylation-regulated upregulated genes and 288 downregulated genes were identified, and these genes were primarily enriched in nervous system development and cancer signalling pathways. Further analysis revealed 21 downregulated oncogenes and 15 upregulated TSGs. We also showed that UHRF1 silencing inhibited cell proliferation and migration and suppressed tumour growth in vivo. Our study suggested that UHRF1 and the oncogenes or TSGs it regulates might serve as biomarkers and targets for breast cancer treatment.

The Influence of Human Resource Information System Career Planning and Development on Employee Performance: A case of the Institute of Accountancy Arusha

This study sought to examine the influence of human resource information system career planning and development on employee performance: A case of the Institute of Accountancy Arusha. Descriptive survey research design and quantitative research approach were employed. Primary data were gathered through structured questionnaires and secondary data were obtained from various sources including published sources and unpublished sources such as books, research reports, journal articles, dissertations, seminars, magazines, and newspapers regarding the influence of HRIS utilization on employee performance in the Institute of Accountancy Arusha. Descriptive statistics was used to analyze data. Findings revealed that human resource information system career planning and development influenced employee performance at IAA through improving working morale, increasing productivity, performing well the work duties, increasing creativity, ability to make a productive decision in employees’ work. It was recommended that human resource information system human resource information system should be strengthened and improved as to continually influence employee’s performance at the Institute of Accountancy Arusha. This can be done by adopting up-to-date (more sophisticated) human resource information systems.

Design and Development of a Microcontroller-based Weight Measurement System for Liquid Agriculture Produce

This research presents the development of a microcontroller-based weight measurement system tailored for liquid agricultural products. The system addresses the limitations of traditional manual weighing methods by incorporating a load cell, ADC, microcontroller, LCD, and Bluetooth module. The load cell converts weight into an electrical signal, processed by the microcontroller and displayed on the LCD. Calibration ensures accurate weight measurements. The system's portability, cost-effectiveness, and user-friendly interface make it a promising solution for agricultural applications. Quantitative aspects include the precise weight measurement enabled by the load cell and ADC, with calibration enhancing accuracy. Qualitative aspects encompass the system's ease of use, portability, and potential for integration into broader agricultural management systems. Future enhancements include additional sensors for comprehensive product analysis, cellular communication for remote data transmission, and data logging capabilities. This research contributes to the automation and efficiency of liquid weight measurement in agriculture, potentially improving resource utilization and crop yields.

Efficiency Measurements of Energy Harvesting from Electromagnetic Environment for Selected General Purpose Telecommunication Systems

The results of measurements of the efficiency of energy harvesting from commonly available general-purpose telecommunications systems, divided into typical bands available under European conditions, have been presented in this paper. Specially designed harvesters were used, dedicated to powering autonomous semi-passive Radio Frequency Identification (RFID) tags. For the assumed resistive loads, the achievable output voltage values of the harvesters were measured across a wide spectrum of electromagnetic field strengths, simulating real conditions. The performance and dynamics of the energy storage process with fixed parameters were studied at an intermediate stage, before the energy conditioning process. The harvesters were treated as typical energy sources with unknown but variable parameters, so their dynamic parameters and instantaneous energy supply were also analyzed. These activities will enable the final development of a power supply system with parameters acceptable for the planned applications and whose efficiency will be maximized under the given conditions. For this purpose, the energy harvesting systems were designed, a suitable laboratory stand was built, and the elaborated circuits were measured to determine the expected parameters of energy harvesting.

Justifying and Implementing Concept of Object-Oriented Observers of Thermal State of Rolling Mill Motors

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper’s contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Harnessing multimodal data fusion to advance accurate identification of fish feeding intensity

Accurately identifying the fish feeding intensity plays a vital role in aquaculture. While traditional methods are limited by single modality (e.g., water quality, vision, audio), they often lack comprehensive representation, leading to low identification accuracy. In contrast, the multimodal fusion methods leverage the fusion of features from different modalities to obtain richer target features, thereby significantly enhancing the performance of fish feeding intensity assessment (FFIA). In this work a multimodal dataset called MRS-FFIA was introduced. The MRS-FFIA dataset consists of 7611 labelled audio, video and acoustic dataset, and divided the dataset into four different feeding intensity (strong, medium, weak, and none). To address the limitations of single modality methods, a Multimodal Fusion of Fish Feeding Intensity fusion (MFFFI) model was proposed. The MFFFI model is first extracting deep features from three modal data audio (Mel), video (RGB), Acoustic (SI). Then, image stitching techniques are employed to fuse these extracted features. Finally, the fused features are passed through a classifier to obtain the results. The test results show that the accuracy of the fused multimodal information is 99.26%, which improves the accuracy by 12.80%, 13.77%, and 2.86%, respectively, compared to the best results for single-modality (audio, video and acoustic dataset). This result demonstrates that the method proposed in this paper is better at classifying the feeding intensity of fish and can achieve higher accuracy. In addition, compared with the mainstream single-modality approach, the model improves 1.5%–10.8% in accuracy, and the lightweight effect is more obvious. Based on the multimodal fusion method, the feeding decision can be optimised effectively, which provides technical support for the development of intelligent feeding systems.

Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm

Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm rev−1) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 °C) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm rev−1 and 0.25 mm, while the responses for these optimal conditions are 69.92 μm, 39.48 °C and 0.137 Kg-CO2 in that order.

Design of Cr-Based Molecular Electrocatalyst Systems for the CO2 Reduction Reaction.

ConspectusHuman influence on the climate system was recently summarized by the sixth Intergovernmental Panel on Climate Change (IPCC) Assessment Report, which noted that global surface temperatures have increased more rapidly in the last 50 years than in any other 50-year period in the last 2000 years. Elevated global surface temperatures have had detrimental impacts, including more frequent and intense extreme weather patterns like flooding, wildfires, and droughts. In order to limit greenhouse gas emissions, various climate change policies, like emissions trading schemes and carbon taxes, have been implemented in many countries. The most prevalent anthropogenic greenhouse gas emitted is carbon dioxide (CO2), which accounted for 80% of all U.S. greenhouse gas emissions in 2022. The reduction of CO2 through the use of homogeneous electrocatalysts generally follows a two-electron/two-proton pathway to produce either carbon monoxide (CO) with water (H2O) as a coproduct or formic acid (HCOOH). These reduced carbon species are relevant to industrial applications: the Fischer-Tropsch process uses CO and H2 to produce fuels and commodity chemicals, while HCOOH is an energy dense carrier for fuel cells and useful synthetic reagent. Electrochemically reducing CO2 to value-added products is a potential way to address its steadily increasing atmospheric concentrations while supplanting the use of nonrenewable petrochemical reserves through the generation of new carbon-based resources. The selective electrochemical reduction of CO2 (CO2RR) by homogeneous catalyst systems was initially achieved with late (and sometimes costly) transition metal active sites, leading the field to conclude that transition metal complexes based on metals earlier in the periodic table, like chromium (Cr), were nonprivileged for the CO2RR. However, metals early in the table have sufficient reducing power to mediate the CO2RR and therefore could be selective in the correct coordination environment. This Account describes our efforts to develop and optimize novel Cr-based CO2RR catalyst systems through redox-active ligand modification strategies and the use of redox mediators (RMs). RMs are redox-active molecules which can participate cocatalytically during an electrochemical reaction, transferring electrons─often accompanied by protons─to a catalytic active site. Through mechanistic and computational work, we have found that ligand-based redox activity is key to controlling the intrinsic selectivity of these Cr compounds for CO2 activation. Ligand-based redox activity is also essential for developing cocatalytic systems, since it enables through-space interactions with reduced RMs containing redox-active planar aromatic groups, allowing charge transfer to occur within the catalyst assembly. Following a summary of our work, we offer a perspective on the possibilities for future development of catalytic and cocatalytic systems with early transition metals for small molecule activation.

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to detect impaired executive function during visuospatial memory encoding in patients with mental illnesses. In 96 patients with first-episode psychosis, 49 with clinical high risk for psychosis, 104 with OCD, and 159 healthy controls, eye movements were recorded during a 3-min RCFT figure memorization task, and organization and immediate recall scores were obtained. These scores, along with the fixation points indicating eye-focused locations in the figure, were used to train a Long Short-Term Memory + Attention model for detecting impaired executive function and visuospatial memory. The model distinguished between normal and impaired executive function, with an F1 score of 83.5%, and identified visuospatial memory deficits, with an F1 score of 80.7%, regardless of psychiatric diagnosis. These findings suggest that this eye tracking-based deep learning model can directly and rapidly identify impaired executive function during visuospatial memory encoding, with potential applications in various psychiatric and neurological disorders.

Piloting a co-created local and alternative food network involving professional buyers in Norway: Forces and tensions influencing viability

We can obtain knowledge about how sustainability transitions can take place through experimenting with niche-activities. An alternative food network (AFN) called Green Parallel was co-created through action research in an agri-food living lab and was piloted in 2019 and 2020. The purpose was to contribute to more organic produce in localized food systems and improve communication between producers and professional buyers (e.g. specialty stores, restaurants, chain retails, private and public canteens) in the Vestfold region in Norway. This paper analyses the occurring forces and tensions that support or hinder the viability of the AFN.The methods of data collection were interviews, reflection notes and participants’ notes from the participatory processes. We utilized perspectives from field theory and institutional economy to understand individual behaviors in relation to internal and external forces and tensions in a multi-level perspective (MLP).A strong supportive force was the initial motivation participants in Green Parallel had to collaborate. In addition, we identified seven forces and tensions affecting the viability of Green Parallel. These forces and tensions worked through complex interdependencies within individuals and actor types and across actor types, as well as across internal and external niche spans. The study can inform further development of organic and local agri-food systems.