Real Complex Research Articles

Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment

The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. Therefore, we propose a novel model based on a hybrid attention mechanism. Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. Secondly, an efficient partial hybrid attention mechanism is established, which realizes the remote dependence and accurate localization of strawberry fruit. Meanwhile, a multi-scale progressive feature pyramid network is constructed, and the fine-grained features of strawberry targets of different sizes are accurately extracted. Finally, a Focaler-shape-IoU loss function is proposed to effectively solve the problem of the difficulty imbalance between strawberry samples and the influence of the shape and size of the bounding box on the regression. The experimental results show that the model’s precision and mAP0.5 reach 92.1% and 92.7%, respectively, which are 2.0% and 1.7% higher than the baseline model. Additionally, our model is better in detection performance than most models with fewer parameters and lower FLOPs. In summary, the model can accurately identify the maturity of strawberry fruit under complex farmland environments and provide certain technical guidance for automated strawberry-picking robots.

Utilising H5P Simulations to Enhance Social Work Education

Higher education is tasked with the challenge of producing graduate-ready professionals. Thus, alternative learning and assessment activities are needed to provide students with real-life complex experiences, particularly in fields like social work. This quasi-experimental design study explored the effectiveness of H5P virtual simulation to teach assessment and direct practice skills to social work students (n = 80). Mixed-methods data based on the pre/post outcomes of skill development were analysed using descriptive and bi-variate analysis as well as thematic analysis for qualitative data. A Wilcoxon signed-rank test reveals significant outcomes in assessment and practice skills between pre- and post-measures. The qualitative findings include three key themes: (1) realism, (2) engagement, and (3) practice skills. This study provides evidence and highlights the importance of using virtual simulation to help students develop skills to manage complex real-world problems.

Integrating Learning and Serious Game Design: The Development and Application of the Learning and Game Integration (LGI) Framework using the ‘Blackout’ Serious Game

Serious games are increasingly recognised as effective educational tools capable of simulating complex scenarios that traditional methods cannot replicate. This paper introduces the Learning and Game Integration (LGI) Framework, designed to seamlessly integrate educational content with interactive game design. Utilising the “Blackout” serious game, which simulates real world complex problem, this study demonstrates how the LGI Framework can be utilised as learning intervention to enhance learning by teaching Design Thinking to university students. The framework consists of four stages: Educational Content Integration, Theoretical Underpinning, Case Study Selection, and Game Design. The “Blackout” game enables students to engage with various stakeholder perspectives, deeply analyse problems, and develop innovative solutions under crisis conditions. Preliminary feedback from its implementation in an undergraduate business course (N=753) indicates increased student engagement and confidence in applying Design Thinking concepts. This paper contributes to the field of business education by presenting an innovative approach to teaching innovative problem-solving skills through serious games, emphasising the need for holistic educational frameworks that bridge theoretical learning with real-world application.

Embracing ambivalence and hesitation: a Ricoeurian perspective on anticipatory choice processes at the end of life.

Especially older adults are increasingly stimulated to think about, talk about and record their preferences with regard to future (health)care decisions, preferably in a pro-active manner. In this paper, I analyse these anticipatory choice processes. My goal is twofold: Firstly, to provide a deeper understanding of what it actually means to decide in advance about end-of-life treatments or options. Secondly, to make a theoretical contribution to bioethics and ACP-theories by rethinking the concept of end-of-life choices from a phenomenological viewpoint. To achieve this, I start by presenting a case narrative that elucidates how these anticipatory choices are lived. Secondly, I map out a theoretical framework about choice based on the phenomenology of the will of Paul Ricoeur. Finally, guided by this Ricoeurian framework, I investigate the potential meaning of choice in the context of contemporary advance care planning trajectories. The analysis demonstrates that choice and agency always imply notions of passivity and uncontrollability. It also indicates the significant value of hesitation and ambivalence. Moreover, it highlights the importance of the notion of co-responsibility in the context of anticipated end-of-life choices, and the relevant distinction between a (willed) choice and a wish. To improve care and support regarding end-of-life trajectories and to promote meaningful conversations, it is imperative to integrate these underrated elements more substantially in our theories, language and practical approaches. I conclude by suggesting that, in order to do justice to the real-life complexities, we might even need to revise the notion of advance 'directives'.

Proton conductive 2D MXene-derived potassium titanate nanoribbons fabricated electrochemical platform for trace detection of enrofloxacin

In recent years, due to exceptional properties like broad interlayered spacing and low working potential, MXene-derived titanate nanoribbons have been established as promising electrode materials. Herein, the electrocatalytic activity of MXene-derived potassium titanate nanoribbon was employed to develop a voltammetric sensor for the detection of enrofloxacin. The sensor's significance is to provide a sustainable solution to quantify the presence of enrofloxacin regarding food safety and environmental monitoring. Moreover, to achieve the United Nations' Sustainable Development Goals by preventing antimicrobial resistance to accomplish the One Health approach. Potassium titanate nanoribbons were synthesized using 2D Ti3C2 MXene as an active precursor material, while X-ray diffraction spectroscopy, field emission scanning electron microscopy, high-resolution transmission electron microscopy, selected area electron diffraction pattern, elemental mapping, and energy-dispersive X-ray spectroscopy were used to characterize the crystallinity, surface and layered morphology of synthesized nanoribbons. The Brunauer-Emmett-Teller (BET) technique was applied to calculate the specific surface area of the synthesized materials. The materials underwent electrochemical characterization using cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Later on, the nanoribbons were fabricated on the surface of a glassy carbon electrode, and the electro-oxidative behaviour of enrofloxacin was studied by CV, DPV, square wave voltammetry (SWV) in 0.1 M phosphate buffer (optimized pH 8). The developed sensor depicts a significantly lower limit of quantification of 0.007 μM (≈2.5 μg/L), and an upper limit of quantification of 18 μM (≈6.5 mg/L) along with a limit of detection (LOD) of 0.00279, 0.00803, 0.00881 μM obtained from CV, DPV, and SWV respectively. Furthermore, the developed electrodes show a reliable selectivity to be examined in real complex matrices, i.e. marine water, river water, agricultural soil, organic fertilizer, milk, honey, and poultry egg.

A facile synthesis of a novel polymer-based nanocomposite as colorimetric response sensor for fluoride ion detection in real-life complex mediums

The surface modification of Poly (acrylamide-co-methylenbisacrylamide-co-N-(4-(2-(phenylcarbamothioyl)hydrazineyl)phenyl)methacrylamide) as a sensor (AR) via titanium dioxide as a novel colorimetric nanocomposite sensor with high sensitivity and selectivity for determination of fluoride anion was described. The colorimetric response of the AR has been screened in dimethyl sulfoxide /Water with a volumetric ratio of 7:3 and acetonitrile in separate media with equal amounts of various anions. In the presence of the mentioned anionic solutions, only the solutions containing fluoride ions, made a significant change in color from pale yellow to orange-red via the –NH deprotonation mechanism, which could be detected with the naked eye. The Box-Behnken design, response surface methodology through UV-Vis spectroscopy techniques in various pH and temperatures and reaction times were tested to optimize the AR conditions. Also, a lateral flow assay test of the synthesized AR detected fluoride ions in real-life samples, including water, contrary to different sources such as toothpaste and mouthwash. Furthermore, according to the World Health Organization report on fluoride ion toxicity in water, the detection limit of the AR was lower than of the declared level.

Reduced graphene oxide foams modified with caffeic acid for water decontamination: Capture and safe storage of Hg2+

Graphene-based materials are attractive adsorbents for water decontamination. In this work, we have produced eco-friendly reduced graphene oxide (rGO)-based sorbents by the hydrothermal reduction of graphene oxide (GO) in presence of caffeic acid (CA) and further modification with chitosan (CA_CS). Scanning electron microscopy observations show porous macrostructures that turn more compact after CA or CA_CS modification. The specific surface area achieves a maximum value of 293 m2 g−1 for the pristine rGO foam. The macropores are mainly distributed in a range between 1–170 µm. The Hg2+ removal efficiency of the rGO-based foams was investigated across several different parameters. The foam modified with caffeic acid exhibited the most promising performance, demonstrating the highest removal efficiency at pH 4–6, with a maximum sorption capacity of 2.79 mg/g as determined by the Sips equation. Kinetic analysis indicates that intraparticle diffusion and the sorption itself are the rate-limiting steps. The notable sorption efficiency, the low desorption rate, and the applicability in real complex water matrices, including tap water, position rGO_CA as a viable material for efficiently capturing Hg(II) from large volumes of water and securely storing it in a small solid volume.

Hypergraph-Based Influence Maximization in Online Social Networks

Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms.

Recovery of synchronized oscillations on multiplex networks by tuning dynamical time scales

The heterogeneity among interacting dynamical systems or variations in the pattern of their interactions occur naturally in many real complex systems. Often they lead to partially synchronized states like chimeras or oscillation suppressed states like in-homogeneous or homogeneous steady states. In such cases, it is a challenge to get synchronized oscillations in spite of prevailing heterogeneity. In this study, we present a formalism for controlling multi layer, multi timescale systems and show how synchronized oscillations can be restored by tuning the dynamical time scales between the layers. Specifically, we use the model of a multiplex network, where the first layer of coupled oscillators is multiplexed with an environment layer, that can generate various types of chimera states and suppressed states. We show that by tuning the time scale mismatch between the layers, we can revive the synchronized oscillations. We analyse the nature of the transition of the system to synchronization from various dynamical states and the role of time scale mismatch and strength of inter layer coupling in this scenario. We also consider a three-layer multiplex system, where two system layers interact with the common environment layer. In this case, we observe anti synchronization and in-homogeneous steady states on the system layers and by tuning their time scale difference with the environment layer, they undergo transition to synchronized oscillations.

Quality-driven deep feature representation learning and its industrial application to soft sensors

Establishing effective soft sensors relies on feature representation that is capable of capturing critical information. Stacked AutoEncoder (SAE) is able to capture the intricate structures of data characterized by high dimensionality and strong non-linearity by extracting abstract features layer by layer, making it widely used. However, the pretraining process of SAE is unsupervised, which means the features extracted cannot leverage label information to provide more actionable insights for downstream tasks. To extract more valuable feature representation, a new quality-driven dynamic weighted SAE (QD-SAE) is proposed in this paper. By incorporating supervised information dominated by the quality variable into the learned features during the pretraining of the SAE and weighting the abstract features layer by layer, the features that are beneficial to the prediction task are thus focused. In QD-SAE, the supervised information is computed by an improved attention score. In the initial state of the supervised fine-tuning process, the weighted features compose the hidden layers of the entire network. Finally, a benchmark function case and a real complex industrial process case are used to verify the effectiveness and advantages of QD-SAE. The experimental analyses demonstrate that the soft sensor constructed by the QD-SAE can predict the output variable with high accuracy and outperforms the conventional neural networks.

Enhancing YOLO for occluded vehicle detection with grouped orthogonal attention and dense object repulsion

In real-life complex traffic environments, vehicles are often occluded by extraneous background objects and other vehicles, leading to severe degradation of object detector performance. To address this issue, we propose a method named YOLO-OVD (YOLO for occluded vehicle detection) and a dataset for effectively handling vehicle occlusion in various scenarios. To highlight the model attention in unobstructed region of vehicles, we design a novel grouped orthogonal attention (GOA) module to achieve maximum information extraction between channels. We utilize grouping and channel shuffling to address the initialization and computational issues of original orthogonal filters, followed by spatial attention for enhancing spatial features in vehicle-visible regions. We introduce a CIoU-based repulsion term into the loss function to augment the network’s localization accuracy in scenarios involving densely packed vehicles. Moreover, we explore the effect of the knowledge-based Laplacian Pyramid on the OVD performance, which contributes to fast convergence in training and ensures more detailed and comprehensive feature retention. We conduct extensive experiments on the established occluded vehicle detection dataset, which demonstrates that the proposed YOLO-OVD model significantly outperforms 14 representative object detectors. Notably, it achieves improvements of 4.7% in Precision, 3.6% in AP@0.5, and 1.9% in AP@0.5:0.95 compared to the YOLOv5 baseline.

Layer-by-layer adsorption behavior of Cr(VI) on MoS2@Mt induced by sulfamethoxazole

Hexavalent chromium (Cr(VI)) contamination in wastewater is a serious environmental threat. Cr(VI) removal is often interfered by antibiotics in wastewater, however, the underlying mechanism remains unclear. Here, molybdenum disulfide/montmorillonite (MoS2@Mt) composites were prepared and used for Cr(VI) removal to reveal the adsorption behavior of Cr(VI) on MoS2@Mt induced by sulfamethoxazole. The induction effect of the typical antibiotic sulfamethoxazole (SMX) was investigated. The adsorption capacity of MoS2@Mt was maximum at pH 2 and decreased with increasing pH. The performance results showed that SMX promoted Cr(VI) removal, with a maximum increase of 14.80 % in removal efficiency. Cr(VI) adsorption on MoS2@Mt followed the Langmuir model, whereas it was more consistent with the Freundlich model in the presence of SMX. This suggested that SMX induced a monolayer to multilayer shift in Cr(VI) adsorption. XPS results indicated that Cr(VI) removal pathways included Cr(VI) adsorption, chemical reduction of Cr(VI) to trivalent chromium (Cr(III)), and Cr(III) chelation. In particular, electrostatic pulling, coordinated adsorption, and hydrogen bond attraction contributed to Cr(VI) adsorption. Mechanism investigation suggested that Cr(VI) or SMX absorbed on the material surface continued to absorb SMX or Cr(VI) by electrostatic attraction, resulting in layer-by-layer adsorption behavior, which facilitated Cr(VI) removal. In actual wastewater treatment, MoS2@Mt reduced Cr(VI) concentrations (0.15–5 mg/L) to below the World Health Organization standard of 0.05 mg/L in the presence of 0.32 mg/L SMX. Overall, this study revealed the mechanism of SMX-induced Cr(VI) adsorption behavior, which favors the elimination of Cr(VI) from real complex wastewater.

Quantum multicast based on joint remote state preparation

Effective propagation of information among multiple users is the purpose of realizing large-scale quantum communication networks. In this paper, multicast protocols for any single, two and three qubits with real amplitude and complex phase information are presented. They were realized using a composite of Greenberger–Horne–Zeilinger states as shared channels. Joint remote state preparation was the main method for completing quantum multicast. At the same time, quantum state tomography of the schemes was carried out on the IBM Quantum platform. The obtained states were compared with the target states by fidelity. The analysis of communication efficiency and noise effects shows that our protocol has advantages in the case of complex coefficients.

The Anatomy of Leadership: From Aspirational to Dark Side Leadership Traits

Leadership is all about others on your team and in your organization. Surgical professionals are most likely to follow leaders they trust to care about their professional and personal well-being, and are aligned with their values, while setting and implementing organizational strategy. Ironically, in order to lead others well, it is best and most effective if leaders are humble, vulnerable, and willing to learn more about themselves: specifically their behavioral tendencies when they both are at their best and when they are under duress. For us as leaders, this includes understanding our own predominant leadership style(s); and learning how to expand our leadership preferred tendencies to other styles that may be more effective in certain situations. The POSNA leadership program (PLP) enables participants and faculty to study effective leadership principles and theories; evaluate published examples of exceptional and disastrous leadership in volatile, uncertain, complex, and ambiguous (VUCA) real life situations; and have in-depth dialogue on how pediatric orthopaedic surgery leaders can learn from and positively model exemplary leadership characteristics and behaviors. Although our operating rooms, clinics, research and administrative areas are not battlefields, and we are healers not warriors, we and the teams we lead do face uncertain, ambiguous, complex and even volatile situations often enough to require implementation of those refined, high-level skills. Key Concepts(1)Like all leaders, surgeons have preferred leadership styles. Learning about, expanding, and adapting leadership style(s) to specific situations is important and impactful.(2)The most effective leaders are humble, have high integrity, think strategically, are decisive decision makers after input from their trusted professional teams.(3)Trust is the essential ingredient of leading successful teams and organizations. Building trust requires empathy, high task competency, the ability to actively listen and engage in dialogue before and during implementation of plan(s).(4)Volatile, Uncertain, Complex, and Ambiguous (VUCA) dilemmas and challenges occur. Leaders and teams who can manage stress, mobilize necessary resources, and respond in a timely fashion to each VUCA situation are most effective.(5)Disruptive behaviors in our profession are more common than ideal. Responding to unprofessional acts, managing perceptions, and redirection to acceptable behavior lessens safety risk for patients and improves team performance and professional well-being.

Advancing X‐Band Electromagnetic Interference Shielding: Single‐Layer Thin Films of Fe3O4‐MWCNT (1:2)‐PVDF Meta‐Nanocomposite Fabricated by Low‐Cost Drop‐Casting Process

This study, investigate of ternary thermoplastic fluoropolymer blend nanocomposite (NC) system for improved microwave (MW) shielding, consisting of polyvinyl dimethylamine fluoride (PVDF) and Fe3O4‐multiwalled carbon nanotube (MWCNT) (1:2) NC. Using a hydrothermal process, complex hierarchical nanostructures (NSs) of Fe3O4‐MWCNT (1:2) NC are produced. These NSs are then integrated using a drop‐casting technique into PVDF matrices with different wt% (20, 40, and 60 wt%) and thicknesses (20, 50, and 80 μm). The single‐layer thin films (SLFs) with the Fe3O4‐MWCNT (1:2)‐PVDF matrix are successfully produced as a consequence of this approach. The presence of FeOC bonding from X‐ray photoelectron spectroscopy (XPS) analysis confirms chemical interaction, while scanning electron microscopy (SEM–Energy dispersive spectroscopy EDX) imaging analysis for material homogeneity. Moreover, the SLFs of Fe3O4‐MWCNT (1:2)‐PVDF (20, 40, and 60 wt% for 80 μm thickness) are newly discovered meta‐nanocomposites (MNCs) properties, which are evidenced by negativeMW real complex permittivity (−274.4, −271.2, and −136.4), superior attenuation constant (12862.73, 16265.18, and 12256.34), and higher alternating current (AC)electrical conductivities (1020.8, 1174.6, and 727.7 S m−1). The synergistic impact of higher attenuation and AC conductivity of MNCs results in an increased average MW shielding effectiveness of ≈33.28 dB for Fe3O4‐MWCNT (1:2)‐PVDF 20 wt% MNCs (≈80 μm).

Leanness Computation: Small Values and Special Graph Classes

Let u and v be vertices in a connected graph G = (V, E). For any integer k such that 0 ≤ k ≤ dG (u, v), the k-slice Sk (u, v) contains all vertices x on a shortest uv-path such that dG (u, x) = k. The leanness of G is the maximum diameter of a slice. This metric graph invariant has been studied under different names, such as "interval thinness" and "fellow traveler property". Graphs with leanness equal to 0, a.k.a. geodetic graphs, also have received special attention in Graph Theory. The practical computation of leanness in real-life complex networks has been studied recently (Mohammed et al., COMPLEX NETWORKS'21). In this paper, we give a finer-grained complexity analysis of two related problems, namely: deciding whether the leanness of a graph G is at most some small value ℓ; and computing the leanness on specific graph classes. We obtain improved algorithms in some cases, and time complexity lower bounds under plausible hypotheses.

From political priority to service delivery: complexities to real-life priority of abortion services in Ethiopia.

Improving access to abortion services has been coined a high priority by the Ethiopian Federal Ministry of Health. Nevertheless, many women are still struggling to access abortion services. The dedicated commitment to expanding abortion services by central authorities and the difficulties in further improving access to the services make for an interesting case to explore the real-life complexities of health priority setting. This article thus explores what it means to make abortion services a priority by drawing on in-depth interviews with healthcare bureaucrats and key stakeholders working closely with abortion service policy and implementation. Data were collected from February to April 2022. Health bureaucrats from 9 of the 12 regional states in Ethiopia and the Federal Ministry of Health were interviewed in addition to key stakeholders from professional organizations and NGOs. The study found that political will and priority to abortion services by central authorities were not necessarily enough to ensure access to the service across the health sector. At the regional and local level, there were considerable challenges with a lack of funding, equipment and human resources for implementing and expanding access to abortion services. The inadequacy of indicators and reporting systems hindered accountability and made it difficult to give priority to abortion services among the series of health programmes and priorities that local health authorities had to implement. The situation was further challenged by the contested nature of the abortion issue itself, both in the general population, but also amongst health bureaucrats and hospital leaders. This study casts a light on the complex and entangled processes of turning national-level priorities into on-the-ground practice and highlights the real-life challenges of setting and implementing health priorities.

Classroom teacher action recognition based on spatio-temporal dual-branch feature fusion

The classroom teaching action recognition task refers to recognizing and understanding teacher action through video temporal and spatial information. Due to complex backgrounds and significant occlusions, recognizing teacher action in the classroom environment poses substantial challenges. In this study, we propose a classroom teacher action recognition approach based on a spatio-temporal dual-branch feature fusion architecture, where the core task involves utilizing continuous human keypoint heatmap information and single-frame image information. Specifically, we fuse features from two modalities to propose a method combining image spatial information with temporal human keypoint heatmap information for teacher action recognition. Our approach ensures recognition accuracy while reducing the model’s parameters and computational complexity. Additionally, we constructed a teacher action dataset (CTA) in a real classroom environment, comprising 12 action categories, 13k+ video segments, and a total duration exceeding 15 h. The experimental results on the CTA dataset validate the effectiveness of our proposed method. Our research explores action recognition tasks in real complex classroom environments, providing a technical framework for classroom teaching intelligent analysis.

A stable and low-fouling lead ion-selective electrode employing polyaniline-MXene composite for solid contact and polypeptide for antifouling

A biofouling-resistant solid-contact lead ion-selective electrode (Pb2+-ISE) with enhanced stability was developed based on dendritic peptides and polyaniline-MXene composites. The biofouling-resistant dendritic peptides, characterized by their high level of hydration and relative conformational entropy, effectively inhibit nonspecific adsorption. Simultaneously, the synergy between polyaniline nanowires and MXene nanosheets is harnessed to enhance the ion-electron transfer rate, thereby improving the stability of the ISE. The fabricated Pb2+-ISEs exhibit commendable electrochemical short-term stability and can rapidly respond to potential changes within 1 second. Under bacterial solution storage conditions, the peptide-modified ISEs maintain their Nernstian response without significant alteration for up to six days. Additionally, the Pb2+-ISEs are insensitive to O2, CO2, N2, and light, making them suitable for the analysis of Pb2+ in real complex samples. The strategic design incorporating dendritic peptides as a biofouling-resistant layer and the PANI-MXene composite as a conductive layer offers an effective approach to construct low-fouling ISEs capable of operating robustly in complex systems.

Why does community ownership foster greater acceptance of renewable projects? Investigating energy justice explanations

ABSTRACT The fact that community ownership increases the social acceptability of renewable energy projects is well established, but there is a lack of empirical evidence to explain why this is the case. This study examines whether energy justice factors (fair involvement, fair distribution of benefits and perceived impacts from wind turbines) can help explain this relationship, whether these factors are interrelated, and whether different ownership structures (community, shared or private ownership) change these associations. Using a postal survey of residents in Scotland (n = 320), the study employs a novel method, Multigroup-Structural Equation Modelling, to investigate these questions. This modelling reveals that acceptance of these projects was influenced by factors of energy justice (i.e. fair involvement, fair benefits and perceived impacts), but the influence from each factor depended on the ownership structure. Specifically, those near the community-owned project prioritised involvement, those near the privately-owned valued fair benefits, and those near the shared ownership prioritised both. By examining the relationships amongst energy justice factors, the real-life complexity of people’s responses to wind energy projects is revealed, whereby factors can have knock-on effects. Importantly, this study finds that the community and shared ownership projects fostered greater community acceptance than the privately-owned because they had more instances where fair involvement influenced other factors, which led to greater overall acceptance. Although community involvement may seem time-consuming, a more inclusive approach can prevent long-term issues caused by local opposition. Policy Highlights Community ownership of renewable energy projects leads to greater acceptance by local residents, as long as the projects meet their expectations. Energy justice plays a crucial role in acceptance of onshore wind energy projects, but residents prioritise different aspects depending on ownership. Policymakers and developers should take into account the preferences and concerns of local residents when designing renewable projects, especially in terms of fair involvement and fair distribution of benefits. Better support for communities and developers is essential if the Scottish Government want shared ownership offered on all new projects.