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Hexanal-based formulation delays softening in ‘Y368’ kiwifruit across maturity stages by preserving cell wall polysaccharides and suppressing cell wall-degrading enzymes
Get PDFKiwifruit has a limited storage life due to rapid softening, with harvest maturity playing a key role in softening rates, leading to substantial postharvest losses. This study investigated the efficacy of a hexanal-based enhanced freshness formulation (EFF) in delaying the softening of “Y368” kiwifruit harvested at mid and late maturity stages, based on soluble solid content (7 and 9%, respectively). Kiwifruit were treated with 0.01 and 0.02% (v/v) EFF or left untreated (control), and stored for 8 weeks at 0 ºC, followed by one week at 20 °C. Physicochemical properties, cell wall polysaccharides (CWPs), and cell wall-degrading enzymes (CWDEs) were assessed. EFF significantly delayed softening and optimized quality at both maturity stages, with late-harvested fruit exhibiting greater relative softening. Principal component analysis showed that EFF-treated fruit had higher firmness and CWP contents, whereas control fruit exhibited greater mass loss and higher CWDE activities. These results highlight the potential of EFF in delaying softening, preserving fruit quality, and enhancing the storage life of “Y368” kiwifruit, particularly for late-harvested fruit. The findings underscore the importance of harvest maturity and the potential of EFF to mitigate rapid softening, offering valuable insights for enhancing postharvest management in the kiwifruit industry.This work was supported by the National Research Foundation Grant number: [SFH220123657394].International Journal of Fruit Scienc
Unveiling the role of stakeholder involvement for digital transformation of Indian food SMEs
Get PDFWith the advent of digitalization, the economy of the world is quickly changing itself and Indian Small and Medium Enterprises (SMEs) are the front runners. This study sheds light on how digital transformation is crucial to support the growth, competitiveness, and sustainability in the present business environment, and how digital transformation is important to the SMEs in India. To encourage SMEs to take up digital technologies, the Government of India has been creating an environment that encourages such moves by launching several initiatives including Digital India, Make in India and Startup India. This paper studies the role of stakeholder involvement in digital transformation in Indian food SMEs. Structural Equation Modeling (SEM) is used on survey responses from 103 food SMEs using the Statistical Package for the Social Sciences (SPSS). From the findings, there is a close relationship between stakeholder involvement and technological advancement (β = 0.595, p < 0.001) and organizational and political factors (β = 0.709, p < 0.001) as viewed by leadership, which reflects multidimensional factors leading to digital adoption. However, stakeholder involvement does not have any significant effect on financial factors (β = 0.018, p = 0.87), financial constraints being a major barrier to transformation. Moreover, although technological advancement results in a positive effect towards digital transformation (β = 0.694, p < 0.001), organizational and financial challenges act as stumbling blocks altogether. In the case of managers, this study indicates proactive involvement of stakeholders, investment in employees upskilling and alignment of organizational goals with digital initiatives for the support of technology adoption. This information assists decision makers in estimating government incentives and public private partnerships to overcome financial constraints. Digital transformation for food sector SMEs depends on a coordinated support of stakeholders, policy and technological readiness as preconditions for long term competitiveness.Sandeep Jagtap acknowledges the support of FORCE (Centre for Food Preparedness and Competitiveness) ýat Lund University, Sweden.Discover Foo
Dataset relating to Arsenic contamination of rainfed versus irrigated rice
No full textData from rice grain analysis, containing 75 variables and 939 samples. A full description of each of the columns is included in the Ingram2025-Dataset_Description.txt file.Arsenic (As) contamination of rice remains a major human health issue in Asia. Most research has been on irrigated rice. However much of the projected increase in global rice demand over coming decades must be met by rainfed lowland systems, for which As relations are poorly understood. We present the most comprehensive survey to date of As in rice in farmers’ fields across Bangladesh, covering both irrigated and rainfed systems. We collected rice grain and soil at 943 sites in the three rice growing seasons: irrigated Boro, rainfed Aus, and longer-duration rainfed Aman. Grain As concentrations increased in the order Aman << Boro < Aus with 2, 25 and 41 % of the sites exceeding permitted thresholds, respectively. The greater concentration in Aus than Boro challenges the accepted wisdom that contaminated irrigation water is the main source of As. The main growth and grain filling periods, when most As is taken up, coincide in Aus with the peak of the monsoon rains, suggesting a link between rainfall and high grain As. We suggest this is due to stronger soil reducing conditions and hence As solubility during peak rainfall. We discuss implications for rainfed lowland rice across Asia and mitigation options.Biotechnology and Biological Sciences Research Council (BBSRC
Chapter 8: Fostering project social sustainability through stakeholder inclusion
No full textIn project-based organizations, it is essential to respect the needs and expectations of different stakeholders. Sustainability and social outcomes have gained increasing importance, reflecting the demand for positive results in economic, social, and environmental areas. These results determine the actual value an organization contributes to its stakeholders. Aligning with the United Nations’ Sustainable Development Goals, organizations should prioritize sustainable economic growth, infrastructure, reduced urban inequalities, and partnerships in society. Neglecting social sustainability can lead to inequalities and suffering within local communities, posing reputational risks, particularly in complex projects. This chapter emphasizes the significance of stakeholder inclusion in project decision-making for better social sustainability. Project organizations should adopt a strategic and systematic approach, actively involving and harmonizing the interests of all stakeholders to achieve organizational goals and contribute to a cohesive and sustainable world
An improved energy management system framework for solar energy integration.
Get PDFHuo, Da - Associate SupervisorRenewable energy sources like wind and solar play a crucial role in decarbonizing
energy supply, but their variable and intermittent nature lead to reliability and
stability issues. One way of sustainably integrating these energy sources into the
grid is through an energy management system. The study reported in this thesis
gives a comprehensive definition of an integrated energy management system
and creates a novel framework that identifies energy forecasting, demand-side
management, and supply-side management, as crucial components for grid
balancing. In addition, this research looks particularly at solar integration, and
how the integrated energy management system offers a unique combination of
solar energy forecasting, time-of-use tariffs, direct load control demand response,
and generator control, in increasing penetration levels of solar energy. The
significance of this research is that the proposed system presents a viable,
sustainable, and cheaper way of increasing PV usage and thereby grid
penetration by prioritising efficient use of available PV supply before calling up
additional supply. To validate the proposed integrated energy management
system, this research looks to understand the functions of each individual
component and how their interconnectedness creates a novel management
system.
Firstly, this research develops a three-step solar forecasting approach that
uses low-level data fusion to combine weather variables from both an on-site and
a local weather station to improve solar energy forecasting. The forecasting
model response is historic PV generation, and the predictors are weather
variables with moderate to strong positive correlations to solar radiation. Data
obtained is preprocessed using Low-level Data Fusion, Pearson Correlation
Coefficient analysis, Rescaling method, and List-wise Deletion method. This
approach is then tested on a 1MW utility scale solar plant, resulting in a 6% and
13% prediction accuracy improvement when compared to solely using data from
an on-site, and local weather stations respectively. This approach is also
validated for three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW),
achieving root mean square error values of 0.0984, 0.1425, and 0.0885
respectively. The resulting low root mean square error values, a measure of the
predicted PV to actual PV generation, proves that the model can be adopted for
different PV plant sizes and is suitable for any customer across the distributed
generation spectrum. To further improve the accuracy of the model, other
preprocessing techniques are investigated and applied. The study shows that the
combination of Low-level Data Fusion, Linear Interpolation, filling outliers, data
smoothing, Rescaling method, moderate to strong PV correlation of weather
parameters using Pearson Correlation Coefficient, day/time/month
decomposition, seasonal decomposition, Principal Component Analysis, and
holdout validation, increases the accuracy of the model by 75%.
The ability of direct load control to manage energy consumption is
validated in a case study by using Connected Power’s unique smart sockets and
Lumen radio’s Mira Mesh Radio Frequency wireless network. Small plug-in loads
were connected to ten smart sockets located in a robotics laboratory and a café,
resulting in reduced energy consumption by 44% and 72% respectively when
compared to the baseline without direct load control.
Finally, the integrated energy management system framework is validated
by testing its capacity to increase PV usage for an off-grid residential house with
a PV/diesel generator power source. A decision-based algorithm is created that
adjusts PV supply forecast errors, initiates direct load control responses to reduce
excess load during periods of low PV supply, and/or increase power supply by
calling up a diesel generator. In addition, this is combined with the proposed
three-step solar energy forecasting approach and a programmable load schedule
based on time-of-use criteria. The effects of customer behaviour are also
analysed by using a 14% override rate, with 80% preconditioning and 20%
rebounding. The hybrid PV/diesel generator power source with the proposed
integrated energy management system is compared against two configurations:
a baseline configuration that uses a solely diesel generator source, and a hybrid
PV/diesel generator power source. Results show that the integrated energy
management system reduced the lifetime expenditure costs and CO2 emissions
by 44% and 46% respectively when compared to the baseline configuration, and
by 8% and 9% in the hybrid photovoltaic/diesel generator, while also increasing
the PV usage from this configuration by over 113%.
This research also addresses opportunities and limitations of the proposed
system and lays the foundation for future research using other intermittent
renewable energy sources such as wind.PhD in Energy and Powe
UAV operations and vertiport capacity evaluation with a mixed-reality digital twin for future urban air mobility viability
No full textThis article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV ApplicationThis study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem.Drone
Dataset: Videos Driven By Sound
No full textDrive By Wire control for Kia Niro, test results from controlling the test vehicles under various speed tests.The development of safe and reliable navigation platforms for automated vehicles remains a critical focus in autonomous driving research. At the
forefront of this effort is the enhancement of perception systems that enable real-time environmental mapping and obstacle detection. Driven by Sound, a
collaborative initiative led by Calyo, Benedex Robotics, and Cranfield University, aims to create a robust sensing platform that leverages 3D
ultrasonic sensors and Lidar to perform effectively in challenging conditions. Cranfield University's contributions include the development of a state-ofInnovate U
Descriptive maturity model for digital servitization of SMEs
No full textPurpose:
This study develops a descriptive maturity model (MM) to assess and guide the servitization progression of manufacturing SMEs through consensus building with both industry and academic practitioners.
Design/methodology/approach:
The maturity model was developed through a systematic literature review (SLR), validated via the Delphi Survey Method with experts in servitization, and tested with two SMEs in the manufacturing sector.
Findings:
The study identifies 7 core maturity dimensions and 21 sub-dimensions, each mapped to basic, intermediate and advanced servitization levels. The resulting model provides a structured and detailed diagnostic framework.
Research limitations/implications:
Advances existing literature by providing a detailed, empirically tested model that differentiates capabilities across servitization levels. The collaboration between industry and academic practitioners addresses a gap in previous research.
Practical implications:
The model serves as a diagnostic and strategic planning tool for SMEs aiming to transition towards higher levels of servitization. The model supports this strategic transformation by defining the needed capabilities.
Originality/value:
This is among the first studies to use a Delphi method to build consensus between academics and industry practitioners for a digital servitization MM. Additionally, results of testing and refinement of the developed model through practical application with two SME businesses are shown, bridging the gap between theory and real-world implementation.Journal of Manufacturing Technology Managemen
Benchmarking deep reinforcement learning for navigation in denied sensor environments
No full textDeep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assumes perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in two navigation tasks - Lidar + position, and vision end-to-end - with configurable sensor denial effects. In particular, we are interested in comparing how different DRL methods (e.g. model-free, on-policy PPO vs. model-free off-policy TD3, vs. model-based DreamerV3) are affected by imperfect sensor readings. We show that DreamerV3 outperforms other methods in the visual end-to-end navigation task with a dynamic goal. Furthermore, DreamerV3 generally outperforms other methods in sensor-denied environments. In order to improve robustness, we use adversarial training and demonstrate an improved performance in denied environments, although we show that this may lead to the agent learning to choose high-risk actions in case of uncertain sensor readings, which is not appropriate for safety-critical scenarios. We anticipate this benchmark of different DRL methods and the usage of adversarial training to be a starting point for the development of more elaborate navigation strategies that are capable of dealing with uncertain and denied sensor readings.This work with MW and PC is supported by Leonardo UK with Cranfield University, as well as WG and AT is supported by EPSRC TAS-S: Trustworthy Autonomous Systems: Security (EP/V026763/1).Journal of Intelligent & Robotic System
Energy-efficient personalized Federated Learning for establishing Green IoT
No full textGreen Internet of Things (Green IoT) is a technique that intends to reduce energy consumption and carbon emissions of Internet of Things (IoT) devices by optimizing hardware design, communication protocols, and data processing. One of the most promising schemes to realize Green IoT is personalized Federated Learning (pFL). Unfortunately, existing pFL methods still need further improvement in achieving Green IoT from the following aspects. 1) Computational energy consumption: model training on IoT devices generates a substantial amount of computational energy consumption. 2) Model performance: the dynamic role differences in each layer of the trained deep neural network need to be considered. Jointly considering these aspects, we present a novel pFL framework named Energy-Efficient personalized Federated Learning (EE-pFL) for establishing Green IoT. Specifically, an IoT device serves as an edge server. Each IoT device produces a customized model through a model training phase and a model aggregation phase. In the model training phase, a threshold-based sparsification strategy is introduced to reduce the computational energy consumption of IoT devices by selectively executing parameter updates. In the model aggregation phase, layer aggregation and an Adaptive Weight Calculation (AWC) mechanism are proposed to capture dynamic role differences in different layers of a deep neural network. Experimental results demonstrate that EEpFL shows lower computational energy consumption and higher classification accuracy than advanced benchmarks.ICC 2025 - IEEE International Conference on Communication