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Institutional Stimulus and Firm Innovativeness: Examining the Roles of Digital Technologies Adoption and Inbound Openness
No full textDrawing on the structure–conduct–performance (SCP) paradigm, this study proposes and tests a framework of how government institutional stimulus can spur small‐ and medium‐sized enterprises' (SMEs) innovativeness. An analysis of survey data from 195 SMEs operating in Ghana—a resource‐constrained developing economy—indicates that (1) institutional stimulus has a positive relationship with SME innovativeness; (2) the effect of institutional stimulus on SMEs' innovativeness is channeled through the adoption of relevant digital technologies; and (3) the positive effect of institutional stimulus on firm innovativeness through the adoption of digital technologies is strengthened under high levels of inbound openness. Our findings make important contributions to the extant innovation and R&D management literature and have practical implications.</p
Machine learning-based algorithm selection for irregular three-dimensional packing in additive manufacturing
No full textIn additive manufacturing (AM), a common problem is the efficient arrangement of arbitrary three-dimensional objects, subject to geometric constraints. This can be mapped to three-dimensional irregular packing (3DIP) problems, which have been systematically addressed by many academics and practitioners. This study demonstrates the utilisation of machine learning for algorithm selection to find efficient layout configurations during the design stage of the AM process. The choice of the most suitable approach to use, typically a packing algorithm, is not trivial, and depends on the non-obvious relationship between the characteristics of the instance in hand and the portfolio of algorithms available. The matching between problem features and algorithm performance forms the basis of the well-known algorithm selection problem. This study introduces the first empirical investigation of algorithm selection for 3DIP problems, conducting extensive experiments with hundreds of combinations of well-known supervised machine learning classifiers and different parameter settings to identify an initial state-of-the-art for this problem. We generate a comprehensive dataset, labelled with the performance of two of the most popular 3DIP algorithms, and analyse the features which can be used to support decision making when selecting a method to solve a 3DIP instance. Our results show that deploying machine learning-based algorithm selection methods are able to outperform the results obtained by the individual constituent packing algorithms applied independently, with the best algorithm selection method obtaining a 1.48% higher average build volume utilisation over the 2000 problem instances tested.</p
A Model of Circular Work
No full textPurposeAn implicit tendency in management to view work as a one-directional linear process aimed at maximising work outputs risks treating the inputs used to achieve work goals as finite and depletable. These inputs are vital human resources that should be protected and nurtured. This article examines the nature and lifecycle of work resources in the work process, identifies resource regeneration as essential for sustainable work, and as a result proposes a model of circular work (CW).Design/methodology/approachBuilding on an evaluation of classical management theory, ideas from circular economy, and insights from management and from work and organisational psychology, we propose the concept of CW as a sustainable alternative to current understandings of work.FindingsWe build a cross-level model of CW that views work resources as renewable, describes how their depletion and loss can be avoided, and makes regeneration an essential condition for circularity, where the inputs used to produce work outputs are returned into the work process. CW is diametrically distinct from historical views about how work works and aligned with growing concerns around resource regeneration and sustainability. We outline four principles of CW and examine possible mechanisms through which work resources can be regenerated to prevent their depletion.Practical implicationsThe notions of resource regeneration and circularity create avenues for designing and managing work in a more holistic and future-orientated way that protects work resources and is sustainable for individuals and organisations alike.Originality/valueCW offers an alternative way to work design that reflects a concern about the lifecycle of resources in the process of work. We discuss implications for designing CW, advancing research and creating healthy, productive and sustainable work.</p
Ultraviolet–Visible Spin‐Resolved Chip‐Scale Spectroscopy
No full textHigh‐resolution and lightweight broadband spectrometers with polarization‐resolving capabilities are crucial for advancing the agrochemical and pharmaceutical industries. Such cutting‐edge devices enable precise chemical analysis and process monitoring by transforming complex optical setups into compact, cost‐effective solutions. However, due to bulky and complex assemblies, achieving simultaneous spectral and polarization information remains challenging. To overcome this, this study develops and experimentally demonstrates a dual‐band, highly efficient circular polarization multiplexed multifocal metalens spectrometer, unlocking the spectral content and polarization information within the Ultraviolet–Visible (UV–Vis) is wavelength range (320–450 nm). Using bandgap‐engineered CMOS‐compatible silicon nitride, this design optimizes single‐element planar arrays to replicate the functionality of multiple optical components. It integrates wavelength, phase, and polarization multiplexing within a single, minimalistic structure. The Pancharatnam–Berry Phase (PB) phase element‐based geometric metasurface maps spectral data into focused spots, enabling circular polarization discrimination. Simulated and experimental results align closely, confirming the effectiveness of the approach. This design platform simplifies the optical information process and supports the generation of portable chiral spectrometers, a groundbreaking advancement for numerous real‐life applications like environmental sensing, healthcare, and many more.</p
Predictors of psychological well-being during imposed prolonged absence from work.
No full textBACKGROUND: Between March 2020 and September 2021, 11.7 million employee jobs were furloughed through the UK Coronavirus Job Retention Scheme (JRS). Imposed work absence shielded workers from job loss, but furloughed workers had increased risk of poor mental health compared to those who stayed working. Understanding the factors that mitigate psychological distress during imposed work absence can inform actions to be taken in future crises. AIMS: To explore the relationships between (a) work and home demands with well-being outcomes, and (b) personal and organisational resources with well-being outcomes, during periods of imposed prolonged absence and uncertainty. METHODS: We analysed online survey data collected with furloughed workers in the UK 'Wellbeing of the Workforce Study'. Measures included psychological well-being, anxiety, life satisfaction, job insecurity, home demands (quantitative and emotional), organisational support for work-family balance, and personal resources (resilience, purpose, and coping ability). RESULTS: Psychological well-being was associated positively with quantitative home demands (β = 0.24, p </p
WVTF: A Transformer-Based Model Toward Improving Wheat Yield Estimation Under Mode Decomposition Technique
No full textAccurate crop yield estimation in advance is of paramount importance for making appropriate decisions in adjusting food price and formulating food policy. Most traditional statistical yield estimation models directly capture the nonlinear relationship between the original time-series remotely sensed variables and yields, ignoring the complexity and nonstationarity in the remotely sensed variables. This study integrated variational mode decomposition (VMD), correlation analysis, and neural network architecture to construct a decomposition–screening–reconstruction–estimation paradigm for wheat yield estimation. First, VMD optimized by the whale optimization algorithm (WOA) was applied to decompose the time-series remotely sensed variables into intrinsic mode function (IMF) components. Second, effective IMF components with strong correlation to the original time-series remotely sensed variables were identified and reconstructed as the key time-series features. Finally, the performances under the proposed paradigm of the Transformer model and baseline models including support vector regression (SVR), random forest (RF), and long short-term memory (LSTM) were compared. The results showed that WOA-VMD-Transformer (WVTF) (R2 =0.66 , root-mean-squared error (RMSE) = 459.09 kg/ha, and mean relative error (MRE) = 8.22%) outperformed other models, and deep learning models (LSTM and Transformer) based on the new paradigm exhibited superior performance compared with traditional models (SVR and RF). In addition, WVTF (R2 =0.54 , RMSE = 468.19 kg/ha, and MRE = 6.13%) provided better performance at the sampling sites compared with the previous proposed model (R2 =0.45 , RMSE = 738.63 kg/ha, and MRE = 8.21%). The estimated wheat yields of 2019–2024 based on the optimal model were identical to the distribution of actual yields. In conclusion, the framework for yield estimation under a novel paradigm extracts the original remotely sensed variables into key time-series features, which provides an important reference for regional crop yield estimation and agricultural development.</p
Estimating wheat grain filling course by fusing digital and thermal infrared images
No full textGrain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R2 = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.</p
The Role of Adaptive and Innovative Trial Designs in Diabetes Research: A Scoping Review.
No full textBACKGROUND: Adaptive and master protocol clinical trials offer significant advantages for diabetes research, including enhanced efficiency and personalized treatment strategies. PURPOSE: This scoping review aimed to systematically map the use of adaptive and master protocol designs in interventional trials for type 1 and type 2 diabetes, identify research gaps, and highlight opportunities for broader implementation. DATA SOURCES: A systematic literature search was performed using MEDLINE, Embase, CENTRAL, Emcare, Global Health, Web of Science, and clinical trial registries. Gray literature searches complemented database findings. STUDY SELECTION: Studies using adaptive, platform, basket, or umbrella trial designs in people with type 1 or type 2 diabetes were included. DATA EXTRACTION: Data were charted using a standardized form. Extracted variables included diabetes type, trial design, adaptive features, interventions, end points, and key findings. DATA SYNTHESIS: Of 396 articles screened, 6 published adaptive trials met the inclusion criteria: 3 in type 1 diabetes, 1 in type 2 diabetes, and 2 in diabetes-related neuropathy. Most used adaptive features for dose finding, response-adaptive randomization, and sample size reestimation. No published platform, basket, or umbrella trials were identified. Six ongoing adaptive trials in type 1 diabetes were identified through registry searches, four under an adaptive platform master protocol. LIMITATIONS: Despite a comprehensive search, some gray literature and unpublished studies may have been missed. Risk of bias was not assessed, consistent with scoping review methodology. CONCLUSIONS: Adaptive and master protocol trials remain rare in diabetes. Overcoming barriers through targeted training and awareness, robust regulatory frameworks, and strategic incentives could support broader adoption.</p
Cardiorespiratory fitness in kidney transplant recipients: a pilot randomised controlled trial of structured home-based rehabilitation and a nested case-control analysis
No full textNo description supplied</p
Cardiovascular Disease in the Middle East and North Africa
No full textThe Middle East and North Africa (MENA) region includes 21 countries with acombined population of roughly 700 million, stretching from Morocco in thewest to Afghanistan in the east, and spanning high-income nations such asQatar (∼76,000 USD in gross domestic product per capita), to economicallyconstrained settings such as Yemen (∼400 USD in gross domestic productper capita). Despite this variability, MENA countries share commonchallenges: high prevalence of obesity, diabetes, hypertension, tobacco use,and physical inactivity. The region is also experiencing rapid urbanization,increasing environmental stress, and persistent inequities in health careaccess and quality. According to the 2023 Global Burden of Diseasestudy,1 MENA had the highest or among the highest cardiovascular burdenattributable to hyperglycemia, hypertension, obesity, hyperlipidemia, poor diet,ambient temperatures, and lead exposure. Tobacco-related cardiovasculardisease (CVD) was especially high in countries such as Egypt.</p