Explore the vast range of titles available.

PurposeThe agricultural supply chain is susceptible to disruptive geopolitical events. Therefore, agri-food firms must devise robust resilience strategies to hasten recovery and mitigate global food security effects. Hence, the central aim of this paper is to investigate how supply chains could leverage digital technologies to design resilience strategies to manage uncertainty stemming from the external environment disrupted by a geopolitical event. The context of the study is the African agri-food supply chain during the Russian invasion of Ukraine.Design/methodology/approachThe authors employ strategic contingency and dynamic capabilities theory arguments to explore the scenario and conditions under which African agri-food firms could leverage digital technologies to formulate contingency strategies and devise mitigation countermeasures. Then, the authors used a multi-case-study analysis of 14 African firms of different sizes and tiers within three main agri-food sectors (i.e. livestock farming, food-crop and fisheries-aquaculture) to explore, interpret and present data and their findings.FindingsDownstream firms (wholesalers and retailers) of the African agri-food supply chain are found to extensively use digital seizing and transforming capabilities to formulate worst-case assumptions amid geopolitical disruption, followed by proactive mitigation actions. These capabilities are mainly supported by advanced technologies such as blockchain and additive manufacturing. On the other hand, smaller upstream partners (SMEs, cooperatives and smallholders) are found to leverage less advanced technologies, such as mobile apps and cloud-based data analytics, to develop sensing capabilities necessary to formulate a “wait-and-see” strategy, allowing them to reduce perceptions of heightened supply chain uncertainty and take mainly reactive mitigation strategies. Finally, the authors integrate their findings into a conceptual framework that advances the research agenda on managing supply chain uncertainty in vulnerable areas.Originality/valueThis study is the first that sought to understand the contextual conditions (supply chain characteristics and firm characteristics) under which companies in the African agri-food supply chain could leverage digital technologies to manage uncertainty. The study advances contingency and dynamic capability theories by providing a new way of interacting in one specific context. In practice, this study assists managers in developing suitable strategies to manage uncertainty during geopolitical disruptions.

PurposeDrawing on institutional theory, this study investigates the role of individual cultural values on the adoption of socially sustainable supply chain management (socially SSCM) for Chinese suppliers facing the normative institutional pressures of guanxi (interpersonal relationships).Design/methodology/approachUsing empirical data collected in three waves from 205 Chinese manufacturers supplying international markets, the proposed theoretical model is tested through partial least squares structural equation modeling (PLS-SEM).FindingsThe results indicate that guanxi has a positive impact on socially SSCM, and this positive effect is strengthened when the individual cultural values of the supplier's representative embody high collectivism and low uncertainty avoidance.Research limitations/implicationsThis study highlights the leading role of guanxi in improving socially SSCM practices due to its normative institutional force. In addition, the findings suggest that future studies should consider individual differences in supply chain partners, which may lead to distinct reactions when facing normative institutional pressures.Practical implicationsThis study suggests international buyers should adopt guanxi management with their Chinese suppliers to encourage them to adopt socially SSCM. In addition, managers should note that the guanxi strategy is more effective when the supplier's representative collectivism is high and uncertainty avoidance is low.Originality/valueThis study contributes to socially SSCM research in emerging economies by unveiling the role of guanxi as a key driver of socially SSCM in the Chinese market and providing empirical evidence of the moderating effect of individual culture on the guanxi normative institutionalization process.

The study aims to propose a decision-making framework for prioritising solutions to overcome barriers to omnichannel adoption in logistics. An empirical case study has been carried out to assess the practicality and validity of this framework in the actual environment. The decision-making framework embraces an integrated approach comprising of fuzzy Delphi Method (FDM), fuzzy Analytic Hierarchy Process (FAHP) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) for capturing human thinking and subjectivity for doing evaluation and prioritisation of solutions. The proposed framework has been evaluated in an Indian apparel firm. The results suggest that the proposed framework can identify and prioritise the solutions of omnichannel adoption in logistics to overcome its barriers. The proposed framework relies on the well-structured knowledge and personal experience of potential experts to consider various fuzzy regions in the decision making. The present framework offers guidelines to practitioners for the successful adoption of omnichannel retailing regarding the identification and prioritisation of solutions to overcome the barriers of omnichannel adoption.

Emotional stress is associated with increased risk of cardiovascular disease. We imaged the amygdala, a brain region involved in stress, to determine whether its resting metabolic activity predicts risk of subsequent cardiovascular events. Individuals aged 30 years or older without known cardiovascular disease or active cancer disorders, who underwent 18F-fluorodexoyglucose PET/CT at Massachusetts General Hospital (Boston, MA, USA) between Jan 1, 2005, and Dec 31, 2008, were studied longitudinally. Amygdalar activity, bone-marrow activity, and arterial inflammation were assessed with validated methods. In a separate cross-sectional study we analysed the relation between perceived stress, amygdalar activity, arterial inflammation, and C-reactive protein. Image analyses and cardiovascular disease event adjudication were done by mutually blinded researchers. Relations between amygdalar activity and cardiovascular disease events were assessed with Cox models, log-rank tests, and mediation (path) analyses. 293 patients (median age 55 years [IQR 45·0–65·5]) were included in the longitudinal study, 22 of whom had a cardiovascular disease event during median follow-up of 3·7 years (IQR 2·7–4·8). Amygdalar activity was associated with increased bone-marrow activity (r=0·47; p<0·0001), arterial inflammation (r=0·49; p<0·0001), and risk of cardiovascular disease events (standardised hazard ratio 1·59, 95% CI 1·27–1·98; p<0·0001), a finding that remained significant after multivariate adjustments. The association between amygdalar activity and cardiovascular disease events seemed to be mediated by increased bone-marrow activity and arterial inflammation in series. In the separate cross-sectional study of patients who underwent psychometric analysis (n=13), amygdalar activity was significantly associated with arterial inflammation (r=0·70; p=0·0083). Perceived stress was associated with amygdalar activity (r=0·56; p=0·0485), arterial inflammation (r=0·59; p=0·0345), and C-reactive protein (r=0·83; p=0·0210). In this first study to link regional brain activity to subsequent cardiovascular disease, amygdalar activity independently and robustly predicted cardiovascular disease events. Amygdalar activity is involved partly via a path that includes increased bone-marrow activity and arterial inflammation. These findings provide novel insights into the mechanism through which emotional stressors can lead to cardiovascular disease in human beings. None.

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT–PCR assay and next-generation sequencing RT–PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. Artificial intelligence algorithms integrating chest computed tomography scans and clinical information can diagnose COVID-19 with similar accuracy as compared to a senior radiologist.

PurposeAgriculture value chains (AVCs) have experienced unprecedented disruption during the COVID-19 pandemic, with lockdowns and stringent social distancing restrictions making buying and selling behaviours complex and uncertain. This study aims provide a theoretical framework describing the stakeholder behaviours that arise in severely disrupted value chains, which give rise to inter-organisational initiatives that impact industry sustainability.Design/methodology/approachA mixed-methods approach is adopted, in which uncertainty theory and relational governance theory and structured interviews with 15 AVC stakeholders underpin the initial conceptual model. The framework is empirically validated via partial least squares structural equation modelling using data from an online survey of 185 AVC stakeholders based in India.FindingsThe findings reveal that buyer and supplier uncertainty created by the COVID-19 lockdowns gives rise to behaviours that encourage stakeholders to engage in relational governance initiatives. Progressive farmers and other AVC stakeholders welcome this improved information sharing, which encourages self-reliance that positively impacts agricultural productivity and sustainability.Practical implicationsThe new framework offers farmers and other stakeholders in developing nations possibilities to sustain their AVCs even in dire circumstances. In India, this also requires an enabling ecosystem to enhance smallholders' marketing power and help them take advantage of recent agricultural reforms.Originality/valueResearch is scarce into the impact of buyer and seller behaviour during extreme supply chain disruptions. This study applies relational governance and uncertainty theories, leading to a proposed risk aversion theory.

PurposeThe study aims to synthesize existing knowledge and proposes a research framework for building a resilient supply chain (SC) through artificial intelligence (AI) technology. It also identifies existing literature gaps and paves the way for a future research agenda.Design/methodology/approachA systematic literature review has been carried out to identify the peer-reviewed articles from Scopus and Web of Science databases. Then, the selected articles published between 2012 and 2023 are analyzed using descriptive and thematic analysis methods to unearth research gaps and offer new research directions.FindingsDescriptive and thematic analysis reveals the overall development of literature on the role of AI for supply chain resilience (SCR). Based on the findings of the thematic analysis, the motivation, application, capability and outcome (MACO) framework has been developed and propositions have been proposed. Several future research directions have also been suggested in terms of theory, context and methodology (TCM).Practical implicationsThe study provides a fresh perspective on the integration of AI technology within the realm of SCR. The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.Originality/valueThis study contributes to the literature on the role of AI for building SCR by uncovering gaps, offering research directions and developing propositions for future research directions.

Dalcetrapib modulates cholesteryl ester transfer protein (CETP) activity to raise high-density lipoprotein cholesterol (HDL-C). After the failure of torcetrapib it was unknown if HDL produced by interaction with CETP had pro-atherogenic or pro-inflammatory properties. dal-PLAQUE is the first multicentre study using novel non-invasive multimodality imaging to assess structural and inflammatory indices of atherosclerosis as primary endpoints. In this phase 2b, double-blind, multicentre trial, patients (aged 18–75 years) with, or with high risk of, coronary heart disease were randomly assigned (1:1) to dalcetrapib 600 mg/day or placebo for 24 months. Randomisation was done with a computer-generated randomisation code and was stratified by centre. Patients and investigators were masked to treatment. Coprimary endpoints were MRI-assessed indices (total vessel area, wall area, wall thickness, and normalised wall index [average carotid]) after 24 months and 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT assessment of arterial inflammation within an index vessel (right carotid, left carotid, or ascending thoracic aorta) after 6 months, with no-harm boundaries established before unblinding of the trial. Analysis was by intention to treat. This trial is registered at ClinicalTrials.gov, NCT00655473. 189 patients were screened and 130 randomly assigned to placebo (66 patients) or dalcetrapib (64 patients). For the coprimary MRI and PET/CT endpoints, CIs were below the no-harm boundary or the adverse change was numerically lower in the dalcetrapib group than in the placebo group. MRI-derived change in total vessel area was reduced in patients given dalcetrapib compared with those given placebo after 24 months; absolute change from baseline relative to placebo was −4·01 mm 2 (90% CI −7·23 to −0·80; nominal p=0·04). The PET/CT measure of index vessel most-diseased-segment target-to-background ratio (TBR) was not different between groups, but carotid artery analysis showed a 7% reduction in most-diseased-segment TBR in the dalcetrapib group compared with the placebo group (–7·3 [90% CI −13·5 to −0·8]; nominal p=0·07). Dalcetrapib did not increase office blood pressure and the frequency of adverse events was similar between groups. Dalcetrapib showed no evidence of a pathological effect related to the arterial wall over 24 months. Moreover, this trial suggests possible beneficial vascular effects of dalcetrapib, including the reduction in total vessel enlargement over 24 months, but long-term safety and clinical outcomes efficacy of dalcetrapib need to be analysed. F Hoffmann-La Roche Ltd.

Background Intestinal derived endotoxin and the subsequent endotoxemia can be considered major predisposing factors for diseases such as atherosclerosis, sepsis, obesity and diabetes. Dietary fat has been shown to increase postprandial endotoxemia. Therefore, the aim of this study was to assess the effects of different dietary oils on intestinal endotoxin transport and postprandial endotoxemia using swine as a model. We hypothesized that oils rich in saturated fatty acids (SFA) would augment, while oils rich in n-3 polyunsaturated fatty acids (PUFA) would attenuate intestinal endotoxin transport and circulating concentrations. Methods Postprandial endotoxemia was measured in twenty four pigs following a porridge meal made with either water (Control), fish oil (FO), vegetable oil (VO) or coconut oil (CO). Blood was collected at 0, 1, 2, 3 and 5 hours postprandial and measured for endotoxin. Furthermore, ex vivo ileum endotoxin transport was assessed using modified Ussing chambers and intestines were treated with either no oil or 12.5% (v/v) VO, FO, cod liver oil (CLO), CO or olive oil (OO). Ex vivo mucosal to serosal endotoxin transport permeability (Papp) was then measured by the addition of fluorescent labeled-lipopolysaccharide. Results Postprandial serum endotoxin concentrations were increased after a meal rich in saturated fatty acids and decreased with higher n-3 PUFA intake. Compared to the no oil control, fish oil and CLO which are rich in n-3 fatty acids reduced ex vivo endotoxin Papp by 50% (P < 0.05). Contrarily, saturated fatty acids increased the Papp by 60% (P = 0.008). Olive and vegetable oils did not alter intestinal endotoxin Papp. Conclusion Overall, these results indicate that saturated and n-3 PUFA differentially regulate intestinal epithelial endotoxin transport. This may be associated with fatty acid regulation of intestinal membrane lipid raft mediated permeability.