Food e-commerce business models and sustainability in chinese market

LAUREA MAGISTRALEL'e-commerce alimentare sta vivendo uno sviluppo continuo e potrebbe avere impatti sempre più rilevanti su diversi ambiti riconducibili alla sostenibilità, inclusi i trasporti, l'uso di energia e lo spreco alimentare. Nonostante la rilevanza sul piano pratico e in termini di ricerca dell'impatto della sostenibilità̀ sullo sviluppo delle aziende e l'industria cinese dell'e-commerce, mancano ricerche che approfondiscano questo tema nel contesto cinese. Questa tesi è stata condotta attraverso un'ampia revisione della letteratura e un approccio esplorativo, attraverso alcuni studi di caso, con l'obiettivo di indagare gli elementi dei diversi modelli di business di e-commerce in ambito alimentare, lo stato attuale di sostenibilità di questi modelli e come sono stati colpiti dall'epidemia di COVID-19. Sono stati sviluppati quattro studi di caso nel contesto e-commerce alimentare cinese. La tesi fornisce una descrizione dei modelli di business di e-commerce alimentare esistenti in Cina e discute l'implicazione di questi modelli sulle prestazioni di sostenibilità. La tesi indaga come le aziende di e- commerce in ambito alimentare sono state colpite dall’emergenza COVID-19 e esplora come hanno risposto alle sfide connesse. L'obiettivo di questo studio è aiutare i professionisti del settore dell'e-commerce alimentare a comprendere come i diversi modelli di business sono posizionati rispetto alle prestazioni di sostenibilità, considerando anche la reazione dell'azienda all’emergenza COVID-19 .Food e-commerce is believed to keep developing and may have sustainability impacts in multiple aspects including transportation, energy use and food waste. Since sustainability s practical significance and impact on the development of companies, and the Chinese food e-commerce industry is of significant research value, there is value and significance in conducting sustainability research on this topic and market. However, there is a lack of research on the sustainable development of food e-commerce in China. Under the background, this thesis was conducted through an extensive literature review and an exploratory multi-case study approach with the aim of investigating the elements of different food e-commerce business models, the current state of sustainability of these food e-commerce business models and how they have been affected by the COVID-19 epidemic. Four cases in the Chinese food e-commerce market have been developed to address the research objectives. The thesis provides a comprehensive description of existing food e-commerce business models in China and discusses the implication of these food e-commerce business models on sustainability performance. The thesis further investigates how food e-commerce companies were affected by the COVID-19 and explored how they have responded to the challenges. The aim of this study is to assist practitioners in the food e-commerce sector in understanding how different business models are positioned in the direction of sustainability. In addition, the company's reaction to the COVID-19 can be incorporated into the case study

Personalized medicine: application to a breast cancer study

In randomized clinical trials, investigators compare the clinical outcomes among treatment arms and make claims on the effectiveness of experimental treatments versus the standard ones. Recent developments in biotechnology and associated biomarkers have led to advances in evaluating heterogeneous patient response and the relationship between treatment responses and certain biomarkers. Precision medicine, therefore, is becoming very popular in the healthcare industry. It is of great public health significance that proper implementation of precision medicine leads to informed and efficient decision making and patient management in clinical practice. Traditionally discovery of a predictive marker of treatment benefit is performed via a test of the interaction term between treatment and the marker of interest in a regression model that predicts the clinical outcome of interest. Recently a new paradigm has been proposed by redefining the search for predictive markers, as the search for an optimal individualized treatment rule (ITR) on treatment selection. Here we describe this new approach and apply those methods to a breast cancer study to identify clinical and genomic markers that are predictive of treatment benefit. The R package “personalized” was used in the implementation. Application of some of these methods does identify optimal ITRs that lead to improved outcomes based on the empirical estimates. However, validation via random splitting of training and testing datasets suggested that the findings may be resulted from over-fitting. These ITR-based methods provide a powerful tool for us to identify predictive markers for treatment response, but caution should be taken especially with high-dimensional marker data

Coherence memory and amnesia in a mode-locked laser

Self-organization of temporal modes in mode-locked lasers usually starts from quantum noise. In this process, incoherent spontaneous emission is steered into coherent ultrashort pulses by dissipation and nonlinearity. In this work, we investigated self-organization dynamics in a mode-locked Mamyshev oscillator starting from coherent pulse seeds as opposed to quantum noise. We observed that the coherence of the seed can be remembered or forgotten depending on the initial inverse population. The excessive nonlinearity in the coherence amnesia regime can devastate the seed coherence, causing the oscillator to undergo a chaotic transition lasting hundreds of round trips before regaining coherence. Conversely, the oscillator converges in only a few round trips for the coherence memory regime. A heterodyne technique was developed to record the fast varying optical phase and characterize these two regimes. Dissipative soliton molecules were synthesized from external pulse pair seeds via the coherence memory pathway. In this case, a plateau of the generated pulse spacing independent from seed pulse spacing, i.e., amnesia of the seed spacing, was observed for close spaced seed pulse pairs. Moreover, we show that pulse seeds can be used for laser reconfiguration and pulse pattern control. Our work paves a way to control transient pulse dynamics and steady pulse forms on demand in mode-locked lasers

Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users

Bikes are among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of riders with different income were rarely studied due to limitations on collecting data. Newly emergent dockless bike-sharing platforms that record detailed information regarding each trip provide us a unique opportunity. Attribute to its better usage flexibility and accessibility, dockless bike-sharing platforms are booming over the past a few years worldwide and reviving the riding fashion in cities. In this work, by exploiting massive riding records in two megacities from a dockless bike-sharing platform, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among users with different income, which indicates that human beings all follow similar physical rules. However, collective mobility patterns, including average range and diversity of visitation, and commuting directions, all exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract more cycling activities are quite different over groups, and locations where users reside are of a low user ratio for both higher and lower income groups. Lower income groups are inclined to visit less flourishing locations, and commute towards the direction to the city center in both cities, and of a smaller mobility diversity in Beijing but a larger diversity in Shanghai. In addition, differences on mobility patterns among socioeconomic categories are more evident in Beijing than in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and towards the transition to a more sustainable green transportation

Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting

Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data privacy protection, domain-specific small language models (SLMs) often underperform LLMs, emphasizing the need for methods that reduce this performance gap while alleviating privacy concerns. In this paper, we present a simple yet effective method that harnesses LLMs' medical proficiency to boost SLM performance in medical tasks under privacy-restricted scenarios. Specifically, we mitigate patient privacy issues by extracting keywords from medical data and prompting the LLM to generate a medical knowledge-intensive context by simulating clinicians' thought processes. This context serves as additional input for SLMs, augmenting their decision-making capabilities. Our method significantly enhances performance in both few-shot and full training settings across three medical knowledge-intensive tasks, achieving up to a 22.57% increase in absolute accuracy compared to SLM fine-tuning without context, and sets new state-of-the-art results in two medical tasks within privacy-restricted scenarios. Further out-of-domain testing and experiments in two general domain datasets showcase its generalizability and broad applicability. Our code can be found at https://github.com/XZhang97666/PrivacyBoost-SLM

AlpaCare:Instruction-tuned Large Language Models for Medical Application

Large Language Models (LLMs) have demonstrated significant enhancements in instruction-following abilities through instruction tuning, achieving notable performances across various tasks. Previous research has focused on fine-tuning medical domain-specific LLMs using an extensive array of medical-specific data, incorporating millions of pieces of biomedical literature to augment their medical capabilities. However, existing medical instruction-tuned LLMs have been constrained by the limited scope of tasks and instructions available, restricting the efficacy of instruction tuning and adversely affecting performance in the general domain. In this paper, we fine-tune LLaMA-series models using 52k diverse, machine-generated, medical instruction-following data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive experimental results on both general and medical-specific domain free-form instruction evaluations showcase AlpaCare's strong medical proficiency and generalizability compared to previous instruction-tuned models in both medical and general domains. We provide public access to our MedInstruct-52k dataset and a clinician-crafted free-form instruction test set, MedInstruct-test, along with our codebase, to foster further research and development. Our project page is available at https://github.com/XZhang97666/AlpaCare

Chronic exercise interventions for executive function in overweight children: a systematic review and meta-analysis

ObjectivesTo systematically evaluate the effectiveness of chronic exercise in physical activity (PA) as an intervention for executive functions (EFs) in children.MethodsWe conducted a systematic search in the following online databases: Web of Science, Cochrane Library, PubMed, Embase, and EBSCOhost. The timing is from database inception to July 2023, following PRISMA guidelines. Our inclusion criteria required studies reporting executive function (EF) levels in overweight children (age 0–18 years) before and after interventions. The Cochrane risk of bias tool assessed study bias, and Egger's test examined publication bias. Subgroup analyses considered three moderators: intervention duration, weekly frequency, and session length.ResultsThe meta-analysis included a total of 10 studies with 843 participants. It revealed a statistically significant yet relatively small overall positive effect (g = 0.3, 95% CI 0.16–0.44, P < 0.01) of chronic exercise on EF in overweight children. Importantly, there was no significant heterogeneity (Q = 11.64, df = 12, P = 0.48; I2 = 0).ConclusionsChronic exercise interventions had a consistent positive impact on EF, irrespective of intervention duration, weekly frequency, or session length. However, given limitations in the number and design of studies, further high-quality research is needed to strengthen these conclusions.Systematic Review RegistrationPROSPERO identifier (CRD42023468588)

MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation

We propose a novel framework for RGB-based category-level 6D object pose and size estimation. Our approach relies on the prediction of normalized object coordinate space (NOCS), which serves as an efficient and effective object canonical representation that can be extracted from RGB images. Unlike previous approaches that heavily relied on additional depth readings as input, our novelty lies in leveraging multi-view information, which is commonly available in practical scenarios where a moving camera continuously observes the environment. By introducing multi-view constraints, we can obtain accurate camera pose and depth estimation from a monocular dense SLAM framework. Additionally, by incorporating constraints on the camera relative pose, we can apply trimming strategies and robust pose averaging on the multi-view object poses, resulting in more accurate and robust estimations of category-level object poses even in the absence of direct depth readings. Furthermore, we introduce a novel NOCS prediction network that significantly improves performance. Our experimental results demonstrate the strong performance of our proposed method, even comparable to state-of-the-art RGB-D methods across public dataset sequences. Additionally, we showcase the generalization ability of our method by evaluating it on self-collected datasets

New estimation of overland flow velocity based on Manning's equation and equivalent roughness

The overland flow velocity is a fundamental hydraulic variable that directly impacts soil separation, sediment transport, and material deposition during soil erosion. Accurately predicting this velocity is challenging due to the significant spatial variability of overland flow hydraulic characteristics resulting from vegetation covers. To quantify the influence of vegetation stem coverage on mean flow velocity, fixed-bed scouring tests were conducted with eleven stem coverages (0-20.42%), four slope gradients (3.49%-20.78%), and five unit discharges (0.278-2.222 L·m −1 ·s −1). The results indicated a trend of mean flow velocity initially increasing and then decreasing as stem coverage rises, with a critical threshold value identified at 2.72%. Compared to studies on bare slopes, an increase in mean velocity ranging from 8.22% to 49.91% was observed on slopes with a stem coverage of 2.72%. The accuracy of velocity prediction can be improved by utilizing both discharge and slope gradient, as opposed to methods that rely solely on unit discharge for prediction. The adjusted correction coefficient (adj. R²) increased from 0.205-0.610 to 0.754-0.968. Finally, a dimensionally harmonious flow velocity prediction model based on the adjusted Manning's equation was established using the equivalent roughness, where flow resistance was found to be a combination of grain resistance and stem resistance. The developed model has been proven accurate in predicting experimental results (adj. R² = 0.831, Nash-Sutcliffe efficiency coefficient = 0.830). The results obtained here enhance our understanding and prediction of soil erosion, providing further insights into the phenomenon

Quantitative evaluation of the effects of artificial grass and stem covers on overland flow hydrodynamics

Vegetation coverage on hillslopes substantially alters the hydrological and hydraulic erosion processes of overland flow; however, the mechanisms by which grass-shrub community coverage influences overland flow hydrodynamics remain insufficiently understood. To clarify the effects of varying grass-shrub coverage on flow dynamics, a systematic flume experiment with a non-erodible bed was conducted. The experiment design comprised 25 combinations of artificial grass (Cg) and stem cover (Cs) (five levels each), five unit discharges (q) ranging from 0.278 to 2.222 L·m−1·s−1, and four slope gradients between 2° and 12°. The results revealed that: 1) the observed flow was predominantly laminar and transitional, with the onset of transitional flow primarly governed by discharge, occurring at a critical threshold of 0.556 L·m−1·s−1. Higher vegetation coverage facilitated the transition of overland flow from supercritical to subcritical regimes, whereas steeper slopes increased the vegetation coverage threshold required for this transition. (2) Increasing vegetation coverage altered the relationship between Manning’s n and the discharge q from negative to positive, while steeper slopes reversed this trend—transforming positive correlations into negative ones and diminishing the proportional increase of Manning’s n with coverage. Vegetation also attenuated the rate at which mean velocity increased with q: as q rose from 0.278 to 2.222 L·s−1·m−1, velocity increased by 118 % at Cg = 0 but only 13 % at Cg = 65.97 %. This underscores vegetation’s critical role in modulating flow resistance and velocity. (3) A predictive model for mean flow velocity under artificial grass-shrub vegetation was developed and rigorously evaluated through error and sensitivity analyses. The model mechanistically incorporates both particle resistance (arising from substrate roughness) and form drag (induced by vegetation morphology). It demostrated high predictive accuracy when validated against the experimental dataset, achieving an adjusted R2 of 0.877 and a Nash–Sutcliffe efficiency of 0.875. For the study dataset, the mean relative error was − 0.029 (standard deviation = 0.164). These findings substantially advance the mechanistic understanding of overland flow hydrodynamics in the presence of grass and stem cover