Hasil untuk "Nutrition. Foods and food supply"

Vincent E. Castillo

Generative Agent-Based Models (GABMs) powered by large language models (LLMs) offer promising potential for empirical logistics and supply chain management (LSCM) research by enabling realistic simulation of complex human behaviors. Unlike traditional agent-based models, GABMs generate human-like responses through natural language reasoning, which creates potential for new perspectives on emergent LSCM phenomena. However, the validity of LLMs as proxies for human behavior in LSCM simulations is unknown. This study evaluates LLM equivalence of human behavior through a controlled experiment examining dyadic customer-worker engagements in food delivery scenarios. I test six state-of-the-art LLMs against 957 human participants (477 dyads) using a moderated mediation design. This study reveals a need to validate GABMs on two levels: (1) human equivalence testing, and (2) decision process validation. Results reveal GABMs can effectively simulate human behaviors in LSCM; however, an equivalence-versus-process paradox emerges. While a series of Two One-Sided Tests (TOST) for equivalence reveals some LLMs demonstrate surface-level equivalence to humans, structural equation modeling (SEM) reveals artificial decision processes not present in human participants for some LLMs. These findings show GABMs as a potentially viable methodological instrument in LSCM with proper validation checks. The dual-validation framework also provides LSCM researchers with a guide to rigorous GABM development. For practitioners, this study offers evidence-based assessment for LLM selection for operational tasks.

Giuseppe Pagano Giorgianni

This paper examines how natural gas supply shocks affect Italian firms' pricing decisions and inflation expectations using quarterly survey data from the Bank of Italy's Survey on Inflation and Growth Expectations (SIGE). We identify natural gas supply shocks through an external IV-VAR approach exploiting likely unexpected news about interruption to gas supplies to Europe. Our findings show that although gas supply shocks do not have huge effects on gas quantity and only modest effect on gas inventories, they are quickly transmitted to spot electricity prices with persistent effects. We then estimate a proxy internalizing BVAR incorporating firm-level variables from SIGE, documenting that gas supply shocks raise firms' current and expected prices as well as inflation uncertainty. Finally, we uncover substantial nonlinearities using state-dependent local projections: under high inflation uncertainty, firms successfully pass cost increases on to consumers, sustaining elevated prices; under low uncertainty, recessionary effects dominate, leading firms to cut prices below baseline.

Hang Wang, Huijie Tang, Ningai Leng et al.

Based on economic theories and integrated with machine learning technology, this study explores a collaborative Supply Chain Management and Financial Supply Chain Management (SCM - FSCM) model to solve issues like efficiency loss, financing constraints, and risk transmission. We combine Transaction Cost and Information Asymmetry theories and use algorithms such as random forests to process multi-dimensional data and build a data-driven, three-dimensional (cost-efficiency-risk) analysis framework. We then apply an FSCM model of "core enterprise credit empowerment plus dynamic pledge financing." We use Long Short-Term Memory (LSTM) networks for demand forecasting and clustering/regression algorithms for benefit allocation. The study also combines Game Theory and reinforcement learning to optimize the inventory-procurement mechanism and uses eXtreme Gradient Boosting (XGBoost) for credit assessment to enable rapid monetization of inventory. Verified with 20 core and 100 supporting enterprises, the results show a 30\% increase in inventory turnover, an 18\%-22\% decrease in SME financing costs, a stable order fulfillment rate above 95\%, and excellent model performance (demand forecasting error <= 8\%, credit assessment accuracy >= 90\%). This SCM-FSCM model effectively reduces operating costs, alleviates financing constraints, and supports high-quality supply chain development.

B. Ross Katz, Abdul Khan, James York-Winegar et al.

Summary: NHANES, the National Health and Nutrition Examination Survey, is a program of studies led by the Centers for Disease Control and Prevention (CDC) designed to assess the health and nutritional status of adults and children in the United States (U.S.). NHANES data is frequently used by biostatisticians and clinical scientists to study health trends across the U.S., but every analysis requires extensive data management and cleaning before use and this repetitive data engineering collectively costs valuable research time and decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that automates the data engineering and management aspects of working with NHANES data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less than $15/yr of ongoing costs for hosting the NHANES data, all while providing researchers with clean data tables that can readily be integrated for large-scale analyses. We provide examples of leveraging BigQuery ML to carry out the process of selecting data, integrating data, training machine learning and statistical models, and generating results all from a single SQL-like query. NHANES-GCP is designed to enhance the reproducibility of analyses and create a well-engineered NHANES data resource for statistics, machine learning, and fine-tuning Large Language Models (LLMs). Availability and implementation" NHANES-GCP is available at https://github.com/In-Vivo-Group/NHANES-GCP

Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu et al.

Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are statistically similar to the original dataset. One critical question is raised: how can we leverage DGMs into morden retail supply chain realm? To address this question, this paper expects to provide a comprehensive review of DGMs and discuss their existing and potential usecases in retail supply chain, by (1) providing a taxonomy and overview of state-of-the-art DGMs and their variants, (2) reviewing existing DGM applications in retail supply chain from a end-to-end view of point, and (3) discussing insights and potential directions on how DGMs can be further utilized on solving retail supply chain problems.

Urvashi Kishnani, Srinidhi Madabhushi, Sanchari Das

Blockchain's influence extends beyond finance, impacting diverse sectors such as real estate, oil and gas, and education. This extensive reach stems from blockchain's intrinsic ability to reliably manage digital transactions and supply chains. Within the oil and gas sector, the merger of blockchain with supply chain management and data handling is a notable trend. The supply chain encompasses several operations: extraction, transportation, trading, and distribution of resources. Unfortunately, the current supply chain structure misses critical features such as transparency, traceability, flexible trading, and secure data storage - all of which blockchain can provide. Nevertheless, it is essential to investigate blockchain's security and privacy in the oil and gas industry. Such scrutiny enables the smooth, secure, and usable execution of transactions. For this purpose, we reviewed 124 peer-reviewed academic publications, conducting an in-depth analysis of 21 among them. We classified the articles by their relevance to various phases of the supply chain flow: upstream, midstream, downstream, and data management. Despite blockchain's potential to address existing security and privacy voids in the supply chain, there is a significant lack of practical implementation of blockchain integration in oil and gas operations. This deficiency substantially challenges the transition from conventional methods to a blockchain-centric approach.

Tong Fang, Hui Zhang, Shangfei Liu et al.

In the core accretion model, planetesimals grow by mutual collisions and engulfing millimeter-to-centimeter particles, i.e., pebbles. Pebble accretion can significantly increase the accretion efficiency and help explain the presence of planets on wide orbits. However, the pebble supply is typically parameterized as a coherent pebble mass flux, sometimes being constant in space and time. Here we solve the dust advection and diffusion within viciously evolving protoplanetary disks to determine the pebble supply self-consistently. The pebbles are then accreted by planetesimals interacting with the gas disk via gas drag and gravitational torque. The pebble supply is variable with space and decays with time quickly, with a pebble flux below 10 $M_\oplus$ Myr$^{-1}$ after 1 Myr in our models. As a result, only when massive planetesimals ($>$ 0.01 $M_\oplus$) are luckily produced by the streaming instability or the disk has low viscosity ($α\sim 0.0001$) can the herd of planetesimals grow over a Mars mass within 2 Myr. By then, planetesimals only capture pebbles about 50 times their mass and as little as 10 times beyond 20 au due to limited pebble supply. Further studies considering multiple dust species in various disk conditions are warranted to fully assess the realistic pebble supply and its influence on planetesimal growth.

Paolo Bottoni, Claudio Di Ciccio, Remo Pareschi et al.

Smart contracts show a high potential for ensuring that Supply Chain Management strategies make a qualitative leap toward higher levels of optimality, not only in terms of efficiency and profitability but also in the aggregation of skills aimed at creating the best products and services to bring to the market. In this article, we illustrate an architecture that employs smart contracts to implement various algorithmic versions of the Income Sharing principle between companies participating in a supply chain. We implement our approach on Hyperledger Fabric, the most widespread platform for private and consortium distributed ledgers, and discuss its suitability to our purposes by comparing this design choice with the alternative given by public blockchains, with particular attention to Ethereum.

César Soto-Valero, Martin Monperrus, Benoit Baudry

The rise of blockchain technologies has triggered tremendous research interest, coding efforts, and monetary investments in the last decade. Ethereum is the single largest programmable blockchain platform today. It features cryptocurrency trading, digital art, and decentralized finance through smart contracts. So-called Ethereum nodes operate the blockchain, relying on a vast supply chain of third-party software dependencies maintained by diverse organizations. These software suppliers have a direct impact on the reliability and the security of Ethereum. In this article, we perform an analysis of the software supply chain of Java Ethereum nodes and distill the challenges of maintaining and securing this blockchain technology.

Hanqing Wu, Shan Jiang, Jiannong Cao

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead.

Neo C. K. Yiu

A variety of innovative software solutions, addressing product anti-counterfeiting and record provenance of the wider supply chain industry, have been implemented. However, these solutions have been developed with centralized system architecture which could be susceptible to malicious modifications on states of product records and various potential security attacks leading to system failure and downtime. Blockchain technology has been enabling decentralized trust with a network of distributed peer nodes to maintain consistent shared states via a decentralized consensus reached, with which an idea of developing decentralized and reliable solutions has been basing on. A Decentralized NFC-Enabled Anti-Counterfeiting System (dNAS) was therefore proposed and developed, decentralizing a legacy anti-counterfeiting system of supply chain industry utilizing enterprise blockchain protocols and enterprise consortium, to facilitate trustworthy data provenance retrieval, verification and management, as well as strengthening capability of product anti-counterfeiting and traceability in supply chain industry. The adoption of enterprise blockchain protocols and implementations has been surging in supply chain industry given its advantages in scalability, governance and compatibility with existing supply chain systems and networks, but development and adoption of decentralized solutions could also impose additional implications to supply chain integrity, in terms of security, privacy and confidentiality. In this research, an empirical analysis performed against decentralized solutions, including dNAS, summarizes the effectiveness, limitations and future opportunities of developing decentralized solutions built around existing enterprise blockchain protocols and implementations for supply chain anti-counterfeiting and traceability.

Diogo Gomes, Julian Gutierrez, Ricardo Ribeiro

We consider a market where a finite number of players trade an asset whose supply is a stochastic process. The price formation problem consists of finding a price process that ensures that when agents act optimally to minimize their trading costs, the market clears, and supply meets demand. This problem arises in market economies, including electricity generation from renewable sources in smart grids. Our model includes noise on the supply side, which is counterbalanced on the consumption side by storing energy or reducing the demand according to a dynamic price process. By solving a constrained minimization problem, we prove that the Lagrange multiplier corresponding to the market-clearing condition defines the solution of the price formation problem. For the linear-quadratic structure, we characterize the price process of a continuum population using optimal control techniques. We include numerical schemes for the price computation in the finite and infinite games, and we illustrate the model using real data.

Rui Yao, Shlomo Bekhor

This paper presents a new ridesharing simulation platform that accounts for dynamic driver supply and passenger demand, and complex interactions between drivers and passengers. The proposed simulation platform explicitly considers driver and passenger acceptance/rejection on the matching options, and cancellation before/after being matched. New simulation events, procedures and modules have been developed to handle these realistic interactions. The capabilities of the simulation platform are illustrated using numerical experiments. The experiments confirm the importance of considering supply and demand interactions and provide new insights to ridesharing operations. Results show that increase of driver supply does not always increase matching option accept rate, and larger matching window could have negative impacts on overall ridesharing success rate. These results emphasize the importance of a careful planning of a ridesharing system.

Abhijit Chakraborty, Yuichi Ikeda

We study on topological properties of global supply chain network in terms of degree distribution, hierarchical structure, and degree-degree correlation in the global supply chain network. The global supply chain data is constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform in 2018. The in- and out-degree distributions are characterized by a power law with in-degree exponent = 2.42 and out-degree exponent = 2.11. The clustering coefficient decays as power law with an exponent = 0.46. The nodal degree-degree correlation indicates the absence of assortativity. The Bow-tie structure of GWCC reveals that the OUT component is the largest and it consists 41.1% of total firms. The GSCC component comprises 16.4% of total firms. We observe that the firms in the upstream or downstream sides are mostly located a few steps away from the GSCC. Furthermore, we uncover the community structure of the network and characterize them according to their location and industry classification. We observe that the largest community consists of consumer discretionary sector mainly based in the US. These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity for propositions S1 (short path length), S2 (power-law degree distribution), S3 (high clustering coefficient), S4 ("fit-gets-richer" growth mechanism), S5 (truncation of power-law degree distribution), and S7 (community structure with overlapping boundaries) in the global supply chain network.

Likwan Cheng, Bryan W. Karney

Scaling in complex supply networks is a population-level optimization phenomenon thought to arise from the evolutions of the underlying individual networks, but the evolution-scaling connection has not been empirically demonstrated. Here, using individually resolved, temporally serial, and population-scope datasets from public water supply networks, we empirically demonstrate this connection. On the log-log plot, structural properties of individual supply networks trace out evolutionary paths describable as linear projectiles, each characterized by a slope reflecting optimized physical economies of scale and an intercept reflecting morphological adaptation to settlement contexts. The universality in scaling slope coexists with the variability in scaling intercept, so that networks of diverse morphologies advance in time along a "common evolutionary track". This cross-level observation establishes that individual-level dynamic evolutions cumulatively manifest population-level optimal scaling in complex water supply networks.

Dirk Witthaut, Martin Rohden, Xiaozhu Zhang et al.

Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood, which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing counter-measures and support network planning and design.

Nelson Christopher Dzupire, Yaw Nkansah-Gyekye

In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot meet customer's demands. In this work we developed a bi-objective model that minimizes system wide costs of the supply chain and delays on delivery of products to distribution centers for a three echelon supply chain. Picking a set of Pareto front for multi-objective optimization problems require robust and efficient methods that can search an entire space. We used evolutionary algorithms to find the set of Pareto fronts which have proved to be effective in finding the entire set of Pareto fronts.

Chi Ho Yeung, K. Y. Michael Wong, Bo Li

A comprehensive coverage is crucial for communication, supply and transportation networks, yet it is limited by the requirement of extensive infrastructure and heavy energy consumption. Here we draw an analogy between spins in antiferromagnet and outlets in supply networks, and apply techniques from the studies of disordered systems to elucidate the effects of balancing the coverage and supply costs on the network behavior. A readily applicable, coverage optimization algorithm is derived. Simulation results show that magnetized and antiferromagnetic domains emerge and coexist to balance the need for coverage and energy saving. The scaling of parameters with system size agrees with the continuum approximation in two dimensions and the tree approximation in random graphs. Due to frustration caused by the competition between coverage and supply cost, a transition between easy and hard computation regimes is observed. We further suggest a local expansion approach to greatly simplify the message updates which shed light on simplifications in other problems.

Gilberto M. Kremer

The aim of this work is to analyze the entropy, entropy flux and entropy supply rate of granular fluids within the frameworks of the Boltzmann equation and continuum thermodynamics. It is shown that the entropy inequality for a granular gas that follows from the Boltzmann equation differs from the one of a simple fluid due to the presence of a term which can be identified as the rate of entropy supply density. From the knowledge of a non-equilibrium distribution function -- valid for for processes closed to equilibrium and quasi-elastic restitution coefficients -- it is obtained that the rate of entropy supply density is equal to the rate of internal energy production density divided by the temperature and the entropy flux is equal to the heat flux vector divided by the temperature. A thermodynamic theory of a granular fluid is also developed whose objective is the determination of the basic fields of mass density, momentum density and internal energy density. The constitutive laws are restricted by the principle of material frame indifference and by the entropy principle. Through the exploitation of the entropy principle with Lagrange multipliers, it is shown that the results obtained from the kinetic theory for granular gases concerning the rate of entropy supply density and entropy flux are valid in general for processes close to equilibrium of granular fluids, where linearized constitutive equations hold.

Takashi Nagatani, Dirk Helbing

Due to delays in the adaptation of production or delivery rates, supply chains can be dynamically unstable with respect to perturbations in the consumption rate, which is known as "bull-whip effect". Here, we study several conceivable production strategies to stabilize supply chains, which is expressed by different specifications of the management function controlling the production speed in dependence of the stock levels. In particular, we will investigate, whether the reaction to stock levels of other producers or suppliers has a stabilizing effect. We will also demonstrate that the anticipation of future stock levels can stabilize the supply system, given the forecast horizon is long enough. To show this, we derive linear stability conditions and carry out simulations for different control strategies. The results indicate that the linear stability analysis is a helpful tool for the judgement of the stabilization effect, although unexpected deviations can occur in the non-linear regime. There are also signs of phase transitions and chaotic behavior, but this remains to be investigated more thoroughly in the future.