Hasil untuk "Nutrition. Foods and food supply"

Donglin Zhang, Weixiang Shi, Boyuan Ma et al.

In recent years, food nutrition estimation has received growing attention due to its critical role in dietary analysis and public health. Traditional nutrition assessment methods often rely on manual measurements and expert knowledge, which are time-consuming and not easily scalable. With the advancement of computer vision, RGB-based methods have been proposed, and more recently, RGB-D-based approaches have further improved performance by incorporating depth information to capture spatial cues. While these methods have shown promising results, they still face challenges in complex food scenes, such as limited ability to distinguish visually similar items with different ingredients and insufficient modeling of spatial or semantic relationships. To solve these issues, we propose an Ingredient-Guided Semantic Modeling Network (IGSMNet) for food nutrition estimation. The method introduces an ingredient-guided module that encodes ingredient information using a pre-trained language model and aligns it with visual features via cross-modal attention. At the same time, an internal semantic modeling component is designed to enhance structural understanding through dynamic positional encoding and localized attention, allowing for fine-grained relational reasoning. On the Nutrition5k dataset, our method achieves PMAE values of 12.2% for Calories, 9.4% for Mass, 19.1% for Fat, 18.3% for Carb, and 16.0% for Protein. These results demonstrate that our IGSMNet consistently outperforms existing baselines, validating its effectiveness.

Aspen Hopkins, Sarah H. Cen, Andrew Ilyas et al.

The widespread adoption of AI in recent years has led to the emergence of AI supply chains: complex networks of AI actors contributing models, datasets, and more to the development of AI products and services. AI supply chains have many implications yet are poorly understood. In this work, we take a first step toward a formal study of AI supply chains and their implications, providing two illustrative case studies indicating that both AI development and regulation are complicated in the presence of supply chains. We begin by presenting a brief historical perspective on AI supply chains, discussing how their rise reflects a longstanding shift towards specialization and outsourcing that signals the healthy growth of the AI industry. We then model AI supply chains as directed graphs and demonstrate the power of this abstraction by connecting examples of AI issues to graph properties. Finally, we examine two case studies in detail, providing theoretical and empirical results in both. In the first, we show that information passing (specifically, of explanations) along the AI supply chains is imperfect, which can result in misunderstandings that have real-world implications. In the second, we show that upstream design choices (e.g., by base model providers) have downstream consequences (e.g., on AI products fine-tuned on the base model). Together, our findings motivate further study of AI supply chains and their increasingly salient social, economic, regulatory, and technical implications.

Zili Wang, Frank Montabon, Kristin Yvonne Rozier

Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) corrupted or unreliable in order to hide the nature of the activities. We need to be able to automatically detect new patterns that correlate with such illegal activity over complex, even temporal data, without requiring large training data sets. We explore neurosymbolic methods for identifying instances of illicit activity in supply chains and compare the effectiveness of manual and automated feature extraction from news articles accurately describing illicit activities uncovered by authorities. We propose a question tree approach for querying a large language model (LLM) to identify and quantify the relevance of articles. This enables a systematic evaluation of the differences between human and machine classification of news articles related to forced labor in supply chains.

Sathish Krishna Anumula

In the rapidly evolving landscape of global supply chains, where digital disruptions and sustainability imperatives converge, traditional operational frameworks often struggle to adapt. This paper introduces the Design-Based Supply Chain Operations Research Model, a novel extension of the Design SCOR framework, which embeds operational research techniques to enhance decision-making, resilience, and environmental stewardship. Building on the foundational processes of DSCOR such as Design, Orchestrate, Plan, Order, Source, Transform, Fulfil, and Return DSCORM incorporates predictive analytics, simulation modelling, and optimization algorithms to address contemporary challenges like supply chain volatility and ESG (environmental, social, governance) compliance. Through a comprehensive literature synthesis and methodological approach involving case-based simulations, we explore DSCORM's hierarchical structure, performance metrics, implementation strategies, and digital modernization pathways. Results from simulated scenarios indicate potential efficiency gains of 15to25 percent, reduced carbon footprints by up to 20 percent, and improved agility in dynamic markets. Discussions delve into practical implications for industries like manufacturing and logistics, highlighting barriers such as data integration hurdles and the need for skilled workforces. By humanizing supply chain management emphasizing collaborative, adaptive strategies over rigid automation DSCORM positions itself as a blueprint for sustainable growth. Conclusions underscore its role in advancing digital transformation, with recommendations for future empirical validations in real-world settings

Nusrat Zahan, Yasemin Acar, Michel Cukier et al.

Cyber attacks leveraging or targeting the software supply chain, such as the SolarWinds and the Log4j incidents, affected thousands of businesses and their customers, drawing attention from both industry and government stakeholders. To foster open dialogue, facilitate mutual sharing, and discuss shared challenges encountered by stakeholders in securing their software supply chain, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) organize Secure Supply Chain Summits with stakeholders. This paper summarizes the Industry Secure Supply Chain Summit held on November 16, 2023, which consisted of \panels{} panel discussions with a diverse set of \participants{} practitioners from the industry. The individual panels were framed with open-ended questions and included the topics of Software Bills of Materials (SBOMs), vulnerable dependencies, malicious commits, build and deploy infrastructure, reducing entire classes of vulnerabilities at scale, and supporting a company culture conductive to securing the software supply chain. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain.

Shenao Wang, Yanjie Zhao, Xinyi Hou et al.

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastructure, foundation models, and downstream applications. This paper provides the first comprehensive research agenda of the LLM supply chain, offering a structured approach to identify critical challenges and opportunities through the dual lenses of software engineering (SE) and security & privacy (S\&P). We begin by establishing a clear definition of the LLM supply chain, encompassing its components and dependencies. We then analyze each layer of the supply chain, presenting a vision for robust and secure LLM development, reviewing the current state of practices and technologies, and identifying key challenges and research opportunities. This work aims to bridge the existing research gap in systematically understanding the multifaceted issues within the LLM supply chain, offering valuable insights to guide future efforts in this rapidly evolving domain.

Laurie Williams, Sammy Migues, Jamie Boote et al.

The Proactive Software Supply Chain Risk Management Framework (P SSCRM) described in this document is designed to help you understand and plan a secure software supply chain risk management initiative. P SSCRM was created through a process of understanding and analyzing real world data from nine industry leading software supply chain risk management initiatives as well as through the analysis and unification of ten government and industry documents, frameworks, and standards. Although individual methodologies and standards differ, many initiatives and standards share common ground. P SSCRM describes this common ground and presents a model for understanding, quantifying, and developing a secure software supply chain risk management program and determining where your organization's existing efforts stand when contrasted with other real world software supply chain risk management initiatives.

Yueqi Wang, Yi Zhang, Yongqiang Zhao et al.

YuanzhangXiao, ChaithanyaBandi, Ermin Wei

We study deregulated power markets with strategic power suppliers. In deregulated markets, each supplier submits its supply function (i.e., the amount of electricity it is willing to produce at various prices) to the independent system operator (ISO), who based on the submitted supply functions, dispatches the suppliers to clear the market with minimal total generation cost. If all suppliers reported their true marginal cost functions as supply functions, the market outcome would be efficient (i.e., the total generation cost is minimized). However, when suppliers are strategic and aim to maximize their own profits, the reported supply functions are not necessarily the true marginal cost functions, and the resulting market outcome may be inefficient. The efficiency loss depends crucially on the topology of the underlying transmission network. This paper provides an analytical upper bound of the efficiency loss due to strategic suppliers, and proves that the bound is tight under a large class of transmission networks (i.e., weakly cyclic networks). Our upper bound sheds light on how the efficiency loss depends on the transmission network topology (e.g., the degrees of nodes, the admittances and flow limits of transmission lines).

Piergiorgio Ladisa, Serena Elisa Ponta, Antonino Sabetta et al.

This work discusses open-source software supply chain attacks and proposes a general taxonomy describing how attackers conduct them. We then provide a list of safeguards to mitigate such attacks. We present our tool "Risk Explorer for Software Supply Chains" to explore such information and we discuss its industrial use-cases.

Amr S. Mohamed, Sumin Lee, Deepa Kundur

The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks. Supply chain attacks present a significant threat to power systems, allowing cybercriminals to bypass network defenses and execute deliberate attacks at the physical layer. Given the exponential advancements in machine intelligence, cybercriminals will leverage this technology to create sophisticated and adaptable attacks that can be incorporated into supply chain attacks. We demonstrate the use of reinforcement learning for developing intelligent attacks incorporated into supply chain attacks against generation control devices. We simulate potential disturbances impacting frequency and voltage regulation. The presented method can provide valuable guidance for defending against supply chain attacks.

Hardik Sharma, Rajat Garg, Harshini Sewani et al.

Globalization has introduced many new challenges making Supply chain management (SCM) complex and huge, for which improvement is needed in many industries. The Internet of Things (IoT) has solved many problems by providing security and traceability with a promising solution for supply chain management. SCM is segregated into different processes, each requiring different types of solutions. IoT devices can solve distributed system problems by creating trustful relationships. Since the whole business industry depends on the trust between different supply chain actors, IoT can provide this trust by making the entire ecosystem much more secure, reliable, and traceable. This paper will discuss how IoT technology has solved problems related to SCM in different areas. Supply chains in different industries, from pharmaceuticals to agriculture supply chain, have different issues and require different solutions. We will discuss problems such as security, tracking, traceability, and warehouse issues. All challenges faced by independent industries regarding the supply chain and how the amalgamation of IoT with other technology will be provided with solutions.

Miranda Bihler, Hala Nelson, Erin Okey et al.

We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems.

Soroush Goodarzi, Vahid Kayvanfar, Alireza Haji et al.

Vaccinating the global population against Covid-19 is one of the biggest supply chain management challenges humanity has ever faced. Rapid supply of Covid-19 vaccines is essential for successful global immunization, but its effectiveness depends on a transparent supply chain that can be monitored. In this research, we have proposed an approach based on blockchain technology, which is used to ensure seamless distribution of the Covid-19 vaccine with transparency, data integrity, and full traceability of the supply chain to reduce risk, ensure safety, and immutability. A vaccine supply chain needs to update the status of the vaccine at every stage, and any problem in the supply and distribution path can lead to irreparable damage. Currently, the research conducted on the use of blockchain in supply chains is still in the early stages. In this paper, the use of blockchain technology to monitor the vaccine supply and distribution system will be investigated. A model close to reality of today's vaccine supply chains in developing countries is considered and then a new intelligent system for vaccine monitoring in the vaccine supply chain is designed based on the considered model. Also, smart contracts based on a blockchain network is designed to check consumer vaccination records as well as vaccine circulation from beginning to end. The implementation and design of the vaccine supply chain is done using smart contracts on the Ethereum blockchain network. Additionally, the system has been tested on both local networks, the HardHat suite and Rinkbey's test network. The system has also been developed to work seamlessly when it is using an integrated IoT chip that can automatically update a batch's location, temperature, and other physical conditions periodically.

Melvin Drent, Poulad Moradi, Joachim Arts

We study the inbound supply mode and inventory management decision making for a company that sells an assortment of products. Stochastic demand for each product arrives periodically and unmet demand is backlogged. Each product has two distinct supply modes that may be different suppliers or different transport modes from the same supplier. These supply modes differ in terms of their carbon emissions, speed, and costs. The company needs to decide when to ship how much using which supply mode such that total holding, backlog, and procurement costs are minimized while the emissions associated with different supply modes across the assortment remains below a certain target level. Since the optimal policy for this inventory system is highly complex, we assume that shipment decisions for each product are governed by a dual-index policy. This policy dynamically prescribes shipment quantities with both supply modes based on the on-hand inventory, the backlog, and the products that are still in-transit. We formulate this decision problem as a mixed integer linear program that we solve through Dantzig-wolfe decomposition. We benchmark our decision model against two state-of-the-art approaches in a large test-bed based on real-life carbon emissions data. Relative to our decision model, the first benchmark lacks the flexibility to dynamically ship products with two supply modes while the second benchmark makes supply mode decisions for each product individually rather than holistically for the entire assortment. Our computational experiment shows that our decision model can outperform the first and second benchmark by up to 15 and 40 percent, respectively, for realistic targets for carbon emission reduction.

Abdul Khalique Shaikh A. K. Al-Alawi, L. R., Al-Busaidi et al.

The cybersecurity of modern systems has dramatically increased attention from both industrial and academia perspectives. In the recent era, the popularity of the blockchain-based system has traditionally been emergent among various industrials sectors especially in supply chain management due to its streamlined nature. This reveals the importance of the quality aspects from a supply chain management perspective. Many industries realized the importance of having quality systems for supply chain management and logistics. The emergence of blockchain technology has created several potential innovations in handling and tracking business activities over the supply chain processes as specific. This paper shed the light on the blockchain and specifically on a smart contract technology which been used to handle the process of creation, verification and checking data over the supply chain management process. Then, touch upon the area of blockchain cybersecurity in the supply chain context. More and more, since the smart contract handles the transfer of data over different locations, then the security protection should be strong enough to secure the data and the assets from any attacks. Finally, the paper examines the main security attacks that affect the data on the blockchain and propose a solution

Alla Abdella, Jeffrey K. Brecht, Ismail Uysal

This article describes location aware temperature profiles from six strawberry shipments across the continental United States. Three pallets were instrumented in each shipment with three vertically placed loggers to take a longitudinal and latitudinal snapshot of 9 strategically different locations (including the top, middle and bottom layers of the pallets placed in the back, middle and the front of the shipping container) for a combined 54 measurement points across shipments of varying lengths. The sensors were instrumented in the field, right at the point of harvest, recorded temperatures every every 5 to 10 minutes depending on the shipment, and uploaded their data periodically via cellular radios on each device. The data is a result of significant collaboration between stakeholders from farmers to distributors to retailers to academics, which can play an important role for researchers and educators in food engineering, cold-chain, machine learning, and data mining, as well as in other disciplines related to food and transportation.

Eden Ephraim, Dennis Jewell

Sidra Malik, Volkan Dedeoglu, Salil S. Kanhere et al.

Traceability and integrity are major challenges for the increasingly complex supply chains of today's world. Although blockchain technology has the potential to address these challenges through providing a tamper-proof audit trail of supply chain events and data associated with a product life-cycle, it does not solve the trust problem associated with the data itself. Reputation systems are an effective approach to solve this trust problem. However, current reputation systems are not suited to the blockchain based supply chain applications as they are based on limited observations, they lack granularity and automation, and their overhead has not been explored. In this work, we propose TrustChain, as a three-layered trust management framework which uses a consortium blockchain to track interactions among supply chain participants and to dynamically assign trust and reputation scores based on these interactions. The novelty of TrustChain stems from: (a) the reputation model that evaluates the quality of commodities, and the trustworthiness of entities based on multiple observations of supply chain events, (b) its support for reputation scores that separate between a supply chain participant and products, enabling the assignment of product-specific reputations for the same participant, (c) the use of smart contracts for transparent, efficient, secure, and automated calculation of reputation scores, and (d) its minimal overhead in terms of latency and throughput when compared to a simple blockchain based supply chain model.