Alisha Roushan, Amrit Das, Anirban Dutta, Uttam Kumar Bera
Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters , specifically flash floods triggered by cloudburst events. We propose a multi-objective supply chain model that minimizes both cost and time during emergencies by employing drones for rapid response and delivery to inaccessible areas. The model leverages Dijkstra’s algorithm to identify the shortest emergency routes and integrates Neutrosophic Compromise Programming (NCP) and the Weighted Sum Method (WSM) to optimize drone deployment for cost-effectiveness and timely intervention. Pentagonal Type-2 Fuzzy Variables (PT2FV) manage uncertainty and accurately represent real-world disasters. The study also introduces a smart contract framework to enhance transparency and accountability in logistics and rescue operations. These smart contracts govern the assignment of drone-based delivery tasks, ensuring that supplies are optimally allocated and transported via the most efficient routes. The system verifies task completion and maintains a transparent record of the logistics process . The robustness of the model is validated through sensitivity analysis, while the smart contract system is confirmed through unit testing, demonstrating its reliability under varied conditions. This work aligns with Industry 5.0 , integrating human-centric decision-making, drones, intelligent systems, and blockchain-based smart contracts to automate and effectively manage disaster, facilitating seamless collaboration between humans and machines.
This paper introduces Bank-Guard Fusion System, a decentralized blockchain wallet system designed for secure cryptocurrency management. Utilizing blockchain's decentralized, cryptographic strengths, the platform enables wallet creation, balance checks, and transactions in a user- friendly, secure environment. Built with React.js, Flask, and MongoDB, it provides a seamless interface and decentralized data handling. Integrating Proof of Work (PoW) and Proof of Stake (PoS) for transaction validation, this solution enhances trust, transparency, and security, bridging users and blockchain technology to facilitate sustainable, scalable, and robust digital transactions. Keywords – Blockchain, Cryptocurrency, Flask, MongoDB, React.js, Decentralized Application, Wallet, Secure Transactions, PoW, PoS, XMSS, Hybrid Security
Michael Lustenberger, Florian Spychiger, Lukas Küng, Jens Martignoni
Abstract The development of Bitcoin and its underlying technology blockchain has enabled a new phenomenon called decentralized autonomous organizations (DAOs). DAOs can be perceived as self-governing organizations whose management is based on programmed and encoded rules on a decentralized and distributed peer-to-peer network. These DAOs typically manage and allocate funds, often in the form of cryptocurrencies. However, in recent years, a variety of DAOs have been established to provide services (e.g., currency exchange, project financing), curate collections (e.g., art collections), or own and manage real assets (e.g., land). Currently, DAO literature focuses mainly on online communities managing digital assets; however, DAOs owning physical properties differ from them in localized communities, asset indivisibility, and additional complexity in collective acquisition, ownership, limited physical capacity, and decentralized governance . Such property-owning DAOs are interesting, because they fuel the transition from purely online organizations into organizations integrating with the physical world. From an organizational system theory perspective this article explores how a DAO owning properties could be designed by exploring three DAO projects that own properties. Applying a conceptual research design , we first identify DAO Design Principles obtained by traditional organizational system theory, followed by examining and describing the core organizational principles for property-owning DAOs. Based on a comprehensive discussion of the conceptual findings, we present a research agenda for further studies on DAOs owning properties.
Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff
Using data from a live trading experiment on the Binance Bitcoin perpetual, we examine the effects of (i) basic order book mechanics and (ii) the persistence of price changes from immediate to short timescales, revealing the interplay between returns, queue sizes, and orders' queue positions. We document a fundamental trade-off: a negative correlation between maker fill likelihood and post-fill returns. This dictates that viable maker strategies often require a contrarian approach, counter-trading the prevailing order book imbalance. These dynamics render commonly-cited strategies highly unprofitable, leading us to model `Reversals': situations where a contrarian maker strategy at the touch proves effective.
Digital currencies primarily operate online, but there is growing interest in enabling offline transactions to improve digital inclusion. Existing offline methods struggle with double-spending risks, often limiting transaction amounts. In this work, we propose a quantum-state-based currency system that uses the non-cloning theorem to enable secure, multi-channel transactions without the risk of double spending. We demonstrate this system's implementation with experimental results, including use cases for currency transfers and swaps. To mitigate credit risks in swaps, we also integrate blockchain to show its wide applicability. Our approach paves the way for quantum-secure digital currencies and opens new possibilities for optimizing multi-channel tokens.
With its decentralization and immutability, blockchain has emerged as a trusted foundation for data management and querying. Because blockchain storage space is limited, large multimodal data files, such as videos, are often stored offline, leaving only lightweight metadata on the chain. While this hybrid storage approach enhances storage efficiency, it introduces significant challenges for executing advanced queries on multimodal data. The metadata stored on-chain is often minimal and may not include all the attributes necessary for queries like time range or fuzzy queries. In addition, existing blockchains do not provide native support for multimodal data querying. Achieving this capability would necessitate extensive modifications to the underlying blockchain framework, even reconstructing its core architecture. Consequently, enabling blockchains with multimodal query capabilities remains a significant problem, which necessitates overcoming the following three key challenges: (1) Designing efficient indexing methods to adapt to varying workloads that involve frequent insertions and query operations; (2) Achieving seamless integration with existing blockchains without altering the underlying infrastructure; (3) Ensuring high query performance while minimizing gas consumption. To address these challenges, we propose MulChain, a novel middleware architecture to enable smooth integration with existing blockchains. At the core of MulChain is the BHashTree, a flexible data structure that dynamically switches between tree and hash nodes based on workload characteristics, ensuring efficient insertion and query operations. Furthermore, the middleware provides standardized interfaces for blockchain systems, unifying query methods across different platforms.
The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continue to increase. In my previous work arXiv:2502.13722, I introduced a novel deep learning approach that demonstrated significant improvements over traditional VWAP execution methods by directly optimizing the execution problem rather than relying on volume curve predictions. However, that model was static because it employed the fully linear approach described in arXiv:2410.21448, which is not designed for dynamic adjustment. This paper extends that foundation by developing a dynamic neural VWAP framework that adapts to evolving market conditions in real time. We introduce two key innovations: first, the integration of recurrent neural networks to capture complex temporal dependencies in market dynamics, and second, a sophisticated dynamic adjustment mechanism that continuously optimizes execution decisions based on market feedback. The empirical analysis, conducted across five major cryptocurrency markets, demonstrates that this dynamic approach achieves substantial improvements over both traditional methods and our previous static implementation, with execution performance gains of 10 to 15% in liquid markets and consistent outperformance across varying conditions. These results suggest that adaptive neural architectures can effectively address the challenges of modern VWAP execution while maintaining computational efficiency suitable for practical deployment.
As robotic systems (RS) become more autonomous, they are becoming increasingly used in small spaces and offices to automate tasks such as cleaning, infrastructure maintenance, or resource management. In this paper, we propose iTrash, an intelligent trashcan that aims to improve recycling rates in small office spaces. For that, we ran a 5 day experiment and found that iTrash can produce an efficiency increase of more than 30% compared to traditional trashcans. The findings derived from this work, point to the fact that using iTrash not only increase recyclying rates, but also provides valuable data such as users behaviour or bin usage patterns, which cannot be taken from a normal trashcan. This information can be used to predict and optimize some tasks in these spaces. Finally, we explored the potential of using blockchain technology to create economic incentives for recycling, following a Save-as-you-Throw (SAYT) model.
Abstract As machine learning technologies advance rapidly across various domains, concerns over data privacy and model security have grown significantly. These challenges are particularly pronounced when models are trained and deployed on cloud platforms or third-party servers due to the computational resource limitations of users’ end devices. In response, zero-knowledge proof (ZKP) technology has emerged as a promising solution, enabling effective validation of model performance and authenticity in both training and inference processes without disclosing sensitive data. Thus, ZKP ensures the verifiability and security of machine learning models, making it a valuable tool for privacy-preserving AI. Although some research has explored the verifiable machine learning solutions that exploit ZKP, a comprehensive survey and summary of these efforts remains absent. This survey paper aims to bridge this gap by reviewing and analyzing all the existing Zero-Knowledge Machine Learning (ZKML) research from June 2017 to August 2025. We begin by introducing the concept of ZKML and outlining its ZKP algorithmic setups under three key categories: verifiable training, verifiable inference, and verifiable testing. Next, we provide a comprehensive categorization of existing ZKML research within these categories and analyze the works in detail. Furthermore, we explore the implementation challenges faced in this field and discuss the improvement works to address these obstacles. Additionally, we highlight several commercial applications of ZKML technology. Finally, we propose promising directions for future advancements in this domain.
The rapid increase in distributed photovoltaic (PV) generation worldwide demands more efficient large-scale modeling methods. This study investigates an automatic modeling technique for low-voltage distributed PV network topology based on ledger information. By analyzing PV system ledger data, we construct and automatically generate the topology model using automated algorithms. Our data-driven method and complex network theory-based algorithm improve system resilience and operational efficiency. Experimental results validate the effectiveness of our approach. Future research will focus on optimizing these algorithms and evaluating their applicability across various scenarios to support optimal PV system regulation and control.
Oshoke Samson Igonor, Muhammad Bilal Amin, Saurabh K. Garg†
Blockchain technology has risen in recent years from its initial application in finance to gain prominence across diverse sectors, including digital forensics. The possible application of blockchain technology to digital forensics is now becoming increasingly explored with many researchers now looking into the unique inherent properties that blockchain possesses to address the inherent challenges in this sector such as evidence tampering, the lack of transparency, and inadmissibility in court. Despite the increasing interest in integrating blockchain technology into the field of digital forensics and its domains, no systematic literature review currently exists to provide a holistic perspective on this integration. It is a challenge to find a comprehensive resource that examines how blockchain is being applied to enhance the digital forensics process. This paper provides a systematic literature review to explore the application of blockchain technology in digital forensics, focusing on its potential to address these challenges and enhance forensic methodologies. Through a rigorous review process, this paper examines selected studies to identify diverse frameworks, methodologies, and blockchain-driven enhancements applied to digital forensic investigations. The discussion highlights how blockchain properties such as immutability, transparency, and automation have been leveraged to improve evidence management and forensic workflows. Furthermore, this paper explores the common applications of blockchain-based forensic solutions across various domains and phases while addressing the associated limitations and challenges. Open issues and future research directions, including unexplored domains and operational gaps, are also discussed. This study provides valuable insights for researchers, investigators, and policymakers by offering a comprehensive overview of the state of the art in blockchain-based digital forensics, summarizing key contributions and limitations, and identifying pathways for advancing the field.
This paper aims to emphasize the significance of creating subnational nutrition action plans in regions with high variation in nutrition challenges and evaluates their projected return on investment in Kenya. Despite steady progress, undernutrition in Kenya remains high, costing the country an estimated US$ 4.2 billion or 7% of its GDP annually. Under Kenya's decentralized government system, numerous counties developed sectoral County Nutrition Action Plans (CNAPs) in 2018 to identify and prioritize essential nutrition actions to target undernutrition at the subnational level. In this paper, the authors present findings from county investment cases (CICs) in five counties - Nandi, Busia, Makueni, Vihiga, and Elgeyo Marakwet-including the costs, health impacts, and benefit to cost ratios of implementing high-impact nutrition interventions. Data was collected on the target coverage and cost of interventions prioritized in each county's CNAPs for the 2018 to 2022 period. A monetized DALY approach, using the value of a statistical life methodology was used for cost-benefit analysis and the Optima Nutrition tool was used for cost-effectiveness analysis. The estimated cumulative impact of the five CNAPs was projected as 1,800 child and 115 maternal deaths averted; preventing and treating 19,000 cases of stunting and 4,700 cases of wasting in children under five and averting 67,000 cases of anaemia in pregnant women and adolescent girls. The county-level benefit-cost ratios range from $5:1 to $14:1 (at a default 3% discount rate). This analysis demonstrates that localized subnational plans can be advantageous for policymaking and prioritization to better address subnational disparities in undernutrition and offer a high return on investment.
Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptos’ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and people’s attitudes toward them. Based on finance theory, these differing characteristics could affect cryptos’ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.
Independent Researcher, San Francisco, CA, USA, Sahaj Tushar Gandhi
Smart contracts, which allow for decentralized, automated transactions on blockchains, have been the source of repeated financial loss from hacking and coding flaws. This article introduces an AI-based deep learning approach to automated detection of vulnerabilities in smart contracts on Ethereum. The architecture integrates code- token embeddings (CodeBERT-style), control- and data-flow graph representations, and a hierarchical graph neural network (HGNN) with attention-based multimodal fusion to allow for comprehensive understanding of human-written programs. We train on labelled datasets from real-world contracts, utilising data augmentation and addressing class imbalance (focal loss + over sampling). For the experimental study, we compare the performance of our framework with existing solely-static and sequence-based transformers approaches apart from other GNN models on public datasets; ScrawlD, SmartBugs and manually curated Github-derived samples. Results The fused HGNN model performs with an average F1-score of 0.91, precision of 0.89, recall of 0.93 and AUC of 0.95 better than transformer- only (F1 = 0.86) and static-tool baselines (F1 = 0.71). The method shows strong generality to a wide range of vulnerability forms (reentrancy, integer overflow, unchecked calls, access control bugs) and enhances the precision for function-level localization. We further develop an interpretation module to map attention weights back to AST/CFG regions for human auditors. The paper also addresses limitations on dataset bias, obfuscation-resilience and adversarial examples and provides ideas for further investigation such as few-shot adaptation with one-class VAEs, integration with continuous deployment pipelines. The contributions: a multimodal deep-learning model for vulnerability detection and localization, an empirical study on state-of-the-art performance in multiple benchmark projects with large amounts of code; and advice how to deploy the AI-assisted contract auditing in development workflows.
• Matching trading periods and investment horizons between equities and cryptocurrencies are fundamentally challenging. • Monday returns and intermarket connectedness of cryptocurrencies notably differ when alternative benchmark (closing) prices are used. • Using inconsistent return estimation methods from different sources delivers spurious intermarket connectedness results. • THETA, GNO, GLM, ENJ, WAXP, KCS, and WAVES are most vulnerable to the return estimation method. • Seemingly inconsequential choices critically affect the main conclusions drawn by the existing studies on market interconnectedness. Cryptocurrencies trade continuously, unlike traditional assets limited to weekdays, creating challenges in calculating Monday returns. This paper investigates the impact of four benchmark closing prices—Friday, Saturday, Sunday, and a weekend average—on intermarket connectedness. Analyzing 72 cryptocurrencies (2018–2024) and their relation to the S&P500 using the TVP-VAR model, we find significant variations in economic and statistical outcomes, influencing both the magnitude and direction of spillovers. Mixed log- and non-log-based return methods yield inconsistent results for specific cryptocurrencies like THETA, GNO, GLM, and WAVES. These findings highlight the critical importance of consistent return methodologies in cryptocurrency market analysis.
With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.
This paper explores the integration of blockchain technology and smart contracts in the development of nextgeneration digital identity solutions. As the demand for secure, privacy-preserving, and user-centric identity management systems increases, blockchain and smart contracts offer a promising framework that enhances transparency, automation, and user control. We outline the methodology employed to assess the effectiveness of blockchain and smart contracts in digital identity management, focusing on aspects such as security, interoperability, and user empowerment. Through comprehensive data analysis, we present the results of our study, demonstrating the potential benefits and challenges associated with implementing blockchain-based identity systems augmented by smart contracts. Our findings contribute to the ongoing discourse on digital identity and provide insights for future research and practical applications.
NGOs can foster a more inclusive and effective civil society in the Global South by collaborating and amplifying marginalized voices. Nongovernmental organizations (NGOs) must prioritize establishing robust relationships with local communities and advocating for their needs to achieve a favorable outcome. As Sandri (2017) stated in his article, "Humanitarian aid to refugees in the Jungle refugee camp of Calais necessitates a diverse array of resources," including housing, food, healthcare, and education. This justifies the responsibility for humanitarian assistance. A later study estimates that around 117.2 million people will be displaced worldwide by 2024, mainly in countries at risk from climate change (UNHCR, 2023).As a result, the refugees' grievances are increasingly complex and unmanageable. Consequently, a thorough policy to allocate and maintain a just global responsibility will come into effect. By enabling peer-to-peer crowdfunding and ensuring secure digital storage of identity documents, blockchain technology can present creative options to assist marginalized individuals. However, it is essential to assess the ethical and logistical consequences of these technological advancements and protect the privacy and rights of refugees. Using blockchain technology, a secure digital identification system can be created that helps refugees overcome common Frontiers in Human Dynamics https://www.frontiersin.org/journals/human-dynamics identification obstacles. However, the blockchain system may need to be more effective for refugees in regions with limited access to technology or reliable internet connectivity, as this solution may need to be more practical for them. Also, certain refugees may need the necessary knowledge or resources to navigate and employ a sophisticated digital identification system efficiently. For instance, the absence of internet infrastructure may present a challenge for a refugee residing in a remote camp to access the blockchain system, impeding their ability to verify their identity and access essential services. Mballa et al. (2020) found that refugees residing in urban areas with improved connectivity and resources may find the blockchain system more accessible and advantageous for establishing secure identification. As he stated in his article, this system may encompass critical documents, such as diplomas, health records, and birth certificates, to facilitate the reconstruction of refugees' lives. Although the integration of important documents may be beneficial, it does not address the fundamental problem of internet connectivity and access in remote regions, which remains a significant obstacle to the implementation of a sophisticated digital identification system such as blockchain, as other scholars such as Ager and Stronger (2008) have noted. Problems with internet access might slow down the rollout of blockchain-based identification systems in more remote places. The research primarily centers on the traditional approach to refugee management while shedding light on future developments in refugee management systems, particularly regarding providing aid and assistance. It's important to consider that individuals in these areas may require assistance using such systems as they may have limited access to reliable connectivity.Moreover, relying heavily on online verification procedures can disproportionately affect marginalized communities with limited access to reliable internet services.Additionally, concerns about privacy and security may arise from storing sensitive personal information on a blockchain platform. Despite these challenges, blockchain-based identification systems have the potential to mitigate identity theft, streamline bureaucratic processes, and provide stability and security for displaced individuals. It is imperative to prioritize the needs of countries in the Global South and ensure they have the necessary resources to help those in need cope with the increasing number of refugees (Valenti, 2022). It is imperative to address the root causes of displacement and strive for sustainable solutions.According to Habib et al. (2023), a decentralized strategy focusing on openness and accessibility is a possible solution to the difficulties of sharing resources. This approach encourages stakeholder collaboration and strengthens the sense of community ownership of resources. Due to its immutability and security, blockchain technology can efficiently manage and distribute resources to refugees, as Corte-Real et al. (2022) found. By creating an immutable registry of funds allocated to refugees, aid can be distributed more efficiently and reliably. At the same time, direct peer-to-peer transactions minimize the need for intermediaries and reduce the risk of corruption and fraud (Nwuluu & Damisa, 2023). 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У сучасному світі корпоративне управління стикається з низкою викликів, пов’язаних із централізацією влади, непрозорістю прийняття рішень, неефективним розподілом ресурсів та корупційними ризиками. Традиційні організації значною мірою покладаються на людський фактор, що може призводити до затримок, конфлікту інтер- есів та зайвих витрат. У цьому контексті децентралізовані автономні організації (ДАО) пропонують альтер- нативний підхід, використовуючи смарт-контракти та блокчейн для автоматизованого й прозорого управління.Одна з основних переваг ДАО полягає в тому, що вони усувають необхідність у посередниках та централізованих структурах, замінюючи їх алгоритмічними механізмами голосування та фінансового управління. Такий підхід дозволяє кожному учаснику організації мати реальний вплив на процеси та гарантує чесний розподіл ресурсів відповідно до заздалегідь визначених правил у коді смарт-контрактів. В умовах цифровізації такі механізми є дуже важливими, оскільки вони забезпечують довіру між учасниками незалежно від їхнього мислення чи статусу та прозорість виконання всіх процесів. Це досягається завдяки тому, що всі транзакції, голосування та рішення зберігаються у блокчейні, де їх не можна змінити або приховати. Такий підхід управління значно підвищує рівень довіри з боку спільноти. Він також вирішує проблему централізованого управління, коли ключові рішення ухвалюються обмеженим колом осіб, що може призводити до недосконалих стратегій розвитку або конфліктів.Уряди багатьох країн, зокрема США та держав Європейського Союзу, активно досліджують способи інтеграції ДАО у чинні правові системи, що свідчить про зростаючу зацікавленість у використанні таких структур для реального бізнесу. Незважаючи на певні виклики у сфері юридичного визначення, цей напрямок продовжує активно розвиватися, і вже зараз є приклади успішних проектів, що працюють на основі децентралізованих механізмів управління.Розробка децентралізованих автономних організацій у корпоративному управлінні є важливим і перспективним напрямком, що дозволяє значно підвищити ефективність бізнес-процесів, забезпечити чесність та прозорість у прийнятті рішень, а також адаптувати компанії до нових умов цифрової економіки. З огляду на стрімке зростання популярності Web3-технологій, DeFi та інших децентралізованих систем, можна впевнено стверджувати, що децентралізовані автономні організації стануть невід’ємною частиною майбутнього корпоративного управління.
Niels Agatz, Jan C. Fransoo, Elliot Rabinovich, Rui Sousa
Last mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, have widespread relevance across major sectors of the economy, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for a significant portion of the costs, jobs, and economic output in these sectors. Global economic output involving last mile deliveries alone, for instance, is valued at $165 billion per year and is growing at about 10% per year (InsightAce Analytic 2024). Recent decades have witnessed an acceleration in the rate of evolution of LMO (Agatz et al. 2024; Boutilier and Chan 2022; Boyer and Hult 2005; Dreischerf and Buijs 2022; He and Goh 2022; Lyu and Teo 2022). Technology-driven innovations have catalyzed profound changes in the planning, design, and execution of LMO, with significant implications for the economics of these operations. Extending the last mile to the final user has increased convenience, accessibility, and reliability. Zipline, for example, has introduced drones to safely deliver lifesaving products in remote communities (Ackerman and Koziol 2019). An increasing number of pharmacies in Europe and Africa have been equipped with smart lockers to allow 24/7 access to critical medicines (Gobir et al. 2024). Some innovations leveraging platforms based on smartphone apps have given small corner stores in neighborhoods in cities across Latin America the means to sell and deliver daily groceries and other household staples to local residents (Escamilla et al. 2021). Other innovations, leveraging artificial intelligence, have found applications in vehicle routing tools and warehouse and fulfillment automation (such as Ocado's system (Mason 2019)), track-and-trace systems that provide real-time communications and visibility into delivery processes (such as Instacart and Uber Eats), anticipatory shipping algorithms to move inventories to specific areas ahead of realized demand (Chen and Graves 2021), and integration tools with third-party services (successfully deployed by ClickPost and ShipEngine). However, considerable challenges remain. For example, because of short time frames and high delivery volumes to many dispersed locations, LMO have little room for human error. Yet, since many firms tend to tap into low-skilled, temporary, or crowdsourced labor to provide these services, there is high variability in performance and worker availability. LMO are also expensive, due in part to rising labor costs, delivery failures, more demanding customers, and vehicle and parking restrictions. Although academic research in LMO has a long tradition in Operations Research (see e.g., Agatz et al. (2011), Otto et al. (2018), Boysen et al. (2019) and Reed et al. (2022)), LMO have barely been considered as an operations problem that requires process understanding and management within a sociotechnical system. The need for this is apparent, as increasing evidence points to managerial, economic, and sociotechnical challenges as major determinants of LMO success. Delivery workers have been noted to largely ignore the recommendations by routing algorithms in urban settings (Liu et al. (2023)); working conditions are an increasing societal and corporate concern; and customer experiences are less than satisfactory in many cases. Further, LMO are associated with negative externalities such as emissions, traffic congestion, and the abuse of public parking space. Operational costs are also very high—often up to a point where LMO are loss-making, such as in grocery home delivery. And, while there have been extensive technological innovations, many seem to fail in scaling at large, which could potentially be due to a poor understanding of the LMO from a process perspective. We need new research to better understand these challenges, as well as to propose new operational practices and business models based on the application of recent innovations. Such research requires a broadening of the phenomenological and theoretical scope of LMO research beyond traditional work in Operations Research. Theories on innovation applied to Operations Management can offer a valuable foundation to study research questions surrounding the scalability of technologies to support new business models in the last mile (Arthur 1994). Similarly, theoretical models examining technology, productivity, and employment can provide a foundation to understand how innovations can change the nature of work in last-mile settings (Autor et al. 2003; Autor 2015). Additional opportunities also exist to use transaction and information cost theories to understand how technological innovations may change organizational boundaries and the nature of organizations in the last mile (Afuah 2003). This confluence of innovations in the field, the multidimensional phenomena that determine performance, and the perspectives from theories from the operations management field provide an opportunity to shape a research program in LMO that will benefit from the Operations Management academic community. This was one of the main goals of our call for papers for the special issue on “Innovations, Technologies, and the Economics of Last-Mile Operations.” Another objective of this special issue was to formalize a research agenda and offer future directions for research to advance our understanding of LMO. To that end, in Section 2, we delve deeper into these operations, their functionalities, distinctive features, and challenges in the context of Operations Management. Then, in Section 3, we expand on research opportunities to tackle the most pressing challenges in LMO and identify knowledge gaps in Operations Management to be addressed in this endeavor. We close in Section 4 with conclusions, recommendations, and potential initiatives to build on the momentum created so far and further advance LMO as a knowledge area within Operations Management. In doing so, we introduce the several papers in the special issue as exemplars of research that can be done in the LMO domain. LMO are made of processes triggered by an agent (e.g., consumer, user, patient, worker, organization) that enable the provision of a service to this agent at the agent's selected location and time (or time period). LMO involve interactions with the agent—who participates in the process and co-creates value—and, by definition, comprise different service processes (Sampson and Froehle 2006). These processes are triggered by an agent's request for service and include the preparation and movement of goods and/or tangible resources (people, equipment) required for providing the service to the agent's selected location at the agent's selected time. A key trait of LMO is the fact that agents select the location and time of the provision of the service and that the provision of the service requires at least in part co-location with the agent. We submit that LMO can be classified into two main categories that differ significantly in the nature and extent of the associated customer co-creation activities (Sampson and Froehle 2006): goods-focused and agent-focused. Goods-focused LMO entails the provision of agent access to goods at a selected location and time, involving the preparation and movement of goods (e.g., groceries, meals) and resources (e.g., delivery vans, delivery people) to that location. A typical example would be e-commerce deliveries to consumer homes. Agent inputs are limited, primarily including information about the required goods (product selection and quantities) and delivery (time and location), as well as engaging in minor interactions with the provider during goods reception. The core value added is the movement of the goods to the agent's selected location and time. Goods-focused LMO correspond to “delivery services” and have received most research attention. Agent-focused LMO entail the provision of more general services to an agent at a selected location and time, involving the preparation and movement of service provision resources (e.g., people, equipment, inventory) to that location. A typical example would be performing repairs of equipment owned by the agent at its selected location, involving the movement of technicians, tools, and inventory (spare parts) to the agent's location. Another example would be an emergency ambulance service, which involves the movement of equipment (vehicle, medical instruments), medical staff, and inventory (medical supplies) to the agent's location. Agent inputs are substantial, including information about the required service, service delivery time and location, and agent's resources, as well as engaging in relevant service co-creation activities at the agent's location. The core value added is the transformation of the agent's inputs (e.g., agent-owned equipment, the agent self). Typically, the level of customization and agent co-creation increases from goods-focused to service-focused LMOs, while the transaction volumes decrease. LMO processes are characterized by a set of distinctive features that raise unique challenges for the management of operations. Based on our conceptualization of LMO and extant literature, we summarize LMO's distinctive features and associated challenges in Table 1. The remainder of the editorial will discuss LMO against this framework and address in more detail several of the distinctive features and challenges. The distinctiveness of LMO processes, their pervasiveness and widespread economic relevance, and the managerial challenges that remain unaddressed jointly motivate the development of a specific research program for LMO within the field of Operations Management. Need to cover very diverse geographical areas, with specific challenges: Reliance on a large number of independent resources (including subcontractors, crowdsourced labor, inventory, contracted or rented equipment, third-party platforms) has the following implications: The features and challenges presented in Section 2 provide a framework for the development of new LMO research that can broaden the scope of LMO subject knowledge, as well as strengthen the theoretical foundations supporting LMO research. This framework also serves as a reference for new research to inform about new technologies and business models in LMO and their implementation and execution. The remainder of this section expands on these research directions. Research on LMO has concentrated on goods-focused LMO, in particular the delivery of goods from a transportation hub or inventory location to an end consumer. 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The emergence of Bitcoin as a pioneering cryptocurrency has transformed financial markets, garnering widespread interest from academicians, policymakers, and investors. The market's inherent volatility and the rapid integration of public information into price movements continue to present a formidable challenge in accurately forecasting Bitcoin prices despite its potential. The limitations of conventional financial models, which frequently need to consider the distinctive attributes of cryptocurrencies, further exacerbate this challenge. Despite the proliferation of ML in various fields, existing models have not fully harnessed these techniques, performing only marginally better than random guesses due to the unique challenges posed by the high volatility and complex dynamics of cryptocurrency markets. This study introduces a systematic review of ML methods specifically tailored for Bitcoin price prediction, with a focus on evaluating the robustness, accuracy, and appropriateness of advanced ML techniques like Long Short-Term Memory (LSTM) networks. The novelty lies in its comprehensive assessment of these methods in the context of data-driven marketing, aiming to enhance both academic understanding and practical applications in financial technology. The previous studies haven't Machine Learning (ML) has become a formidable instrument that has the potential to improve the accuracy of forecasting; however, there still needs to be more comprehension regarding the most effective ML models in this field. The study's importance is derived from its systematic examination of various machine learning (ML) techniques employed to predict the price of Bitcoin, with a particular emphasis on their integration into data-driven marketing strategies. The results will substantially contribute to both academic research and practical applications, providing valuable insights that can be used to develop more dependable forecasting tools, thereby benefiting investors, marketers, and policymakers.
In traditional stock market, the global processing framework shares the data to various mediators like financial and government institutions. The institutional firms needs to handle with large number of data in the system and cooperates with others to provide the trades on the stock exchange platform, and consistently buy-sell orders pass through various parties before settlement. It involves a complex chain of intermediaries, has several drawbacks like weak transparency, long lead times for financial settlements, and a single point of failure. Blockchain (BC) computer node network securely shares the common ledger without intermediaries. This paper uses a deep learning-based Smart contract in the private ethereum consortium blockchain (PEC-BC) to provide financial security. First, the data is collected. Then it is given to the next stage. The Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA) is used to choose the leader block. Further, the selected new leader block is used in the Adaptive Deep Temporal Context Networks (ADTCN) with a consensus algorithm to make secured smart contracts. Here, the parameters are optimized by DB-BOA. The developed ADTCN-based financial security system was compared with other conventional methods, and algorithms performed well.
Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL's role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning (RL) to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.