Search Results (30)

Craniopharyngiomas (CPs) are rare intracranial tumors arising from remnants of Rathke’s pouch. Despite their benign histology, CPs present considerable clinical challenges due to their tendency to exert mass effect and adherence to critical neurovascular structures. There remains no clear consensus on the most effective management of CPs. We explore the latest developments in targeted treatment approaches, examining how innovations in surgery, radiation therapy, and emerging therapies are improving outcomes and management for these challenging tumors. In addition to providing our experience, we reviewed previously reported case series and reviews relevant to CPs. Studies show a bimodal age distribution for CP diagnosis, with peak incidence occurring in children aged 5–14 years and in adults aged 50–74 years. Surgical resection is the typical initial treatment for CPs, and adjuncts, including radiation therapy and intracavitary treatments, have been proven effective for tumor control. Additionally, genetic mutations associated with CPs offer an opportunity for novel strategies that address the underlying molecular mechanisms driving tumor growth through targeting the Wnt/β-catenin and/or MAPK/ERK pathways to disrupt the aberrant signaling that promotes tumor proliferation and survival. Survival rates for CPs are generally favorable (five-year survival of 80%), with recent studies showing improved outcomes and higher survival rates in children. CPs remain rare and challenging tumors. Although surgical resection is the main treatment, surgeons must weigh the benefits of achieving a gross total resection with the risks of iatrogenic injury. Adjuncts, including intracavitary and radiation treatments, may assist with subtotal resections and recurrences, as well as approved BRAF inhibitor therapy for the papillary variant. Many improvements in diagnostic and therapeutic methods were made after Dr. Cushing coined the term “craniopharyngioma”. Ongoing experiments, investigations, and advances in radiation techniques and molecular targeted therapies will provide patients with promise for safer and more effective treatments. Full article

This study examined the association between corporate cryptocurrency activities and tax avoidance outcomes, utilizing data from US public firms covering the period from 2015 to 2023. Financial data were sourced from Compustat, while details regarding cryptocurrency activities were manually extracted from 10-K and 10-Q filings. Our analysis employed a fixed-effects regression model to examine the impact of these activities on cash effective tax rates (ETR). The findings indicate that firms engaged in cryptocurrency activities tend to have a lower ETR compared with those without such involvement. Notably, this effect was predominantly observed in companies directly engaged in cryptocurrency activities, such as accepting cryptocurrency as a payment method or actively trading cryptocurrency on an exchange platform. In contrast, firms involved in crypto mining or initial coin offerings did not exhibit a similar association. Our findings offer significant regulatory insights for governance bodies concerned with the implications of corporate cryptocurrency activities on tax strategies. Full article

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for “GWO embedding IS”), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs’ positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér–Rao lower bound (CRLB), even at low SNR levels. Full article

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models. Full article

This paper offers a game-theoretic model of a firm that raises funds for financing an innovative business project and chooses between ICO (initial coin offering) and equity financing. The model is based on information problems associated with both ICO and equity financing well-documented in the literature. Several new features are introduced, for example, information complexity, which is analyzed along with a more traditional imperfect information and an asymmetric information approach. The model provides several implications that have not yet been tested. For example, we find that the message complexity can be beneficial for firms conducting ICOs. Also, high-quality projects can use ICO as a signal of quality. Thirdly, the average size of projects undertaking equity financing is larger than that of firms conducting ICO. Full article

Rice is one of the major food crops across the globe, and its quality and safety highly influence human health. It is the basis of many different products, including rice flour, rice bread, noodles, rice vinegar, and others. Therefore, the rice supply chain has garnered increasing attention due to the high demand for food safety. Furthermore, malpractices in the rice supply chain can impact farmers by generating low revenues despite their great efforts in rice cultivation. In addition, they would cause governments to suffer significant economic losses due to the high cost of importing rice crops from other countries during the off-season. These issues derive from the lack of reliability, trust, transparency, traceability, and security in the rice supply chain. In this research, we propose a secure, trusted, reliable, and transparent framework based on a Blockchain for rice crop supply chain’s traceability from farm to fork. A new crypto token, the Rice Coin (RC), is introduced to keep a record of all transactions between the stakeholders of the rice supply chain. Moreover, the proposed framework includes an economic model and a crypto wallet and introduces an Initial Coin Offering (ICO) for the RC. Based on smart contracts, a transaction processing system was developed for the transparency and traceability of rice crops, including the conversion of the RC to fiat currency. Furthermore, the InterPlanetary File System (IPFS) is proposed in this research to store encrypted data of companies, retailers, and farmers, so to increase data security, transparency, and availability. In the end, the experimental results showed a better performance of the proposed framework compared to already available supply chain solutions in terms of transaction verification time, transaction average gas cost, and new block latency. Full article

This study aims to examine the potential of blockchain technology in the financing and financial management of entrepreneurial tourism projects. It highlights two objectives: how the technology can be used as an alternative financing tool and how it can improve efficiency and transparency in the financial management of tourism companies. This study shows that initial coin offerings are an effective way to finance innovative tourism projects and that blockchain technology can improve the competitiveness and efficiency of tourism companies. Due to the lack of empirical data on the actual implementation and impact of blockchain technology in the tourism industry, it is suggested that further research is needed to examine the practical application of blockchain technology in the tourism industry, its potential impact on tourism businesses and its implications for the regulatory framework. The proposed methodology includes a systematic literature review on the application of blockchain technology for the financing of tourism projects and the financial improvement of tourism business models. The results indicate that blockchain technology has the potential to transform the financing and financial management of the tourism industry and improve its efficiency and transparency. Furthermore, combining blockchain with other technologies can provide additional benefits in supply chain management and event automation. Full article

The digitalization of the economy provokes the rethinking of manufacturing processes. Despite numerous publications related to Industry 4.0 as a manufacturing approach, the production of fully digital and crypto-asset products was poorly researched. Besides having a supplementary role, crypto-assets may form an entire smart city product. The authors assess the manufacturing of smart city products, fully or partially formed by crypto-assets. The initial issuance of the crypto assets was usually addressed as an Initial Coin Offer, or through the process of increasing the issuer’s capital. The authors assess the Initial Coin Offer, and address it, like manufacturing to produce products for sale. The authors classify all milestones related to the crypto-assets’ issuance, distribution, and revaluation, and assign incomes and expenses to each milestone. Additionally, the ICO-based production costs and revenues were classified according to crypto-asset types, as defined by European Economic Area legislative acts. Full article

Analogous to traditional Initial Public Offerings (IPO), Initial Coin Offerings (ICOs) represent an emerging channel through which firms can access external funding using the new evolving digital financial market for tokens. However, while ICOs represent an alternative funding channel for startups, the ICO market is essentially unregulated, which creates opportunities for fraud such as ‘ICO scams’. This paper addresses the question as to what the expected losses attributable to scams in the market for ICOs are. Using web scrapping techniques, all ICOs launched between August 2014 and December 2019 were first screened for accusations of fraud, before a novel methodological framework was employed to understand the true costs associated with scams. The findings reveal that 56.80% of ICOs were subject to scams, corresponding to 65.80% of the relevant market capitalization, estimated at USD 15.38 billion. Moreover, it is found that the loss distribution due to scam ICOs is governed by a fractal process. Specifically, the power law exponent for the distribution governing losses due to scam ICOs suggests that the second moment is not defined, rendering the sample mean unstable. Taken together, the results in this paper provide evidence that we have not yet seen the largest loss in the market for ICOs and are supportive of an urgent need for ICO market regulations from governments and regulatory agencies. Full article

The landscape of ICOs and its underlying Blockchain technology needs more clarity, given that several overlapping and opposing views exist from governmental institutions, institutional investors, economists, and academia. Those positions stem from confusion, bias, and vested interest. Having consensus from the pioneer entrepreneurs who define Blockchain technology usage, and its marketplace address this need. Furthermore, an agreement on the problems blockchain is solving from the industry perspective would further the understanding of the technology direction and its “raison d’être.” or “reason of existence”. The paper analyzes 4367 businesses that requested funding using ICO whitepapers and raising more than $20 billion US dollars during the most active ICO period. Using Latent Semantic Analyses (LSA), the paper identifies a one-factor solution that explains 98.15% of all ICOs. The paper conducts a second-order analysis that generates an 18-factor solution. Through the empirical analysis, the paper presents its findings as an ICO marketplace stacked layer model. The model is comprised of four layers: (1) Trust; (2) Value exchange; (3) Automation; and (4) Applications to enable value exchange, and an era of new business models. The paper then presents an unbiased, unified entrepreneurial definition of the Blockchain technology usage. Full article

This paper analyzes a financing problem for an innovative firm that is considering launching a web-based platform. The model developed in the paper is the first one that analyzes an entrepreneur’s choice between initial exchange offering (IEO) and initial coin offering (ICO). Compared to ICO, under IEO the firm is subject to screening by an exchange that reduces the risk of investment in tokens; also the firm receives access to a larger set of potential investors; finally tokens become listed on an exchange faster. The paper argues that IEO is a better option for the firm if: (1) the investment size is relatively large; (2) the extent of moral hazard problems faced by the firm is relatively large; (3) the degree of investors’ impatience is relatively small. Furthermore, a non-linear relationship between firm quality and its financing choice is found. Most of these predictions are new and have not been tested so far. Full article

Entrepreneurial, innovative and small- and medium-sized firms experience difficulties with raising funds using traditional debt and equity. Consequently, they are constantly looking for new strategies of financing. The latest inventions are crowdfunding and token issues. In contrast to traditional ways of raising funds these innovations: (1) use modern technology (online transactions, blockchain, etc.) much more actively; (2) are usually quicker in reaching potential investors/funders; (3) use more active network benefits such as, for example, a large number of interactions between investors/funders and between funders and firms. These changes are so significant that some experts list them among the top business inventions of the 21st century. This article provides a review of the growing number of theoretical papers in the areas of crowdfunding and token issues, compares their findings with empirical evidence and discusses directions for future research. The research shows that a large gap exists between the theoretical literature and empirical literature. Full article

Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver. Full article

This paper introduces a machine learning approach to quantify altruism from the linguistic style of textual documents. We apply our method to a central question in (social) entrepreneurship: How does altruism impact entrepreneurial success? Specifically, we examine the effects of altruism on crowdfunding outcomes in Initial Coin Offerings (ICOs). The main result suggests that altruism and ICO firm valuation are negatively related. We, then, explore several channels to shed some light on whether the negative altruism-valuation relation is causal. Our findings suggest that it is not altruism that causes lower firm valuation; rather, low-quality entrepreneurs select into altruistic projects, while the marginal effect of altruism on high-quality entrepreneurs is actually positive. Altruism increases the funding amount in ICOs in the presence of high-quality projects, low asymmetric information, and strong corporate governance. Full article

The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest. Full article