Search Results (10,407)

The rapid expansion of the Internet of Medical Things (IoMT) and intelligent assistive technologies has intensified the need for resource-optimized cryptographic hardware to protect sensitive biometric data. Cryptographic hardware performance relies primarily on modular arithmetic operations, especially field multiplication. Although the binary extension field provides carry-less arithmetic ideal for battery-powered devices, standard general-purpose processors lack the dedicated hardware required to execute these operations efficiently. This paper proposes a novel multicore-based modular multiplication algorithm that bridges this gap by exploiting the parallel coordination fabric and high-bandwidth interconnects of modern embedded multicore systems. Central to this work is the Progressive Product Reduction (PPR) paradigm, which optimizes hardware efficiency by integrating the multiplication and field reduction phases into a unified process, thereby minimizing intermediate data storage and computational depth. We introduce and analyze two distinct architectural strategies—Progressive Product Reduction with Column Division (PPR-CD) and Progressive Product Reduction with Row Division (PPR-RD)—and establish rigorous mathematical models to estimate hardware area, critical path delay, and exact operational latency across various core configurations. Our performance evaluation demonstrates that the PPR-RD architecture achieves superior Area-Delay Product (ADP) and energy efficiency, providing a scalable framework for securing sensitive biometric data in next-generation assistive devices. This implementation ensures robust cryptographic protection for assistive devices while maintaining energy autonomy and computational resilience essential for sustaining consumer trust, advancing global health equity, and driving financial stability in future digital economies. Full article

Maintaining up-to-date inventory data is essential for modern supply chains, yet transitioning to automated tracking frequently requires prohibitive structural modifications and substantial financial investments. To address this bottleneck and enable low-cost innovation within existing legacy warehouse infrastructures, we developed an industrially deployable, practical vision system that integrates deep learning, classical computer vision, and robotic elements, which we call the MOBOT system. Its vision pipeline unifies robotic image capture, depth-based ROI localization, perspective transformation, material identification, and lightweight geometric counting algorithms into an adaptive, field-ready framework. We evaluated this inventory-monitoring system in an operational warehouse storing semi-finished metal products. Experimental results confirm the system’s high robustness across various geometries: structured hollow shapes, such as tubes, hollow sections, and U-profiles, result in a low error rate of 2–4%, while for irregular, solid cross-sections, the fundamental error rate measured under real-world conditions is 7–20%. This robot-assisted platform delivers unambiguous semi-finished metal product inventory monitoring without requiring facility modifications. Full article

Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4063 SME and 1903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in two of four SME sectors, matched it in a third, and was marginally outperformed in the fourth; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: an architecture for AI-driven audit planning that is internally validated against verified audit outcomes within the historical Lebanese dataset examined, built entirely from data tax administrations already collect, and potentially transferable to comparable jurisdictions, though not yet operationally deployed. Full article

Background/Objectives: A potential association between individualized multimodal immunotherapy (IMI) within a multiphase combined treatment strategy and prolonged survival was explored through retrospective real-world data analysis. Methods: The patient selection criteria were as follows: treatment after 27 May 2015, adults between ages 18 and 70, GB diagnosis and IDH1wt status documented, known MGMT promoter methylation status (methylated versus unmethylated), known status of OS, and no second malignancy. IMI is currently only accessible to patients with a >60 KPI and sufficient financial resources. Results: A total of 104 patients were identified, distributed as per MGMT methylation versus unmethylation status, respectively, as 18% female/25% male versus 23% f/38% m; the median age at intake was 54 y versus 50 y; the resection extent was 12 R0, 28 < R0 and three not documented versus 25, 28 and eight; and the median KPI at intake was 70 overall. Patients received a median of 37 versus 31 modulated electro-hyperthermia sessions, 37 versus 32 NDV injections, and two versus one dendritic cell vaccines. The median OS and percentage 2 y OS were 33.1 months and 69.7% versus 19.0 months and 35.4%. There were no major adverse reactions (ARs), but the AR burden increased with checkpoint inhibitor use. A 33-patient subset was analyzed on health-related quality of life (HRQoL) throughout IMI treatment, based on at least five EQ-5D-5L questionnaires available from intake. Mobility and self-care affected HRQoL less than usual activity, pain/discomfort and anxiety/depression. Throughout IMI, HRQoL remained stable. Before progressive disease, the median health utility index was 0.86, resulting in a median quality-adjusted life-months of 4.6 versus a median of 5.4 calendar months. Conclusions: Real-world observation indicates IMI may prolong GB patient OS while maintaining HRQoL. Full article

Climate diplomacy in Bangladesh may reflect a structural gap between two tiers of media coverage. This qualitative study draws on four focus group discussions with 24 district journalists in Cox’s Bazar, Ukhia, Shatkhira, and Shunamganj from four of the country’s most climate-vulnerable districts and semi-structured interviews with 10 Dhaka-based national journalists (N = 34), conducted between August and December 2024 and analyzed through inductive thematic analysis supplemented by concordance-based coding. National participants engage with international diplomatic forums but report limited operational knowledge of ground-level climate impacts, while district-based participants hold granular, project-level knowledge of climate impacts, financial flows, and displacement yet describe themselves as structurally excluded from diplomatic representation. District participants also raised institutional barriers such as NGO and INGO opacity, restricted access to government information, and mismanagement of disaster relief that were largely absent from the Dhaka data and characterized climate financing as politically routed along lines of power rather than community need. These findings suggest that weak diplomatic representation may reflect an information-pathway failure rather than, or in addition to, a capacity deficit, with implications for media-inclusion research in climate governance. Full article

Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI. Full article

This study examines 22@Barcelona (22@BCN), a widely cited case in the development of innovation districts. Initially conceived as a means of transforming Poblenou from a post-industrial area into a knowledge-economy hub, 22@BCN also formed part of Barcelona’s broader trajectory of entrepreneurial urban transformation. Its slowdown after 2008, Spain’s property-market bust, and the subsequent rise in residential and office rents brought into view a tension between innovation-led redevelopment and real-estate financialization. The analysis follows an explanatory single-case design and draws on documentary sources and secondary statistical data, primarily from official databases, to examine 22@BCN across multiple scales from 2000 to 2025. It traces the relationships among dependence on real-estate finance, housing pressures, changing regulatory arrangements, and uneven outcomes within 22@BCN. The findings suggest that these contradictions cannot be explained simply as a result of local planning failure or incomplete implementation. Rather, they need to be understood in relation to the interaction between innovation policy, real-estate finance, and multi-scalar governance. In doing so, the study contributes to critical research on innovation districts by highlighting the wider political–economic conditions through which districts are produced, financed, and governed. Full article

Rural maternity care in the United States faces an unprecedented crisis. As of 2022, more than half of rural hospitals lacked obstetric services, leaving many rural communities without local maternity care. This descriptive case study documents the implementation of maternity services at a 25-bed rural critical access hospital in North Carolina. We reviewed and coded data derived from the following sources from 2020–2025: institutional planning documents, clinical protocols, standard workflows, operational procedures, administrative records, meeting minutes, presentations and summaries, and published commentaries, manuscripts and articles related to the new maternity unit. We identified six critical domains for sustainability: (1) hospital and community engagement, (2) stable multidisciplinary staffing models, (3) emergency preparedness, (4) anesthesia service delivery, (5) financial sustainability, and (6) risk-appropriate scope of care. Successful implementation required integrated approaches across organizational dimensions facilitated by committed institutional leadership; community advocacy; innovative staffing models with family physicians, certified nurse-midwives, and certified registered nurse anesthetists; competency-based emergency training; Medicaid-based financial sustainability; and explicit risk stratification with clear interfacility communication protocols. Workforce sustainability emerged as the most significant ongoing challenge. Rural maternity service sustainability requires multifaceted, evidence-based approaches integrating workforce development, organizational infrastructure, financial mechanisms, and clear scope definitions. This case study demonstrates a model of perinatal regionalized and risk-appropriate care with replicable strategies that can inform efforts to address maternal health equity and strengthen rural health care systems. Full article

University–industry research collaboration is an important pathway through which firms integrate external scientific knowledge, strengthen innovation capabilities, and develop sustainability-oriented technological solutions. Public procurement, as a demand-side policy instrument, may provide market demand, resource expectations, and policy signals that encourage firms to cooperate with universities and research institutes. However, whether public procurement is associated with firms’ university–industry research collaboration remains insufficiently examined. Based on Chinese A-share listed firms from 2013 to 2024, this study matches public procurement contract data with listed firms and their subsidiaries and constructs firm-year measures of public procurement. University–industry research collaboration is measured by co-applied patents between firms and universities or research institutes. The baseline two-way fixed-effects results show a positive and statistically significant association between public procurement scale and university–industry research collaboration, although the estimated magnitude is economically small. Additional count-data and selection-adjusted analyses provide partial support for this relationship, but the results are not fully consistent across all specifications. The mechanism analysis provides suggestive evidence that R&D investment scale may serve as a channel through which public procurement is related to collaborative research activities. Further analysis shows that financing constraints positively moderate this relationship, and ownership heterogeneity indicates that the association is stronger among state-owned enterprises. These findings suggest that public procurement should not be understood as a uniformly effective driver of university–industry collaboration. Rather, its role appears to depend on firms’ financial conditions and institutional characteristics. This study contributes to research on demand-side innovation policy, university–industry collaboration, and sustainable innovation systems by highlighting the conditional nature of the procurement–collaboration relationship. Full article

Financial time-series forecasting remains challenging because of high volatility, nonlinear market dynamics, and the growing volume of heterogeneous information available to market participants. This study investigates Bitcoin forecasting in a big-data setting by combining high-frequency market data aggregated to hourly forecasting features, news headlines, Bitcoin on-chain variables, and broader macro-financial indicators. Rather than treating headline sentiment as a predefined categorical property, we construct continuous headline-conditioned market-impact labels from short-horizon Bitcoin price responses, directional volume imbalance, and volatility conditions. A chronology-controlled expanding-window FinBERT procedure generates scores without reusing each headline’s own future-derived target. The scores are integrated into multivariate Bitcoin forecasting using iTransformer, with LSTM as a benchmark. Evaluation uses repeated runs, benchmarks, Diebold–Mariano tests, backtesting, and forecast-free momentum controls. The headline-derived signal exhibits a measurable but temporally heterogeneous association with subsequent Bitcoin movements. Adding the headline score yields small, statistically non-significant error reductions for iTransformer, whereas it significantly worsens LSTM forecasts. Backtesting shows no consistent improvement in terminal portfolio value, but the headline feature alters the risk–return profile in several strategy configurations. Overall, the study provides a chronology-aware evaluation of whether headline-conditioned market-impact signals add predictive or economic value, while acknowledging residual dependence from exploratory iTransformer architecture selection. Full article

Churn prediction research is largely accuracy-oriented, and the link between prediction and financial decision-making remains underdeveloped. This study builds and tests the chain from prediction to profit as follows: calibrated prediction, SHAP-based explanation, a profit-maximizing threshold, and EMP-based evaluation, across five datasets in three sectors. Resampling and class weighting do not improve ranking quality (PR-AUC) and degrade calibration up to 19-fold (ECE); shifting the threshold recovers the same recall without retraining. SHAP-based analysis shows no driver is consistently strong across sectors: usage volume has the broadest reach, but the strongest drivers are dataset-specific, and models do not transfer after semantic alignment. The profit-maximizing threshold matches or beats the fixed 0.5 threshold in all 100 cost-success scenarios examined, and a paired test across datasets and seeds confirms the difference (Wilcoxon p < 0.001); a threshold that looks reasonable by accuracy can still cause a loss. Predictability and profitability rank inversely across datasets (Spearman ρ = −0.90): the most predictable dataset yields the lowest EMP, driven by churners’ value distribution. Compared with ProfLogit, which embeds the profit objective in training, a threshold on a well-calibrated model proves sufficient. Value comes from turning calibrated probabilities into decisions with a financial criterion, not from balancing data. Full article

Computing capacity has become a metered factor of production for digitally intensive enterprises, yet its consumption exhibits strong temporal heterogeneity—tidal intraday cycles, weekly contrasts, seasonal surges, and project-driven regime shifts—so that individually provisioned capacity is structurally underutilized. This paper proposes a park-level computing power sharing center (CPSC) as an institutional mechanism that converts the temporal complementarity of co-located enterprises into measurable cost savings. We develop a general mode-design framework that separates CPU core-hours from GPU card-hours, characterizes demand via deterministic tides and stochastic modulations, and derives optimal pooled capacity commitments through a newsvendor-type quantile condition. A parametric calibration protocol maps observable temporal features—peak-to-trough ratios, inter-tenant phase spreads, and residual volatility—into closed-form diversity-factor expressions with Monte Carlo confidence intervals. The procurement model covers a multi-option contract menu (on-demand, one–three-year reserved instances, savings plans, and spot), region-specific pricing, hardware class tariffs, and ancillary costs, including network egress, migration, and data sovereignty compliance; benefits are measured relative to each tenant’s individually optimal reserved portfolio, not naive retail procurement. A mechanism design analysis incorporating Shapley value allocation, Bayesian incentive compatibility, and penalty structures ensures individual rationality and robustness to misreporting and strategic load shifting. We further develop an energy model—with utilization-dependent power draw, facility PUE, embodied carbon, and marginal grid emission factors—showing that financial savings translate into genuine emission reductions only when pooling enables physical capacity retirement rather than mere billing reallocation. The framework is applied to twenty representative Chinese parks spanning seven functional categories; all park-level data are reconstructed from public sources using the calibration methodology, and the reported figures are model-derived projections, not empirical measurements. The model yields procurement saving estimates of 4.6–20.2% relative to individually optimal reserved-procurement portfolios, with high-diversity parks at the upper end. Sensitivity analyses across regional tariffs, hardware mixes, and cross-country utilization benchmarks (Uptime Institute, US DOE, EU Commission) confirm robustness and delineate boundary conditions. This paper concludes with a data provenance taxonomy and a phased implementation roadmap. Full article

The most attractive companies to work for play a crucial role in the sustainability environment. Previous studies have examined the relationship between excellent human resource management and social and environmental sustainability. The aim of this study is to investigate the relationship between workplace excellence and economic sustainability. To this end, data from the annual ranking of the Revista de Actualidad Económica of the best places to work in Spain during the period 2013–2023 have been used, where Corporate Social Responsibility (CSR) is considered a key factor. This study uses observational secondary firm-level data covering the period 2013–2023 and applies comparative descriptive and inferential statistical analysis to examine differences between firms included and not included in the ranking. The findings indicate that the most attractive companies to work for are internationalized, large in size, and predominantly operate in the financial and insurance, professional and scientific, information, and energy supply sectors. These companies show better ratios in productivity, results and value added per employee, as well as equity. However, their profitability and solvency do not show significant results over time, so it cannot be clearly concluded that there is a relationship between the best companies to work for and economic sustainability. The practical implications of this study include recommendations for the government to promote complementary policies towards sustainability in general, such as both environmental (environmental certification) and inclusive social measures, among companies demonstrating higher CSR. Full article

Paludiculture, the sustainable productive use of wet and rewetted peat soils, offers a dual solution for preserving soil carbon and reducing greenhouse gas emissions whilst offering a form of income for landowners/farmers. However, establishing best practices for the transition to wet agriculture production systems remains difficult due to the inherent variability of peatland sites. This study synthesises evidence from the peer-reviewed literature, grey literature, and stakeholder surveys to map current practices, concepts, knowledge gaps, and research priorities. The main gap is a lack of long-term studies tracking crops from establishment through multiple harvests. Practical knowledge is also geography-ically, limiting applicability without local environmental and policy context. Further critical gaps include crop species selection, upscaling, biodiversity impacts, specialised harvesting equipment, and evolving end-product markets, alongside country-specific legal and financial barriers. Key recommendations include establishing a robust initial site investigation as edaphic properties and previous land-use can inform crop establishment and potential operational hurdles. Furthermore, increased funding for long-term farm-based projects, technological innovation and prioritising producing data that supports finance models must be made available to offset the costs of transitioning and developing new, system-level agronomical tools. Ultimately, scaling paludiculture hinges on robust incentive frameworks, shared knowledge platforms, and standardised nomenclature to guarantee long-term environmental and economic sustainability. Full article

Emissions trading systems are central policy instruments for the transition toward a low-carbon economy, yet evidence on the value relevance of recognized carbon items is limited. This study examines whether the recognition and reported amounts of emission allowance assets (EAs) and emission liabilities (ELs) are associated with share price conditional on conventional accounting information. Using 16,036 firm-year observations of KOSPI and KOSDAQ non-financial firms from 2018 to 2025, reflecting the available coverage of firm-level EA and EL data, this study employs an extended Ohlson price model. EA recognition is negatively associated with share price, and this association remains statistically significant in both the industry and firm fixed-effect specifications. The reported amount of EAs is also negatively associated with share price, although the result is sensitive to model specification. The positive associations of EL recognition and reported amounts do not persist after controlling for firm fixed effects, although they are observed in the industry fixed-effect specifications. The findings are consistent with the interpretation that recognized carbon-accounting items may capture information about regulatory exposure and operating conditions beyond their formal accounting classifications. These findings provide evidence on the value relevance of recognized carbon-accounting information under the K-ETS. Full article