Search Results (42)

This paper presents an organization-managed IMAP Agent framework for extending email functionality in environments that rely on outsourced mail services. In this study, outsourced mail services refer to externally operated mailbox providers offering sufficiently scalable email infrastructures and standard IMAP interfaces, such as Gmail, Microsoft 365, and other commercial mailbox providers. In the proposed framework, IMAP Agents are operated within an organization, while user authentication continues to rely on existing institutional infrastructures such as Identity Providers (IdP) or Integrated Authentication Infrastructure (IAI). The IMAP Agent operates as a post-authentication processing component using credentials issued by these infrastructures, without modifying or intervening in the outsourced mail service itself. The framework enables organization-managed mailbox-side email processing without requiring administrative control over the mail server or dependence on provider-specific APIs. As a proof of concept, representative email-processing functions are implemented, including detection of suspicious messages based on header-level authentication information and automatic insertion of thread-consistent warning messages without altering the original email content. To evaluate the feasibility of the proposed framework, a prototype system was implemented using multiple containerized IMAP Agent instances. The experimental results showed that warning messages were typically appended within approximately 300 ms after message detection. Multi-container evaluations ranging from 1 to 100 concurrent IMAP Agent instances demonstrated low CPU overhead and approximately linear memory growth under idle-monitoring conditions, indicating the operational feasibility of deploying multiple IMAP Agent instances on a single host. These results suggest that the proposed framework can provide provider-independent and organization-managed extension of email functionality in outsourced mail environments through standard IMAP operations. Full article

Background: Nerve conduits are used to bridge peripheral nerve defects caused by trauma, iatrogenic injury, or oncologic disruption. Three-dimensional (3D) biomimetic scaffolds for peripheral nerve regeneration have advanced significantly in recent years, driven by improvements in printing technology and neuronal seeding techniques. We report on published designer conduits that can recreate the epineurium, a critical yet challenging-to-manufacture feature of nerve tissue. Methods: A medical librarian conducted a literature search for our systematic review on EMBASE, Web of Science, and PUBMED, following PRISMA guidelines, for articles from January 2010 to January 2026 for the systematic review. Descriptive statistical analysis was performed using Microsoft 365 Suite software. The literature review was conducted using keywords and search terms describing the history and development of 3DP nerve guidance conduits published prior to January 2026. Results: Our search yielded 273 titles, of which 8 were included after full-text review; these studies used 3D printing to generate nerve conduits for preclinical models. Manual data extraction identified studies reporting successful epineurial recreation. The included scaffold materials were polycaprolactone, poly(l-lactide-co-ε-caprolactone), poly(lactic-co-glycolic acid), acrylate resin, and gelatin methacryloyl. In animal model studies, various terms were used to describe the epineurium outer sheath. Despite this variability in nomenclature, many of these reports indicated successful sciatic functional index (SFI) recovery, favorable g-ratios, good durability, high cell viability, and significant neurite elongation at the time of sacrifice. Conclusions: 3DP nerve conduits targeting the epineurium are promising approaches for treating peripheral nerve defects. The constructs promote oriented growth and myelination. Future research on incorporating the epineurium into nerve scaffolds may consider encapsulating NGF to promote more efficient nerve regeneration, standardizing the definition of epineurial recreation, designing mechanical and permeability reporting benchmarks, and evaluating cell strategies using comparable functional and histologic endpoints. Full article

Climate-driven droughts pose major threats to rainfed farming worldwide. To address these impacts, smart agricultural approaches focusing on conservation practices (CPs) have been widely recommended by institutions such as the Food and Agriculture Organization of the United Nations (FAO), the World Food Programme (WFP), and the International Fund for Agricultural Development (IFAD), among others. This systematic review synthesizes evidence on CPs for climate-driven drought adaptation and the barriers to their adoption in southern Mozambique, where drought is predominant. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search across four academic databases retrieved 595 records (2000–April 2025), of which 23 were peer-reviewed studies. Data was extracted and analyzed using Microsoft Excel 365 and NVivo 15. As a result, five major CPs were identified: (i) Minimum tillage; (ii) Mulching and residue retention; (iii) Maize–legume (cowpea, groundnuts, pigeon pea, and soybeans) intercropping and crop rotation; (iv) Drought-tolerant maize varieties; and (v) indigenous practices. The systematic review has shown that minimum tillage was associated with 89–90% increase in maize and legume yields; Mulching expands maize yields by 24–59%; intercropping increases maize and legume yields by more than 30%; drought tolerant maize varieties expand yields by 26–46%; and local practices support farming continuity and contribute to resilience, although quantitative yield effects were not reported, with adoption ranging from 75–100%. These findings suggest that minimum tillage and intercropping/crop rotation are the most effective CPs in enhancing yield and resilience. Despite their potential, the adoption is generally low (average around 40%, with some as low as 7–16% for minimum tillage). Reasons for limited uptake include economic, cultural, institutional, biophysical, and technological barriers. These findings highlight the need for integrated policy approaches that combine climate-smart agriculture with indigenous knowledge in southern Mozambique. Full article

Augmented reality (AR) is increasingly adopted in industrial environments as a tool for improving employee training and supporting complex assembly operations. The purpose of this study was to investigate the design, implementation, and strategic potential of AR-based work instructions using Microsoft HoloLens 2 in a real manufacturing environment. The study proposes and applies an integrated evaluation framework combining direct observation, performance evaluation, semi-structured interviews, quantitative SWOT analysis, PDCA-based process assessment, and economic cost analysis to assess AR-based training in a real manufacturing environment. AR training was implemented through Microsoft Dynamics 365 Guides for a standardized assembly procedure and evaluated with respect to training efficiency, user interaction, and feasibility of deployment. The results indicate improved task guidance consistency and descriptive performance indicators, suggesting enhanced training support under real production conditions. The SWOT analysis identified a favorable SO strategic position, highlighting strong internal capabilities and promising external opportunities for further deployment. The cost analysis shows that AR-based training becomes economically advantageous when applied to a larger number of trainees, despite high initial investment costs. Overall, the study demonstrates that AR-based training, when evaluated through a structured strategic and economic framework, represents a promising and strategically advantageous approach for industrial education, provided that ergonomic challenges, user adaptation, and financial constraints are systematically addressed. Full article

Traditional drug design methods based on trial and error are costly and inefficient. The computational approach ToSS-MoDE (Topological Substructural Molecular Design) offers an alternative by linking molecular descriptors to biological activity. To develop a QSAR model to predict the anti-inflammatory activity of synthetic and natural compounds using weighted spectral moments. Spectral moments (µ_k) were calculated from the adjacency matrix between bonds for 410 compounds (180 active and 230 inactive). MODESLAB software (MICROSOFT OFFICE 365) was used to generate descriptors, and Linear Discriminant Analysis (LDA) was applied to classify activity. The model was validated with an external series of 62 compounds. Results. The model showed an overall classification of 91.59% in the training series and 90.2% in validation. The spectral moments µ₀, µ₃, µ₄, and µ₅ were the most significant. Diosgenin, a natural metabolite, showed potential anti-inflammatory activity (classification probability: 81%). The model showed strong training performance (91.7% accuracy) and promising external performance for confidently classified compounds. All datasets, descriptor-generation settings, coefficients, and posterior probabilities are fully described in the main text to ensure full reproducibility. Full article

Background: This study was conducted to guide researchers by providing a global macroscopic perspective on the main characteristics of publications on nursing care in patients undergoing organ and tissue transplantation. Methods: The data obtained from the Web of Science (WoS) database in light of the determined keywords were analyzed using quantitative and qualitative criteria. The bibliometric analyses and visualizations were conducted using Microsoft Excel 365, VOSviewer (version 1.6.20), and the Biblioshiny interface within the R environment. Results: A total of 525 records were initially identified from the Web of Science database. After excluding meeting abstracts, editorials, and letters to ensure data quality, 411 publications (articles and reviews) were included in the final bibliometric analysis. The majority of these publications (87.6%) were published after 2005. The top five countries with the highest number of publications are the United States (USA), Brazil, China, Turkey, and Australia. Conclusions: This study is the first bibliometric analysis study to examine the trend of scientific publications indexed in Web of Science for nursing care in patients with organ and tissue transplantation processes. The findings have the potential to be used to improve the work of scientists conducting research in the field of nursing care. Full article

Objectives: This study sought to identify sociodemographic and clinical predictors associated with the absence versus presence of alterations in mental, neurological, cardiovascular, osteomuscular, and pulmonary conditions, to provide information towards targeted interventions for non-communicable diseases (NCDs) in urban Colombian populations. Methods: A cross-sectional analysis was performed on 2495 patients (70.1% women) from public health facilities in Bogotá, using the Colombia Open Data “Enfermedades Crónicas” dataset collected between January and December 2023. Associations between sociodemographic variables (sex, age groups, education, and ethnicity) and clinical variables (BMI, type of disability, COVID-19 vaccination status, psychiatric risk, and the modified Medical Research Council dyspnea scale) were examined in relation to health outcomes. Data cleaning involved the exclusion of 107 outliers identified by z-scores >|3| using Microsoft Excel 365. Categorical variables were summarized using frequencies and proportions, and Pearson’s chi-square tests were applied to assess bivariate associations (e.g., BMI–health conditions, and sex–disability associations). Multivariable Firth’s penalized logistic regression models (implemented in Python 3.14 and Jamovi 2.3) were used to predict the absence of alteration (reference category: presence), adjusting for multicollinearity (variable inflation factor, VIF) and events-per-variable ratios. Odds ratios (ORs), 95% confidence intervals (CIs), and two-tailed p-values were estimated, with statistical significance set at p < 0.05. Results: Women predominated in obesity (81% vs. 19% in men, p < 0.001) and in unaltered conditions (e.g., 71% of cases without pulmonary alterations) but exhibited a lower crude prevalence of disability (6% vs. 16% in men, p < 0.001). Men represented higher proportions of alterations (e.g., 53.8% of pulmonary cases vs. 46.2%, p = 0.006) and mental disabilities (70%, p < 0.001). Firth regression models identified the following predictors: for mental alteration, a single COVID-19 vaccine dose (OR = 2.39, 95% CI 1.12–5.09, p = 0.024), occupation (OR = 1.07, 95% CI 1.05–1.10, p < 0.001), BMI (OR = 0.96, 95% CI 0.93–0.98, p < 0.001), and disability (inverted OR = 4.35, 95% CI 2.56–7.69, p < 0.001); for neurological alteration, occupation (OR = 1.15, 95% CI 1.10–1.21, p < 0.001) and disability (inverted OR = 3.45, 95% CI 1.43–8.33, p = 0.006); for cardiovascular alteration, BMI (OR = 1.02, 95% CI 1.00–1.03, p = 0.042); for osteomuscular alteration, occupation (OR = 1.03, 95% CI 1.01–1.06, p = 0.011); and for pulmonary alteration, occupation (OR = 1.07, 95% CI 1.03–1.11, p = 0.001). The models demonstrated a moderate to excellent goodness-of-fit (R² = 0.25–0.72). Conclusions: Sex, BMI, disability status, occupation, and COVID-19 vaccination status emerged as key predictors of NCD-related alterations, highlighting specific vulnerabilities such as partial immunization for mental health risk, and disability for mental and neurological outcomes. Targeted interventions, including completion of vaccination schedules, mitigation of occupational exposure, BMI management, and disability-inclusive care, may reduce health disparities and support PAHO/WHO 2025 targets. Longitudinal studies are recommended to establish causal relationships in the context of Colombia’s fragmented subnational NCD evidence base. Full article

Risk management has become increasingly important in the financial world. Considering its importance, it is necessary to measure these risks. The financial market uses two risk measures: Value at Risk (VaR) and Expected Shortfall (ES). After the subprime crisis, the market began to emphasize ES instead of VaR. The hypothesis of this paper to be tested is that longer periods provide better information than shorter, more recent periods for measuring ES volatility to hedge trades. The ES can be adopted using parametric, semi-parametric, and non-parametric methods, and the analyses of the log return indicators started on 3 January 2000 and ended on 5 May 2023. The analyses carried out to evaluate these log return indicators covered the period from 6 May 2023 to 1 August 2025, where it was found that the exchange rate volatility of the Brazilian Real exceeded the VaR limits and even reached the Expected Shortfall risk zone. Then, a different analysis was performed, starting on 11 March 2020 and ending on 5 May 2023. This second analysis, as the first analysis, was carried out to evaluate these log return indicators that covered the period from 6 May 2023 to 1 August 2025. In this latest period analysis, the exchange rate volatility of the Brazilian Real reached the Exchange Shortfall risk zone in a different way compared to the first way. All three types of methods—parametric, non-parametric, and semi-parametric—show distinct behaviors depending on the period evaluated. The hypothesis was rejected, but the hedging strategies should account for asset volatility. The software used to calculate the estimators was Microsoft Excel 365 and Stata 14.2. Full article

Objectives: To assess the utility of Microsoft Copilot, a generative AI tool, in providing meaningful differential diagnosis and management strategies comparable with those generated by a board-certified radiologist using cone beam computed tomography (CBCT) studies in maxillofacial disease and thus assess its potential utility to help with the initial provisional diagnostic process. Study Design: A pilot project designed as a single-center, retrospective study using a convenient sample was conducted. De-identified data collected from patient charts in a consistent format was fed to Microsoft 365 Copilot (MCP) to generate a list of meaningful differential diagnosis (DD) and management protocols. Scores ranging of 0–3 were given for 0–3 matches in DD and management protocols, respectively. Results: Proportional analysis showed that the radiologist and Copilot agreed on the DD in 75.2% of cases and 94.6% of cases in management protocols. For biopsy recommendations, the radiologist and Copilot advised biopsy in 33 (89.2%) cases while they did not recommend biopsy in 23 (41.8%) cases. Conclusions: Generative AI platforms at this point may have value in generating DD and management protocols based on maxillofacial CBCT findings. However, the radiologist’s judgement based on clinical context, feature recognition, and critical analysis seemed to outperform MCP. Larger studies with statistical validation are warranted. Full article

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools. Full article

The rapid integration of generative artificial intelligence (AI) into creative workflows is transforming design from a human-driven activity into a synergistic process between humans and AI systems. Yet, most current tools still operate as linear “executors” of user commands, which fundamentally clashes with the non-linear, iterative, and ambiguous nature of human creativity. Addressing this gap, this article introduces a conceptual framework of five irreducible paradoxes—ambiguity vs. precision, control vs. serendipity, speed vs. reflection, individual vs. collective, and originality vs. remix—as core design tensions that shape human–AI co-creative systems. Rather than treating these tensions as problems to solve, we argue they should be understood as design drivers that can guide the creation of next-generation co-creative environments. Through a critical synthesis of existing literature, we show how current executor-based AI tools (e.g., Microsoft 365 Copilot, Midjourney) fail to support non-linear exploration, refinement, and human creative agency. This study contributes a novel theoretical lens for critically analyzing existing systems and a generative framework for designing human–AI collaboration environments that augment, rather than replace, human creative agency. Full article

Generative models are being used in all domains. While primarily built for processing texts and images, their reach has been further extended towards data-driven forecasting. Whereas there are many statistical, machine learning and deep learning models for predictive forecasting, generative models are special because they do not need to be trained beforehand, saving time and computational power. Also, multivariate forecasting with the existing models is difficult when the future horizons are unknown for the regressors because they add mode uncertainties in the forecasting process. Thus, this study experiments with TimeGPT(Zeroshot) by Nixtla where it tries to identify if the generative model can outperform other models like ARIMA, Prophet, NeuralProphet, Linear Regression, XGBoost, Random Forest, LSTM, and RNN. To determine this, the research created synthetic datasets and synthetic signals to assess the individual model performances and regressor performances for 12 models. The results then used the findings to assess the performance of TimeGPT in comparison to the best fitting models in different real-world scenarios. The results showed that TimeGPT outperforms multivariate forecasting for weekly granularities by automatically selecting important regressors whereas its performance for daily and monthly granularities is still weak. Full article

Demand forecasting is different from traditional forecasting because it is a process of forecasting multiple time series collectively. It is challenging to implement models that can generalise and perform well while forecasting many time series altogether, based on accuracy and scalability. Moreover, there can be external influences like holidays, disasters, promotions, etc., creating drifts and structural breaks, making accurate demand forecasting a challenge. Again, these external features used for multivariate forecasting often worsen the prediction accuracy because there are more unknowns in the forecasting process. This paper attempts to explore effective ways of leveraging the exogenous regressors to surpass the accuracy of the univariate approach by creating synthetic scenarios to understand the model and regressors’ performances. This paper finds that the forecastability of the correlated external features plays a big role in determining whether it would improve or worsen accuracy for models like ARIMA, yet even 100% accurately forecasted extra regressors sometimes fail to surpass their univariate predictive accuracy. The findings are replicated in cases like forecasting weekly docked bike demand per station every hour, where the multivariate approach outperformed the univariate approach by forecasting the regressors with Bi-LSTM and using their predicted values for forecasting the target demand with ARIMA. Full article

Background/Objectives: Inherited retinal diseases (IRDs) are a heterogeneous group of rare eye disorders characterized by progressive photoreceptor degeneration, leading to severe visual impairment or even blindness. This study aims to investigate the prevalence, types, and clinical significance of ophthalmic comorbidities in Portuguese patients with IRDs. Methods: This nationwide Portuguese population-based retrospective study was based on the IRD-PT registry (retina.com.pt). Statistical analysis was conducted using Microsoft^® Excel^® for Microsoft 365 and IBM SPSS Statistics version 29.0.2.0. Informed consent was obtained from all participants. Results: A total of 1531 patients (1254 families) from six centers were enrolled. The cohort consisted of 51% males, with a mean age of 45.8 ± 19.3 years and a mean age at diagnosis of 39.4 ± 19.5 years. Overall, ocular comorbidities were reported in 644 patients (42.1%). In 176 individuals (11.5%), multiple concurrent comorbidities were found. Cataract was the most common comorbidity (21.3%), followed by amblyopia (6.3%) and high myopia (5.9%). Statistically significant associations with ocular comorbidities were observed in isolated progressive IRDs. Specifically, AR RP was associated with cataract (p < 0.001), and gene analysis revealed several significant associations. CRB1 was statistically linked to epiretinal membrane (ERM) (p = 0.003), EYS with cataract (p = 0.001), PROM1 with choroidal neovascularization (CNV) (p = 0.0026), and USH2A with macular hole (p = 0.01). Patients with the RPE65 mutation in Leber congenital amaurosis were associated with ERM (p = 0.019). There was also a significant association between X-linked RP and high myopia (p < 0.001) and CNV in Best disease (p < 0.001); in syndromic IRDs, cataract, cystoid macular edema, and ERM were observed in Usher syndrome, p = 0.002, p = 0.002, and p = 0.005, respectively, and the MYO7A gene was linked to cataract (p = 0.041) and strabismus (p = 0.013); pseudoxanthoma elasticum was significantly associated with CNV (p = 0.002); and foveal hypoplasia was associated with anterior segment dysgenesis (p < 0.001). Conclusions: This study enhances the current understanding of ocular comorbidities in IRDs in Portuguese patients. Common findings were cataract, refractive error, and CME. Stationary IRDs and pattern dystrophies showed fewer concomitant comorbidities, supporting their classification as non-progressive or benign conditions. The significance of registries like IRD-PT cannot be overstated, particularly in the context of rare diseases. These databases serve multiple crucial functions in enabling detailed documentation of disease characteristics and long-term monitoring of disease progression. Full article