Search Results (767)

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article

Purpose: This study aimed to develop and validate a new machine learning (ML)-based screening tool for a two-step prediction of the need for and type of nutritional therapy (enteral, parenteral, or combined) using Nutrition Risk Screening 2002 (NRS-2002) and other demographic parameters from the Optimal Nutrition Care for All (ONCA) national cohort data. Methods: This multicenter retrospective cohort study included 191,028 patients, with data on age, gender, body mass index (BMI), NRS-2002 score, presence of cancer, and hospital unit type. In the first step, classification models estimated whether patients required nutritional therapy, while the second step predicted the type of therapy. The dataset was divided into 60% training, 20% validation, and 20% test sets. Random Forest (RF), Artificial Neural Network (ANN), deep learning (DL), Elastic Net (EN), and Naive Bayes (NB) algorithms were used for classification. Performance was evaluated using AUC, accuracy, balanced accuracy, MCC, sensitivity, specificity, PPV, NPV, and F₁-score. Results: Of the patients, 54.6% were male, 9.2% had cancer, and 49.9% were hospitalized in internal medicine units. According to NRS-2002, 11.6% were at risk of malnutrition (≥3 points). The DL algorithm performed best in both classification steps. The top three variables for determining the need for nutritional therapy were severe illness, reduced dietary intake in the last week, and mild impaired nutritional status (AUC = 0.933). For determining the type of nutritional therapy, the most important variables were severe illness, severely impaired nutritional status, and ICU admission (AUC = 0.741). Adding gender, cancer status, and ward type to NRS-2002 improved AUC by 0.6% and 3.27% for steps 1 and 2, respectively. Conclusions: Incorporating gender, cancer status, and ward type into the widely used and validated NRS-2002 led to the development of a new scale that accurately classifies nutritional therapy type. This ML-enhanced model has the potential to be integrated into clinical workflows as a decision support system to guide nutritional therapy, although further external validation with larger multinational cohorts is needed. Full article

This paper presents a novel framework for valuing ESG-based collaborative synergies in cross-border mergers and acquisitions (M&A) using a real options approach, with a specific application to L’Oréal’s acquisition of Aesop. The methodology integrates a Value-at-Risk (VaR) model to quantify and adjust for ESG-related risks, providing a more robust valuation framework. We demonstrate how linking sustainability practices with real option valuation in multinational corporations (MNCs) can enhance long-term value creation and reduce risk, thereby aligning synergy goals with ESG objectives. By applying our VaR-adjusted model to the L’Oréal–Aesop case, this study contributes to corporate finance by integrating advanced risk management and sustainability into synergy valuation, and to international business by providing an empirical example of this integrated valuation approach for cross-border acquisitions. Full article

Background/Objectives: Traditional models like ordinary least squares (OLS) struggle to capture non-linear relationships in children’s subjective well-being (SWB), which is associated with physical activity. This study evaluated machine learning (ML) for predicting SWB, focusing on sports participation, and explored theoretical prediction limits using a global dataset. It addresses a gap in understanding complex patterns across diverse cultural contexts. Methods: We analyzed 128,184 records from the ISCWeB survey (ages 6–14, 35 countries), with self-reported data on sports frequency, emotional states, and family support. To ensure cross-country generalizability, we used GroupKFold CV (grouped by country) and leave-one-country-out (LOCO) validation, yielding mean R² = 0.45 ± 0.05, confirming robustness beyond cultural patterns, SHAP for interpretability, and bootstrapping for error estimation. No pre-registration was required for this secondary analysis. Results: XGBoost and LightGBM outperformed OLS, achieving R² up to 0.504 in restricted datasets (sensitivity excluding affective leakage: R² = 0.35), with sports-related variables (e.g., exercise frequency) associated positively with SWB predictions (SHAP values: +0.15–0.25; incremental ΔR² = 0.06 over demographics/family/school base). Using test–retest reliability from literature (r = 0.74), the estimated irreducible RMSE reached 0.941; XGBoost achieved RMSE = 1.323, approaching the predictability bound with 68.1% of explainable variance captured (after noise adjustment). Partial dependence plots showed linear associations with exercise without satiation and slight age decline. Conclusions: ML improves SWB prediction in children, highlighting associations with sports participation, and approaches predictable variance bounds. These findings suggest potential for data-driven tools to identify patterns, such as through physical literacy pathways, informing physical activity interventions. However, longitudinal studies are needed to explore causality and address cultural biases in self-reports. Full article

This study examines how strategic human resource management (SHRM) practices in pharmaceutical multinational enterprises (MNEs) operating in Greece are influenced by digital innovation and social dialogue. Structured questionnaires were distributed to 82 participants across seven large pharmaceutical MNEs in Greece, using purposive and stratified sampling to capture perspectives from senior managers, middle managers, and specialized employees. Findings indicate that while digital tools are present in SHRM systems, their integration remains functional rather than strategic. Social dialogue mechanisms exist but exert limited influence on decision-making. The study proposes that SHRM models—economies like Greece (characterized by medium-level competitiveness performance)—must be recontextualized to account for organizational learning capacities, and the strategic alignment between innovation, management, and social dialogue. We suggest that MNEs in the pharmaceutical sector should invest in integrated SHRM systems that prioritize cross-functional collaboration, localized adaptability, and participatory governance. Full article

Workplace health and safety issues have long plagued the construction industry. While safety efforts have traditionally focused on physical risks, increasing attention is being paid to mental health and work-related stressors, which can negatively affect both productivity and safety. In Saudi Arabia, the construction sector presents a unique context because of its highly diverse, multinational workforce. Workers of different nationalities often operate on the same job site, leading to potential communication barriers, cultural misunderstandings, and inconsistent safety practices, all of which may amplify stress and safety risks. This research aims to investigate the influence of work-related stressors on construction workers’ safety in Saudi Arabia and identify which stressors most significantly contribute to the risk of injury. A structured questionnaire was distributed to 349 construction workers across 16 job sites in Saudi Arabia. The survey measures ten key stressors identified in the literature, including job site demand, job control, job certainty, skill demand, social support, harassment and discrimination, conflict with supervisors, interpersonal conflict, and job satisfaction. Data were analyzed using logistic regression and Pearson correlation to examine relationships between stressors and self-reported injuries. The findings indicated that work-related stressors significantly predict workplace injury. While the first regression model showed a modest effect size, it was statistically significant. The second model identified job site demand and job satisfaction as the most influential predictors of injury risk. Work-related stressors, particularly high job demands and low job satisfaction, substantially increase the likelihood of injury among construction workers. These findings emphasize the importance of incorporating psychosocial risk management into construction safety practices in Saudi Arabia. Future studies should adopt longitudinal designs to explore causal relationships over time and include qualitative methods such as interviews to gain a deeper understanding. Additionally, factors such as nationality, organizational policies, and management style should be investigated to better understand their moderating effects on the stress–injury relationship. Full article

Market equilibrium models are essential tools within classical economic theory for analyzing the interaction between supply and demand. However, traditional formulations are often based on deterministic relationships and assume the existence of perfect information, an assumption that diverges from real-world conditions, which are characterized by ambiguity and uncertainty. This article addresses the modeling of multiproduct supply and demand equilibrium under uncertainty, using systems of linear equations with fuzzy coefficients and/or variables. By applying fuzzy set theory, the model incorporates the inherent vagueness of supply and demand functions, enabling a more flexible and realistic representation of market behavior. The proposed methodology involves reformulating the equilibrium conditions through fuzzy arithmetic and examining the existence and nature of fuzzy solutions. The theoretical proposals are illustrated through a simplified real-world case involving a Colombian multinational company, demonstrating their applicability and effectiveness. Full article

Background/Objectives: Anti-tumour necrosis factor (anti-TNF) medications were historically commonly prescribed as the first-line biologic treatment for chronic inflammatory pouch conditions. However, their use in these conditions is mainly based on retrospective studies of relatively small numbers of patients with short follow up periods. We aimed to describe the long-term outcomes of first-line anti-TNF therapy in a large, multi-centre, multi-national patient cohort with chronic inflammatory pouch conditions. Methods: This was an observational, retrospective, multi-centre, multi-national study. We included patients with chronic inflammatory pouch conditions initially treated with anti-TNF drugs infliximab (IFX) or adalimumab (ADA), who had a follow up of at least 1 year. The primary outcome was anti-TNF treatment persistence, defined as continuation of anti-TNF throughout the study period. The secondary outcome was pouch failure, defined by the need for a defunctioning ileostomy or pouch excision. Results: We recruited 98 patients with chronic inflammatory pouch conditions initially treated with anti-TNF medications—63 (64.3%) treated with IFX and 35 (35.7%) treated with ADA. Average follow up length was 94.2 months (±54.5). At the end of the study period only 22/98 (22.4%) patients were still on anti-TNF treatment. In those in whom the first-line anti-TNF was discontinued, the median time to discontinuation was 12.2 months (range 5.1–26.9 months). The most common cause for anti-TNF discontinuation was lack of efficacy despite adequate serum drug levels and absence of anti-drug antibody formation (30 patients, 30.6%). Loss of response due to anti-drug antibody formation was the cause for discontinuation in 18 patients (18.4%), while 12 patients (12.2%) stopped treatment because of adverse events or safety concerns. Out of the 76 patients discontinuing anti-TNF treatment, 34 (34.7% of the cohort) developed pouch failure, and 42 (42.8% of the cohort) are currently treated with a different medical therapy. Conclusions: First-line anti-TNF therapy for chronic pouch inflammatory conditions is associated with low long-term persistence rates. This is due to a combination of lack of efficacy and adverse events. A significant percentage of patients initially treated with anti-TNF therapy develop pouch failure. Full article

Due to globalization, companies are exposed to a culturally diversified workforce; therefore, great emphasis is placed on identifying the most effective leadership style that would be able to manage such a workforce. Although numerous studies have attempted to identify successful leadership styles in different cultural settings, none have focused on the perceptions of top managers who work in multinational corporations (MNCs) in culturally diversified surroundings. Thus, our research attempts to identify the most preferred leadership style and characteristics from the perspective of top managers in MNCs in the United Arab Emirates (UAE). The 13 leadership characteristics analyzed in this study were generated from the 21 characteristics found by Global Leadership and Organizational Behavior Effectiveness (GLOBE) research. The participants, top managers in MNCs, needed to evaluate leadership styles by considering leadership characteristics. To ensure the objectiveness of the study, we analyzed their answers by applying the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicated that the most preferred leadership characteristics were visionary, inspirational, collaborative team-oriented, and performance-oriented. Moreover, the transformational leadership style emerged as the most preferred leadership style. The study’s findings show that top managers believe that employees in MNCs in the UAE seek a leader with a vision who will inspire, motivate, and help them fulfill their true potential. Full article

Objectives: Febrile neutropenia is a common oncologic emergency with significant morbidity and mortality. Although the MASCC (Multinational Association for Supportive Care in Cancer) score is widely used for risk stratification, its limited sensitivity and lack of laboratory parameters reduce its prognostic utility. This study aimed to evaluate whether incorporating serum lactate and CRP measurements into the MASCC score enhances its predictive performance for hospital admission and the 30-day mortality. Methods: This retrospective diagnostic accuracy study included adult patients diagnosed with febrile neutropenia in the emergency department of a tertiary care hospital between January 2021 and December 2024. The original MASCC score was calculated, and three modified models were derived: the MASCC-L (lactate/MASCC), MASCC-C (CRP/MASCC) and MASCC-LC models (CRP × lactate/MASCC). The predictive accuracy for hospital admission and the 30-day all-cause mortality was assessed using ROC analysis. Results: A total of 269 patients (mean age: 67.6 ± 12.4 years) were included; the 30-day mortality was 3.0%. The MASCC-LC model demonstrated the highest discriminative ability for mortality prediction (area under the curve (AUC): 0.995; sensitivity: 100%; specificity: 98%). For hospital admission prediction, the MASCC-C model had the highest specificity (81%), while the MASCC-LC model showed the best balance of sensitivity and specificity (both 73%). All the modified models outperformed the original MASCC score regarding both endpoints. Conclusions: Integrating lactate and CRP measurements into the MASCC score significantly improves its prognostic accuracy for both mortality and hospital admission in febrile neutropenic patients. The MASCC-LC model, relying on only three objective parameters, may serve as a practical and efficient tool for early risk stratification in emergency settings. Full article

Background/Objective: Sepsis is a leading cause of death in noncoronary intensive care units (ICUs). Fluids for intravascular resuscitation include crystalloids and colloids. There is extensive clinical evidence on colloid use, but large trials comparing gelatine with crystalloid regimens in ICU and septic patients are lacking. This study aimed to determine whether early, protocol-driven volume resuscitation using a gelatine-based regimen achieves hemodynamic stability (HDS) more rapidly than a crystalloid-based regimen in septic patients. Methods: This prospective, controlled, randomised, double-blind, multinational phase IV study compared two parallel groups of septic patients receiving a gelatine-based regimen (Gelaspan^® 4% and Sterofundin^® ISO, B. Braun Melsungen AG each, at a 1:1 ratio) or a crystalloid regimen (Sterofundin^® ISO). Primary endpoint was time to first HDS within 48 h after randomisation. Secondary endpoints included fluid overload, fluid balance, and patient outcomes. Results: 167 patients were randomised. HDS was achieved after 4.7 h in the gelatine group and after 5.8 h in the crystalloid group (p = 0.3716). The gelatine group had a more favourable fluid balance at 24 h (medians: 3463.00 mL vs. 4164.00 mL; p = 0.0395) and less fluid overload (medians: 4296.05 vs. 5218.75%; p = 0.0217). No differences were observed in serious adverse events or mortality. Conclusions: The study provided clinical evidence of balanced gelatine solution for volume resuscitation in septic patients, although it was terminated prematurely. The early and protocol-based administration of gelatine was safe and effective in the enrolled patient population. Time to HDS was not different between groups but the gelatine-based regimen led to better fluid balance and less fluid overload. Full article

This study examined globalisation, de-globalisation, the combination, and the future of value chains to ascertain which would be best for the future of value chains. The study used a cross-sectional survey of 277 randomly selected employees of multinational manufacturing firms in Nigeria. The data were analysed using structural equation model path diagram techniques. The results indicate that de-globalisation and the combination of globalisation and de-globalisation have direct and indirect significant relationships with the future of value chains, but globalisation does not have any direct significant relationship with the future of value chains but has an indirect significant relationship with the future of value chains. In addition, supply chain management significantly mediates the relationships among globalisation, de-globalisation, the combination, and the future of value chains. By establishing a significant association between the combination and the future of value chains, the study departs from future studies whose results are largely situated on the bipolar ends of a continuum. The study makes significant contributions to the traditional theory of trade protectionism, endogenous growth theory, and institutional theory, as well as to practice. Full article

A process evaluation was embedded in the multinational Music Interventions for Dementia and Depression in ELderly care (MIDDEL) trial to better understand barriers and facilitators for implementing music-based interventions (MBIs). Stakeholders from 66 care home units across 5 countries completed a survey at baseline (n = 229) and after a six-month intervention period (n = 101), comparing expectations and experiences between countries, intervention groups, and stakeholders. MBIs were evaluated and found to be relevant and feasible. Barriers include a lack of support, turnover among employees, and a lack of motivation. Facilitators include individual stakeholders who proactively facilitate and stimulate implementation, as well as the presence of stable, well-functioning teams, clear communication, and adhering to project plans. Fewer barriers than expected related to care staff workload and the time needed for implementing new MBIs in care homes. MBIs can be beneficial for people with dementia, yet implementation in care homes can be challenging due to contextual factors. Involving stakeholders in key positions is essential: care home managers are pivotal for policy-making and the sustainable adoption of MBIs, whereas the commitment and the involvement of care staff are needed for day-to-day implementation. Insight into these barriers to and facilitators of implementation can contribute to the interpretation of trial results. Full article

Efficient and consistent DNA extraction is crucial for genotyping but often hindered by the limitations of traditional manual processes, which are labour-intensive, error-prone, and costly. We introduce a semi-automated, robotic-assisted DNA extraction (RoboCTAB) tailored for large-scale plant genotyping, leveraging advanced yet affordable liquid-handling robotic systems. The protocol/workflow integrates a CTAB extraction protocol specifically adapted for a robotic liquid-handling system, making it compatible with high-throughput genotyping techniques such as SNP genotyping and sequencing. Various plant parts (leaves, roots, manual seed chip) were explored as the source material for DNA extractions, with the aim of identifying the tissue best suited for collection on a large scale. Young roots (radicle) proved the easiest to harvest at scale, while the harvest of leaves and seed chips were more laborious and error-prone. DNA yield and quality from both leaves and roots (but not seed chips) were similar and sufficient for downstream analysis. Interestingly, root tissue could still be extracted from imbibed seeds, even if the seeds failed to germinate, thus proving useful for DNA extraction. Cost analysis indicates significant savings in labour costs, highlighting the approach’s suitability for large-scale projects. Quality assessments demonstrate that the robotic process yields high-quality DNA, maintaining integrity for downstream applications. This semi-automated DNA extraction system represents a scalable, reliable solution for large-scale genotyping that is accessible to many users who cannot implement highly sophisticated and costly systems as are known to exist in large multinational seed companies. RoboCTAB, a low-cost, optimized method for high-throughput DNA extraction, minimizes the risk of cross-contamination. RoboCTAB is capable of processing up to four 96-well plates (384 samples) simultaneously in a single run, improving cost-efficiency and providing seamless integration with laboratory workflows, potentially setting new standards for efficiency and quality in DNA processing and sequencing at scale. Full article

Dietary choices and patterns have enormous consequences along the lines of individual, community, and planetary health. Excess meat consumption has been linked to chronic disease risk, and at large scales, the underlying industries maintain a massive environmental footprint. For these reasons, public and planetary health experts are unified in emphasizing a whole or minimally processed plant-based diet. In response, the purveyors of ultra-processed foods have added “meat alternatives” to their ultra-processed commercial portfolios; multinational corporations have been joined by “start-ups” with new ultra-processed meat analogues. Here, in our Viewpoint, we revisit the 1990s food industry rhetoric and product innovation, a time in which multinational corporations pushed a great “low-fat transition.” We focus on the McLean Deluxe burger, a carrageenan-rich product introduced by the McDonald’s Corporation in 1991. Propelled by a marketing and media-driven fear of dietary fats, the lower-fat burger was presented with great fanfare. We reflect this history off the current “great protein transition,” a period once again rich in rhetoric, with similar displays of industry detachment from concerns about the health consequences of innovation. We scrutinize the safety of carrageenan and argue that the McLean burger should serve as a cautionary tale for planetary health and 21st century food innovation. Full article