Search Results (2,914)

Thymoquinone (TQ), the active compound in Nigella sativa (black seed), has shown promising effects against cancer in many laboratory studies. In this review, we explore how TQ works on different aspects of cancer, from stopping cancer cell growth and spread, to triggering cancer cell death, reducing inflammation, and helping the immune system fight back. We also highlight how TQ may overcome one of the biggest problems in cancer treatment—chemoresistance. When used together with common treatments like chemotherapy, radiation, or immunotherapy, TQ has been shown to improve their effects and reduce harmful side effects in preclinical models. Our review further discusses how TQ affects cancer stem cells, the tumor environment, and gene regulation through epigenetics. While these findings are encouraging, the lack of human studies remains a major gap. We also address TQ’s limited absorption and suggest ways to improve its delivery in the body, such as using nanoparticles or other carriers. Through this review, we aim to show the wide-ranging potential of TQ as a natural compound that may help make cancer treatments more effective and better tolerated. We call for clinical studies to take this research further and bring TQ closer to use in real-world cancer care. Full article

As the transformative influence of novel technologies sweeps across industries, organisations are called upon to position their staff in the equally dynamic operational environment, which includes embedding technical and legal communication skills in their training programs. For many organisations, internal and external communication of data modelling and related concepts, reporting, and monitoring still pose major challenges. The aim of this research is to develop an effective data training framework for learners with or without mathematical or computational maturity. It also addresses subtle aspects such as the legal and ethical implications of dealing with organisational data. Data was collected from a training course in Python, delivered to government employees in different departments in the United Arab Emirates (UAE). A structured questionnaire was designed to measure the effectiveness of the training program using Python, from the employees’ perspective, based on three key attributes: their personal characteristics, professional characteristics, and technical knowledge. A descriptive analysis of aggregations, deviations, and proportions was used to describe the data attributes gathered for the study. The main findings revealed a huge knowledge gap across disciplines regarding the core skills of big data analytics. In addition, the findings highlighted that previous knowledge about statistical methods of data analysis along with prior programming knowledge made it easier for employees to gain skills in data analytics. While the results of this study showed that their training program was beneficial for the vast majority of participants, responses from the survey indicate that providing a solid knowledge of technical communication, legal and ethical aspects would offer significant insights into the big data analytics field. Based on the findings, we make recommendations for adapting conventional data analytics approaches to align with the complexity or the attainment of the non-orthogonal United Nations Sustainable Development Goals (SDG). Associations of selected responses from the survey with some of the key data attributes indicate that the research highlights vital roles that technology and data-driven skills will play in ensuring a more prosperous and sustainable future for all. Full article

Background: Antibiotic resistance is a significant health problem in healthcare settings, especially intensive care units (ICUs), where patients are critically ill. This study aims to identify the bacterial profile and antibiotic resistance patterns of Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter, and Escherichia coli (ESKAPEE) in blood specimens collected from adult patients admitted to the ICUs of public hospitals in Abu Dhabi, United Arab Emirates. The World Health Organization lists these pathogens as priority pathogens that greatly threaten humans. Methods: This cross-sectional study used routinely collected data through the AMR surveillance system between 2018 and 2022. Results: A total of 838 culture-positive blood specimens were reported during the study period, and 965 ESKAPEE pathogens were isolated. The most frequently isolated bacteria were Klebsiella pneumoniae (31%), Escherichia coli (22%), and Staphylococcus aureus (20%). Acinetobacter baumannii exhibited high resistance to Amikacin (81%), Meropenem (72%), and Imipenem (87%). Escherichia coli demonstrated resistance to Imipenem (42%) and Cefotaxime (54%). Klebsiella pneumoniae showed resistance to Imipenem (37%) and Cefotaxime (39%). Staphylococcus aureus showed resistance to Penicillin G (80%), Oxacillin (4%), and Ciprofloxacin (54%). Conclusions: The study showed a high prevalence of resistance in the most frequently isolated ESKAPEE pathogens in adult ICU patients. This brings into focus the need for appropriate infection control measures and strong antibiotic stewardship programs. The findings of the study support the ongoing efforts to deploy a better diagnostic tool for rapid pathogen identification, which is key in the targeted management of patients with bloodstream infection, especially in ICUs. Full article

An intense rainfall event affected the United Arab Emirates (UAE) between 15 and 16 April 2024. This study investigated the atmospheric conditions responsible for the formation of large convective storms during this period. Specifically, we analyzed the atmospheric dynamics and large-scale flow that led to the development of a cut-off low-pressure system (COL) over the Arabian Peninsula on 15 April 2024, triggering a two-day period of intense precipitation over the UAE. Our findings indicate that the storms were driven by upper-air instability, a prolonged moisture influx from the monsoon system into the UAE, and the presence of a surface front. Some regions recorded over 200 mm of precipitation within this period, resulting in flash floods, infrastructure disruptions, and significant impacts on the local population. The unusual development of the rainfall event was linked to the displacement of the subtropical jet (STJ), which facilitated the formation and intensification of a COL traversing the region. Full article

Objectives: The ability of clinicians to predict prolonged mechanical ventilation (MV) in patients with acute hypoxemic respiratory failure (AHRF) is inaccurate, mainly because of the competitive risk of mortality. We aimed to assess the performance of machine learning (ML) models for the early prediction of prolonged MV in a large cohort of patients with AHRF. Methods: We analyzed 996 ventilated AHRF patients with complete data at 48 h after diagnosis of AHRF from 1241 patients enrolled in a prospective, national epidemiological study, after excluding 245 patients ventilated for <2 days. To account for competing mortality, we used multinomial regression analysis (MNR) to model prolonged MV in three categories: (i) ICU survivors (regardless of MV duration), (ii) non-survivors ventilated for 2–7 days, (iii) non-survivors ventilated for >7 days. We performed 4 × 10-fold cross-validation to validate the performance of potent ML techniques [Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF)] for predicting patient assignment. Results: All-cause ICU mortality was 32.8% (327/996). We identified 12 key predictors at 48 h of AHRF diagnosis: age, specific comorbidities, sequential organ failure assessment score, tidal volume, PEEP, plateau pressure, PaO₂, pH, and number of organ failures. MLP showed the best predictive performance [AUC 0.86 (95%CI: 0.80–0.92) and 0.87 (0.80–0.93)], followed by MNR [AUC 0.83 (0.76–0.90) and 0.84 (0.77–0.91)], in distinguishing ICU survivors, with non-survivors ventilated 2–7 days and >7 days, respectively. Conclusions: Accounting for ICU mortality, MLP and MNR offered accurate patient-level predictions. Further work should integrate clinical and organizational factors to improve timely management and optimize outcomes. This study was initially registered on 3 February 2025 at ClinicalTrials.gov (NCT06815523). Full article

Oil production generates approximately 250 million barrels of produced water (PW) daily, nearly three times the volume of oil, with salinity levels reaching up to 300,000 ppm. Improper management of this wastewater causes significant environmental degradation, including soil salinization and aquatic toxicity. To address these impacts, this study applies circular economy (CE) principles to PW management through flash vaporization and resource recovery. Implementing this approach enables 85–90% water recovery and reduces salinity to below 1000 ppm, allowing reuse for irrigation. Simultaneously, residual brine processed via evaporation ponds yields 15–25% potash (KCl) and 30–40% halite (NaCl), thereby transforming waste into valuable products. As a result, the integrated CE process can reduce wastewater disposal by 80%, cut greenhouse gas emissions by 25–30%, and lower treatment costs by 20–35%, while generating additional revenue of $150–300 per ton of recovered potash. These outcomes demonstrate that adopting CE strategies in PW management not only mitigates environmental degradation but also strengthens economic resilience and resource efficiency. The framework offers a scalable pathway for achieving the UN Sustainable Development Goals (SDG 6 and 12) and advancing sustainability within the oil and gas industry. Full article

Objective: Pediatric cancer, though less prevalent than adult malignancies, constitutes a significant public health concern due to its long-term effects on survival, development, and quality of life. This study aimed to investigate spatial patterns and temporal trends of pediatric cancer in Romania over a ten-year period (2008–2017), identifying persistent and emerging geographic hotspots using Geographic Information Systems (GIS)–based modelling and spatial statistics. Methods: A national pediatric cancer registry provided by the Ministry of Health was analyzed for cases among individuals aged 0–18 years, categorized by administrative-territorial units (ATUs), ICD-10 codes, sex, and year. Spatial indicators of persistence (recurrent prevalence across multiple years) and continuity (uninterrupted recurrence) were computed. Hotspot analysis was conducted using Local Moran’s I, and trend patterns were assessed through temporal modeling. Additionally, fractal and complexity metrics were applied to characterize the spatial structure and heterogeneity of cancer persistence and continuity across regions. Results: Although national pediatric cancer prevalence exhibited a modest decline from 3.57‰ in 2008 to 3.44‰ in 2017, GIS-based spatial modeling revealed stable high-risk clusters in Central and South-Eastern Romania, particularly in historically industrialized counties such as Hunedoara, Prahova, and Galați. These correspond to regions with past heavy industry and chemical pollution. Male children presented a higher frequency of malignant tumors (48,502 cases in males vs. 36,034 in females), while benign and uncertain-behavior neoplasms increased more prominently among females (from 3847 to 4116 cases, compared with 3141 to 3199 in males). Several rural localities showed unexpected prevalence spikes, potentially associated with socioeconomic deprivation, limited health literacy, and reduced access to pediatric oncology services. Regional disparities in diagnostic and reporting capacities were also evident. Conclusion: GIS-based spatial epidemiology proved effective in revealing localized, sex-specific, and persistent disparities in pediatric cancer across Romania. The integration of spatial indicators and complexity metrics into national cancer control programs could strengthen early detection, optimize resource allocation, and reduce health inequities. These findings highlight the value of combining geospatial analysis and fractal modeling to guide evidence-based public health strategies for pediatric oncology. Full article

Employees are the fundamental source of a sustainable competitive advantage. Without the high quality of human capital, organisations cannot attain a competitive advantage that can be sustained over time. Employer branding is a strategy that focuses on engaging and attracting the best talent from the job market, which is crucial for sectors known for high employee turnover rates. In recent years, digital platforms and information technology systems have revolutionised employer branding by helping organisations connect with talent in more personal and innovative ways. In this study, we sought to explore and understand the role of LinkedIn in employer branding efforts and evaluate the benefits of using LinkedIn to brand an employer as the first choice for prospective employees. Additionally, the research proposes a LinkedIn-Integrated Employer Branding Model (LIEBM) that incorporates LinkedIn strategies. Qualitative data were collated from the recruiting heads of the leading four- and five-star hotels in Al Ain, United Arab Emirates. The findings indicate that LinkedIn is widely used to improve an organisation’s employer brand image through various strategies. The results also demonstrate that employing LinkedIn strategies enhances the benefits of employer branding, contributing to actionable insights in the hotel industry to make excellent decisions at pre-recruitment, recruitment and selection stages. Full article

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification. Full article

The assessment of forest health and terrain usability is closely tied to slope accessibility. Current methods for evaluating terrain accessibility based solely on slope characteristics often lack precision and fail to capture the combined effects of topography and vegetation. This study introduces succolarity, together with succolarity reservoir and delta (Δ) succolarity, as fractal-based measures for assessing undeveloped land accessibility. The analysis focused on two test areas: the Ceahlău Mountains and the Blaj–Vulpăr Hills. Results revealed lower accessibility values for the Ceahlău Mountains (0.01 to 0.23 for slopes of 0–5° and 0–30°) compared to the Blaj–Vulpăr Hills (0.035 to 0.598 for the same ranges). These significant contrasts demonstrate that terrain fragmentation and compact forests act as decisive constraints, with slope predominating in mountains and vegetation in hilly areas. The findings are valuable for environmental agencies, emergency services, and research groups studying land morphology and mobility. Practical applications include infrastructure planning, sustainable land-use management, and strategic operations in remote terrains. Incorporating additional datasets (e.g., hydrographic networks, seasonal vegetation) and refining methodologies will further enhance succolarity-based assessments, supporting sustainable development in challenging environments. Full article

Experimental studies published to date on the effects of silver nanoparticles (AgNPs) on plants have yielded highly contradictory results: reported outcomes range from growth inhibition to stimulation. The objective of this research was to test the hypothesis that the ontogenetic stage at the time of exposure to AgNPs is a key determinant of both the qualitative profile and quantitative magnitude of plant responses. For this purpose, laboratory seed priming and small-plot field experiments with wheat plants (Triticum aestivum L.) treated with stabilized dispersions of AgNPs at 1–100 mg∙L⁻¹ were conducted. It was shown that seed priming with low concentrations of AgNPs (1–5 mg∙L⁻¹) did not affect wheat seedling growth, whereas dispersions at ≥25 mg∙L⁻¹ suppressed development. In agreement, antioxidant enzyme activities (POD, CAT, PPO) increased at 1–5 mg·L⁻¹ and decreased at 100 mg·L⁻¹. By contrast, foliar treatments of field-grown wheat increased plant population density, plant height, spike structure metrics, and grain yield. The optimal regimen—three foliar applications at 5 mg·L⁻¹—increased grain yield by 12.1% from 5.89 t·ha⁻¹ to 6.60 t·ha⁻¹. At low doses of AgNPs, activities of peroxidase, catalase, and polyphenol oxidase in seedlings tissues increased, indicating activation of nonspecific defense mechanisms; at higher concentrations, activities of these enzymes decreased, indicating antioxidant system exhaustion and dysfunction. The findings demonstrate dose- and stage-dependent effects and corroborate the central role of the developmental stage of wheat in determining responses to AgNPs, indicating opportunities to optimize stage-aware, low-dose application regimes to enhance productivity while minimizing phytotoxic risk. Full article

In this paper, the optimization of a harmonic-excited synchronous machine (HESM) is carried out. A two-phase harmonic exciter winding of the HESM provides brushless excitation and sufficient starting torque at any rotor position. The HESM under consideration is intended to be used for applications requiring speed control, especially in the field-weakening region. The novelty of the proposed approach is that a two-level optimization based on a two-stage model is used to reduce the computational burden. It includes a finite-element model that takes into account only the fundamental current harmonic (basic model). Using the output of the basic model, a reduced-order model (ROM) is parametrized. The ROM considers pulse-width-modulated components of the inverter output current, zero-sequence current injected into the stator winding, and harmonic excitation winding currents. A two-level optimization technique is developed based on the Nelder–Mead method, taking into account the significantly different computational complexity of the basic and reduced-order models. Optimization is performed considering two operating points: base and maximum speed. The results show that an optimized design provides significantly higher efficiency and reduced inverter power requirements. This allows the use of more compact and cheaper power switches. Therefore, the advantage of the presented approach lies in the computationally effective optimization of HESMs (optimization time is reduced by approximately three orders of magnitude compared to calculations using FEA alone), which enhances HESMs’ performance in various applications. Full article

This study aims to extend the understanding of groundwater level dynamics in the Abu Dhabi Emirate by evaluating the performance of two interpolation models, local polynomial interpolation (LPI) and exponential ordinary kriging (EXP-OK), over a 20-year period. These models were selected for their demonstrated effectiveness in groundwater studies, with LPI offering strong local adaptability to spatial variability and EXP-OK providing robust geostatistical modeling for regional patterns. This study also aims to assess the performance of the two interpolation models in identifying missing groundwater level measurements to accurately estimate groundwater storage. The evaluation of the two models is conducted using ArcGIS and IBM-SPSS statistics, including cross-validation, descriptive statistics and exploratory spatial data analysis (ESDA). The findings revealed that both LPI and EXP-OK are effective in analyzing groundwater fluctuations in the study area, with LPI demonstrating a slight edge in predictive accuracy. The ability of the LPI to capture local data variations resulted in a smoother representation of groundwater level data. Owing to its superior performance, the LPI was selected for the estimation of groundwater storage. The study reports that the average change in groundwater storage over the study period could range from −0.066 to −2.112 cubic meters per square meter of aquifer area. These findings emphasize the importance of continuous monitoring and analysis for sustainable water resource management in the study area. Full article

We present an example of how a doctoral network can bring together multidisciplinary expertise and novel scientific advances in atmospheric dust. This network (Dust-DN) has started operations and is a strategic alliance of high-profile partners, able to leverage unique facilities for atmospheric research and innovative space missions. The network aims to improve our understandings of dust processes and microphysics, identify the signature of source regions, address the socio-economic impacts of dust transport and improve the quantification of the role of dust in the climate system. The first results have already been achieved and are shown here, and many more are expected to follow. Full article

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions. Full article