Due to the rapid population growth, industrialization, and climate change, freshwater scarcity is turning out to be one of the most acute global problems. Traditional desalination methods such as reverse osmosis (RO) and multi-stage flash distillation are efficient; nevertheless, traditional desalination methods are energy consuming and unaffordable in remote and low-income areas. The solar distillation stills offer an alternative that is sustainable and environmentally friendly since they utilize the large amount of solar energy to make potable water by using natural evaporation and condensing mechanisms. The present review paper provides a critical analysis of the latest advances in the solar distillation technologies with a particular focus on the design innovations, the approaches toward the performance improvement, and the way to integrate the technology with other renewable energy sources. Many design configurations such as active, passive, stepped, tubular, and multi effect systems are examined to determine their characteristics of operation. The use of advanced materials, including nanofluids, phase-change materials, and selective coatings, to enhance thermal performance and yield water is also explained. The review also assesses economic viability, environmental advantages and practical implementation in rural arid and disaster-prone areas. The solar distillation with passive techniques improves efficiency of 10–30
The United Arab Emirates (UAE) is steadfast in its commitment to sustainability, reflected in its ambitious Green Agenda 2030. Central to these objectives is the need to develop a workforce skilled in green careers that can drive the country toward a sustainable future. This study investigated the types of green careers that are critical to the UAE’s sustainability targets, including renewable energy, sustainable construction, waste management, and environmental consultancy. By identifying these key areas, the research provided a framework for integrating sustainability into the Emiratization initiative. Furthermore, the study then determined the types of programs and training required to develop the competencies needed for a skilled workforce in these green careers. The authors analyzed existing policies and programs and recommended adaptations to support the training and development of Emiratis in these fields, ensuring that the local workforce is equipped with the necessary skills and knowledge. Through this comprehensive analysis, the study aimed to provide actionable recommendations for policymakers and businesses. These recommendations highlighted how the Emiratization program can be leveraged to meet both employment and sustainability objectives, demonstrating that fostering green careers through Emiratization can enhance the employability of Emirati citizens while achieving broader environmental and economic goals.
This paper investigates the effectiveness of integrating a matrix converter (MC) into a wind turbine system based on a Doubly Fed Induction Generator (DFIG), focusing on the indirect control of active and reactive power using fractional regulators. To evaluate the performance of this architecture, three distinct configurations are studied and compared: (i) a conventional system using an AC-DC-AC converter combined with a conventional PI regulator, (ii) a structure integrating a matrix converter with a PI regulator, and (iii) an advanced combination of MC with a fractional regulator. Simulation results demonstrate that this latter solution offers significant improvements in dynamic performance, more precise regulation of active and reactive power, and higher energy efficiency than the other two methods.
AIM/OBJECTIVE:The research investigated the relationship between servant leadership (SL) and innovative work behavior (IWB) among nurses in Pakistan. It also formulates and hypothesizes a serial mediation model, integrating trust-in leadership (TL) and knowledge sharing (KS) as mediated variables. BACKGROUND:Nurses' IWBs are critical for improving patient healthcare quality. Despite their significance, scant research has examined how leadership styles, especially SL, can trigger such behavior in nursing. The paper seeks to fill this gap by examining how SL can enhance innovation among nurses, while accounting for the mediating variables of TL and KS within the Pakistani healthcare system. DESIGN/METHODOLOGY/APPROACH:A quantitative, cross-sectional study was conducted, involving 269 nurses from hospitals in Islamabad and Rawalpindi. A time-lagged approach to data collection was used to reduce common method bias. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed to test the proposed hypothesized relationships using SPSS (version 27) and AMOS (version 23). RESULTS:The hypotheses revealed a significant impact of the constructs. It was discovered that SL has both direct and indirect positive influences on IWB among nurses through TL and KS. Moreover, the findings support high serial mediation, implying that SL has a positive effect on IWB by first building trust, which then leads to knowledge sharing. CONCLUSIONS:The paper contributes to the literature on servant leadership and innovative work behavior by examining mediating mechanisms within nursing teams in Pakistan. The paper also offers practical implications for nurse managers and hospital administrators on fostering a culture of innovation and trust among nurses. CLINICAL RELEVANCE:The findings offer actionable insights for nursing management by demonstrating that adopting a servant leadership style can directly and indirectly enhance nurses' innovative work behaviors. By prioritizing trust-building and fostering a culture of knowledge sharing, nurse managers can stimulate innovation, which is critical for improving patient care quality and overall healthcare outcomes.
Glioblastoma (GBM) is an aggressive tumor type known to recur after maximal safe surgical resection followed by concurrent radiation therapy (RT) and chemotherapy (temozolomide—TMZ), and adjuvant TMZ maintenance chemotherapy. It exhibits high intratumor heterogeneity within a single specimen, and thus clinical management remains a challenge due to its rapid progression and high recurrence rate. Machine learning algorithms are currently being implemented in biomarker discovery to develop accurate predictive models that can guide clinical decision making. Emerging evidence identifies metabolomics as a critical player in understanding tumor metabolism and progression. Machine learning computation models have been instrumental in GBM classification and biomarker discovery, as well as the evaluation of tumor staging. Metabolomic profiling of biogenic amines in the setting of surgery, chemoradiation, and understanding relapse also suggests a coordination between metabolic pathways and tumor stage. Many challenges in machine learning and metabolomics-based approaches for disease classification remain due to the dimensionality of datasets, as well as identifying more streamlined panels of metabolite biomarkers. The purpose of this review is to showcase the recent developments in the applications of machine learning in metabolomics as a promising approach to enhancing the biomarker discovery process for future classification and interpretation of patient response to therapies for GBM management in the clinical setting. It also presents the major challenges of implementing machine learning approaches in GBM management and its future directions.
