Effective Resource Management Research Articles

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

Artificial intelligence based succored power prediction in stand alone PV system

ABSTRACT Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management.

Abstract PO4-13-04: Added Value from Patient Advocates in a Translational Working Group: the COMET Study

Abstract Introduction: A phase III multicenter prospective randomized clinical trial called “Comparing an Operation to Monitoring, with or without Endocrine Therapy (COMET)” (NCT02926911) assesses the risks/benefits of active monitoring (AM) versus surgery for women with low-risk ductal carcinoma in situ (DCIS). It is funded by the Patient-Centered Outcomes Research Institute and enrolled 997 women between 06/30/17 and 01/13/23. Research Patient Advocates (PAs) have been embedded from the study design stage and continue to provide guidance as part of the COMET leadership team. The COMET biobank is funded by independent foundations and is managed through monthly Translational Working Group (TWG) meetings. The TWG includes PAs, clinicians, pathologists, radiologists, and researchers from multiple institutions who discuss topics like categorization of biospecimens into discovery/validation sets; development of a pathology workflow/sample tracking process; use of small samples; and potential areas of future research/technologies that may improve DCIS diagnostics, prognostics, and care management. Methods: The TWG leveraged the existing Alliance Foundation Trials, LLC biobank infrastructure and facilitates the collection, submission, storage and analysis/use of blood, tissue, and breast images collected at specified timepoints and stored in central tissue/image repositories. PAs are active and integral in the TWG, assisting with logistical issues (contracts, transfer agreements, resource requests); communicating with sites; identifying study topics and biomarkers relevant to diverse patients; providing guidance on commercial predictive/prognostic tests; promoting effective stewardship of samples; ensuring overall focus remains on advancing clinical utility; and reporting translational study results to trial participants who agreed to donate samples/images. Results: Over 90% of requested samples/images have been submitted by ˃85 sites. Each patient’s biospecimens and pathology images are linked and de-identified for research. Use of artificial intelligence (AI) is being considered to assist in sample review. TWG members have complementary areas of expertise/experience and promote active stewardship for effective management of these scarce resources. PAs have played key roles concerning equitable data-sharing and acceleration of data and material transfer agreements. They have also been active in development of standards for discovery/training sets, validation test sets; communication procedures for patient cases that progress to future breast events; ways to catalog technologies (e.g., multi-omics, AI); and in research proposal review. Consensus has been achieved regarding major issues such as authorship, biospecimen custodianship, intellectual property, and criteria relevant to patient needs. Logistical barriers, including data sharing and technicalities of biospecimen release, have been resolved. The TWG also played an integral role in resolving recruitment challenges to COMET by creating standard pathology eligibility criteria, resulting in evidence-based protocol amendments that increased accrual. A retrospective review of biospecimens has been performed to determine adequacy for ensuing correlative molecular and spatial profiling studies. Conclusion: PAs help the COMET TWG set policy, and oversee biospecimen/image collection, and biobank use and sharing. PA input also facilitates equitable, transparent research studies and technology development that can improve personalized decisions for surgery versus AM in women with low-risk DCIS. PAs in the TWG aim to integrate the diagnostic and prognostic tools developed as part of the COMET study into future patient care. Citation Format: Deborah Collyar, Desiree Basila, Thomas Lynch, Stuart Schnitt, Jeffrey Marks, Siri Strand, Terry Hyslop, Sunil Badve, Mark Watson, H. T. Carisa Le-Petross, Lars Grimm, Robert West, Anna Weiss, Anna Rapperport, Lorraine King, Rachel Factor, Marc Ryser, Ann Partridge, E Shelley Hwang, Alastair Thompson. Added Value from Patient Advocates in a Translational Working Group: the COMET Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-04.

Definition and characteristics of climate-adaptive cities: a systematic review

BackgroundCities, as frontline responders to climate change, necessitate a precise understanding of climate-adaptive features. This systematic review aims to define and outline the characteristics of climate-adaptive cities, contributing vital insights for resilient urban planning.MethodsThis systematic review, initiated on March 6, 2018, and concluded on August 26, 2021, involved reviewing multiple electronic databases based on the study's objectives. The Critical Appraisal Skills Program (CASP) tool was used for quality assessment and critical evaluation of articles retrieved through a comprehensive and systematic text search. Descriptive and thematic analyses were conducted to extract definitions, features, and characteristics of climate-adaptive cities.ResultsOut of 6104 identified articles, 38 articles met the inclusion criteria. In total, 20 definitions and 55 features for climate-adaptive cities were identified in this review. Codes were categorized into two categories and ten subcategories. The categories included definitions and features or characteristics of climate-adaptive cities.ConclusionA climate-adaptive city, as derived from the findings of this study, is a city that, through effective resource management, future-oriented planning, education, knowledge utilization, innovation in governance and industry, decentralized management, and low-carbon economy, leads to the adaptability, resilience, sustainability, and flexibility of the capacity of individuals, communities, institutions, businesses, and systems within a city against all climate change impacts and reduces their negative consequences.

The Influence of Supervisor, Teamwork, and Discipline on Employee Performance

The Influence of Supervisor, Teamwork, and Discipline on Employee Performance

Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study in Tam Ky City, Quang Nam Province, Vietnam

Changes in land use/land cover (LULC) play a critical role in effective natural resource management, monitoring, and development, particularly within the realm of urban planning. In the examination of Tam Ky city, Quang Nam province, Vietnam, spanning from 2000 to 2020, remote sensing and Geographic Information System (GIS) techniques were employed. The Landsat satellite data (Landsat 7 ETM+ for 2000, Landsat 5 TM for 2010, and Landsat 8 OLI for 2022) underwent analysis using the supervised classification method in ArcGIS 10.8 software to identify and categorize six primary LULC classes: water bodies, agriculture, settlements, vegetation, construction, and bare soil/rocks. The reliability of the classification was evaluated through k values, revealing high accuracy with values of 0.951, 0.953, and 0.950 for the years 2000, 2010, and 2020, respectively. Notable shifts in LULC were observed during the period from 2000 to 2020. The areas covered by vegetation and settlements expanded by 53 and 1300 ha, respectively, while water bodies, agriculture, construction, and bare soil/rocks experienced reductions of 466, 48, 413, and 425 ha, respectively. To facilitate a rapid assessment, the study also incorporated the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The trends identified in this study are consistently aligned with the results of the supervised classification. The identified changes in LULC pose a substantial environmental threat, and the study's outcomes serve as a valuable asset for future land use planning and management in the area. The method's high accuracy enhances the dependability of the results, making them crucial for well-informed decision-making and sustainable development initiatives.

Leadership Development Strategies in Organizations: The Role of Human Resource Management in Improving Effectiveness and Productivity

Effective human resource management strategies are key to sustainable business growth in a rapidly changing and competitive business environment. For organizations wishing to remain relevant and competitive in the global marketplace, the importance of leveraging innovation, environmental sustainability, and ethics and social responsibility in human resource management is of great concern. Therefore, the purpose of this research is to determine how the implementation of human resource management strategies that focus on innovation, sustainability, and ethics can help organizations achieve sustainable growth. This research was conducted using a descriptive-analytical approach by collecting data through literature review and content analysis of various relevant sources. The findings of this study revealed that HRM strategies based on innovation and sustainability help organizations to maximize human resource potential and create productive and sustainable work environments. In addition, the application of ethics and social responsibility in HR decision-making is a key pillar in creating an organizational culture of integrity. Organizations can achieve sustainable growth and sustainability by implementing this strategic approach.

Mendorong Motivasi Wirausaha UMKM untuk Memajukan Ekonomi Masyarakat di Kelurahan Joglo Kota Jakarta Barat

Micro, Small, and Medium Enterprises (MSMEs) are business entities managed by individuals, families, or small-scale business units, classified based on annual turnover, asset volume, and workforce. Effective human resource management is essential for MSMEs in systematically organizing finances, enabling business owners to understand their financial conditions and business developments. Easy access to information on assets, liabilities, equity, cash flow, and profit fluctuations provides crucial support for MSME entrepreneurs in making strategic business decisions. This activity not only motivates entrepreneurs to maintain business continuity but also guides them towards achieving desired business and economic goals. The drive to engage in entrepreneurship stems from a strong individual desire to realize self-potential through creative and innovative thinking, creating new products that have a social and economic impact on the local community. The courage to explore and implement new ideas is reinforced by strong entrepreneurial motivation, creating a positive cycle between motivation and action that strengthens the character of resilient and high-quality entrepreneurs.

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

The rapid pace of urbanization and the increasing concentration of populations in urban areas have generated a substantial demand for architectural structures, resulting in a significant increase in building stock and continuous material flows that interact with the environment. This study emphasizes the importance of high-spatial-resolution mapping of residential building stock for effective urban-construction resource management, planning, and waste management. Focusing on Xi’an as a case study, the research develops a comprehensive framework for mapping urban residential building stock by integrating diverse data dimensions, including temporal, spatial, network, and multi-attribute aspects. The findings indicate that between 1990 and 2020, approximately 4758 residential communities were established in central Xi’an. The analysis of seven key residential construction materials revealed that the building stock escalated from 1.53 million tons to 731.12 million tons, with a steady spatial expansion of material distribution. The study attributes this growth to factors such as population increase, economic advancement, and policy initiatives, which, in turn, have driven the demand for residential building materials and reinforced the interdependence between urban expansion and residential construction development. Remarkably, from 1990 to 2020, the population surged by 2.1-fold, the economy by 66-fold, and the stock of residential building materials by 477-fold, indicating that the growth rate of material stock consistently outpaced that of both population and economic growth. Over the past three decades, the rapid expansion of residential buildings has led to the encroachment of urban ecological spaces by concrete structures. The methodology proposed in this study for quantifying building material offers valuable insights for policymakers and urban and environmental planners to foster responsible resource consumption and supports component-level circularity in the built environment.

Navigating Crisis: Strategic Adaptations in Family Businesses Amid Economic Uncertainty

This research delves into the strategic adaptations made by family businesses to navigate crises amidst economic uncertainty. Family businesses represent a significant portion of the global economy and face unique challenges during periods of economic instability. Through a comprehensive review of academic literature, case studies, and expert insights, this study identifies key strategies employed by family businesses to withstand turbulent economic conditions. The findings highlight the critical role of clear communication and transparent decision-making processes in ensuring business resilience during crises. Family businesses that prioritize open dialogue among stakeholders are better equipped to respond effectively to challenges and maintain continuity. Moreover, flexibility within business models emerges as essential for adapting to rapidly changing market dynamics. Family businesses often leverage their inherent strengths, such as strong relationships with customers and communities, to navigate crises successfully. Prudent financial planning and risk management are identified as fundamental for safeguarding the financial stability of family businesses amidst economic uncertainty. Effective management of resources and proactive risk mitigation strategies enable businesses to weather downturns and position themselves for recovery. Additionally, the study emphasizes the importance of maintaining a long-term perspective and fostering family unity during crises. Family businesses that prioritize cohesion and collaboration among family members demonstrate greater resilience and are more likely to emerge stronger from adversity. By synthesizing these insights, this research provides valuable guidance for family business owners, managers, and policymakers facing economic uncertainty.

Resident water conservation behaviors among different age groups in rural areas of the Weibei arid belt, China

Understanding water conservation behaviors across various age groups is crucial for developing effective water resource management strategies particularly in arid and semiarid zones. Based on a semi-structural questionnaire survey of 445 households in China’s Weibei arid belt and extended Theory of Planned Behavior Model, a Structural Equation Model (SEM) was constructed to elucidate the age-related impact of water conservation knowledge, attitudes, subjective norms, and perceived control on water-saving behaviors. The results show that (1) youth group prioritizes personal hygiene and laundry activities whereas the elderly exhibit lower such requirements. (2) Water consumption varies significantly by age, with the elderly group consumes the least, averaging 62.26 litres per capita per day (Lpcd), followed by the middle-aged group at 73.95 Lpcd, and the youth at the highest consumption rate of 84.60 Lpcd. (3) Influential factors on water conservation behavior differ substantially by age: Among the youth, water conservation behavior is strongly influenced by attitude in this domain (standardized path coefficient [SPC] = 0.32). The middle-aged group is influenced by subjective norms (SPC=0.27) and attitudes toward water conservation (SPC=0.27). Conversely, the elder group's behavior is dominated by knowledge (SPC=0.35) and perceived control (SPC=0.32). Given such distinct perceptions and behaviors of household water consumption, tailored water conservation strategies are essential of water conservation education through social media for youth, tiered pricing for the middle-aged, and water-saving subsidies to the elderly in the case areas.

Ensuring Ethical Standards in an Organization: A Comprehensive Guide on How the Human Resource Manager can Create an Ethical Organization

In each and every organization, Effective and efficient Human Resource Management is a key component that enables employees and managers to contribute effectively and productively to the overall business direction and the accomplishment of the organization’s goals and objectives through various functions such as employee motivation, effective hiring, performance management, organizational development, safety, wellness, benefits, communication, career development and training.

Hydrogeochemical characterization and water quality evaluation for drinking and irrigation purposes of coastal aquifers of Middle Andaman

A comprehensive study to characterize the hydrogeochemistry of the region and the impact of saltwater intrusion on groundwater was conducted along the eastern coast of Middle Andaman of Andaman and Nicobar (A&N) Islands. The escalating population growth and intensified tourism activities have resulted in the over-extraction of groundwater. Seismic activities led to the opening of lineaments to the sea and the dissolution of limestone in the influence of seawater. 24 groundwater samples and 1 reference sample from sea were taken from various locations of middle Andaman. The analysis involved the determination of major cations, anions, and heavy metals using Inductively Coupled Plasma Optical Emission spectroscopy (ICP), spectrophotometry, and flame photometry. Furthermore, X-ray diffraction analysis, binary diagrams, Chloro-alkaline indices (CAI), Gibbs Plot, correlation matrix, Piper plot, Chadha’s plot and Principal Component Analysis (PCA) to the major ions data indicated rock-water interactions, strong correlations among alkali and alkaline earth metals, and interactions between seawater and carbonate minerals respectively. The water quality index indicated “very poor to unsuitable for drinking purposes” in 24% of the samples. Additionally, indices for irrigation suitability; total hardness (TH), residual sodium carbonate (RSC), and Magnesium adsorption ratio (MAR) were found to be detrimental for irrigation in 80%, 08%, and 12% of the water samples, respectively. These results highlight the importance of implementing effective water resource management techniques, such as groundwater extraction rates, adopting appropriate water treatment technologies to mitigate the adverse impacts of heavy metals, saltwater intrusion and maintain water quality for human consumption and agricultural use in the region.

Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning

Abstract The simulation of spring discharge is of high interest for water supply management. Accurate modeling and prediction of spring discharge can help water authorities and managers optimize water use, face seasonal variability, and prepare for possible drought conditions. Therefore, developing reliable simulation models and forecasting tools for spring discharge is essential for the effective management of groundwater resources relying on karstic springs. While the use of complementary modeling approaches has been explored in various hydrological contexts, its application to spring discharge modeling remains relatively unexplored. Previous studies on spring discharge modeling have predominantly focused on either conceptual/distributed models or data-driven models separately, without fully investigating the potential advantages of combining these two approaches. Consequently, there is a research gap in understanding the potential benefits and effectiveness of integrating these models for spring discharge modeling. This study developed and evaluated a hybrid modeling approach integrating a conceptual hydrological model with a machine learning technique to simulate spring discharge from the largest karst aquifer supplying the city of Bordeaux, France. Specifically, the study combined the five-parameter GR5J reservoir model with a random forests data-driven model in a hybrid framework. Model performance was assessed through comparison with….

Manajemen Efektif Pendidik dan Tenaga Kependidikan untuk Menciptakan Pembelajaran yang Berkualitas

Management of educators and education personnel is a key aspect of personnel management in educational institutions, with strategies for managing teacher and educator staff being the main focus for achieving quality learning. Professional development is also crucial in improving the competence and quality of trainers, while evaluation of their activities is necessary to monitor and improve performance. This study uses the literature method, with data collected through library data, reading, and managing research materials. The findings of this study show that effective human resource management strategies involve careful planning, thorough training and development, and regular performance evaluation. The implementation of education policies that focus on quality learning requires cross-departmental cooperation and active participation from various stakeholders. Environmental and organizational factors, including management, regulation, human resources, infrastructure, relationships, collaboration and changes in the external environment, also influence the effectiveness of HRM in educational institutions. By understanding the importance of educator and education personnel management and implementing effective HRM strategies, educational institutions can create a quality and competitive learning environment. This will positively impact the quality of education provided to students and help achieve broader educational goals.

Evaluation of water shortage and instream flows of shared rivers in South Korea according to the dam operations in North Korea

Abstract The Korean Peninsula's mountainous terrain poses challenges to effective water resource management. Notably, two significant river basins, North Han River and Imjin River basins, are essentially shared rivers originating in North Korea. After the construction of various dams in North Korea, billions of tons per year of water annually decreased from the upper reaches of these rivers of North Korea to South Korea. This study conducted an impact analysis on two major river basins affected by dam operations in North Korea. Both before and after the Imnam Dam operation, significant reduction (27.7%) in the average monthly inflow was observed, and the total release of the Hwacheon Dam in South Korea decreased by 40.2%. The analysis of instream flow indicated that the operation of dams in North Korea had a substantial impact on fulfilling instream flow requirements for dams located in North Han River and Imjin River basins. To ensure instream flow, this study proposed two plans. The first plan involved the utilization of existing dams in the North Han River basin, while the second plan suggested connecting the dams in North Korea, taking into account the shared rivers.

THEORETICAL FRAMEWORKS SUPPORTING IT AND BUSINESS STRATEGY ALIGNMENT FOR SUSTAINED COMPETITIVE ADVANTAGE

In today's dynamic business environment, the alignment between information technology (IT) and business strategy is critical for organizations seeking sustained competitive advantage. This paper presents a comprehensive examination of theoretical frameworks supporting this alignment, aiming to provide insights into how organizations can effectively integrate IT capabilities with strategic objectives. The theoretical foundations explored include the Resource-Based View (RBV), which emphasizes leveraging unique resources and capabilities to achieve competitive advantage. Additionally, the Dynamic Capabilities Framework underscores the importance of an organization's ability to adapt and innovate in response to changing market conditions, highlighting the role of IT in facilitating agility and flexibility. The Strategic Alignment Model offers a structured approach to ensuring coherence between business strategy, IT strategy, and organizational infrastructure, enabling seamless integration and alignment. Value Chain Analysis provides a framework for understanding how IT can add value across different stages of the value chain, informing strategic decision-making and resource allocation. Moreover, IT Governance Frameworks, such as COBIT and ITIL, offer guidelines for effective management and governance of IT resources to support strategic objectives. Business Process Reengineering (BPR) is also examined as a methodology for redesigning business processes to align with strategic goals and leverage IT capabilities. Through an exploration of these theoretical frameworks, this paper aims to equip organizations with the knowledge and tools necessary to achieve sustained competitive advantage through the strategic alignment of IT and business strategies. Keywords:  Theoretical Frameworks, IT, Business Strategy Alignment, Sustained Competitive Advantage.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

Groundwater mapping of urban and coastal areas in Brunei

Groundwater mapping plays an important role in effective water resource management, sustainable development, and environmental protection. In the tropical Brunei Darussalam (north Borneo Island), groundwater mapping is yet to be provided. The aim of the study is to provide groundwater mapping of Brunei, particularly for the urban and coastal areas of the Brunei-Muara capital district. The study uses a GIS interpolation technique to generate a groundwater contour map based on groundwater data from 572 sacrificial boreholes located in the study area. Remote sensing data and published maps from secondary sources were digitised in ArcGIS software to produce thematic layers for further hydrological evaluations. Results showed that groundwater levels in the study area are generally high and shallow, ranging from 0 to 18 m below ground level with a mean value of 2.9 m. According to the evaluation of geo-thematic layers and groundwater contours, groundwater flows towards the South China Sea in the coastal areas and towards the Brunei River further inland. Hydraulic gradients towards the South China Sea also vary between 0.004 and 0.08. Thus, assuming surface aquifer thickness in the weathered zone between 10 to 20 m, hydraulic conductivities ranges from 1∙10−5 to 1∙10−4 m∙s−1, a submarine groundwater discharge (SGD) flux between 4.7∙10−7 to 4.0∙10−4 m3∙s−1 per unit width can be estimated for the shallow aquifer. This study provides valuable insights into the groundwater system dynamics so important, which are critical for its future utilisation and protection, aiming to contribute to the national water security in Brunei Darussalam.

Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling.