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Diamond-like carbon (DLC) coatings are widely used for numerous engineering applications due to their superior multi-functional properties. Deposition of good quality DLC is governed by energy per unit carbon atom or ion and plasma kinetics, which are independent parameters. Translating independent parameters to dependent parameters to produce a best DLC is subjected to deposition method, technology, and system configurations which may involve above 50 combinations of bias voltage, chamber pressure, deposition temperature, gas flow rate, etc. Hence DLC coatings are optimized to identify the best parameters which yield superior properties. This article covers DLC introduction, the role of independent parameters, translation of independent parameters to dependent parameters, and discussion of four generations of DLC optimization. The first-generation of DLC optimization experimentally optimizes the parameter-to-property relationship, and the second-generation describes multi-parameter optimization with a hybrid of experimental and statistical-based analytical methods. The third generation covers the optimization of DLC deposition parameters with a hybrid of statistical methods and artificial intelligence (AI) tools. The ongoing fourth generation not only performs multi-parameter and multi-property optimization but also use AI tools to predict DLC properties and performance with higher accuracy. It is expected that AI-driven DLC optimizations and progress in virtual synthesis of DLC will not only assist in resolving DLC challenges specific to emerging markets and complex environments, but will also become a pathway for DLC to enter a digital-twin era. [Display omitted] • DLC quality is a function of carbon ion energy, an independent parameter. • Translating independent to dependent parameters make above 50 experimental combinations. • New DLC markets need robust optimizations for fabrication and properties. • AI/ML optimizations have ability to provide optimal solutions for new DLC markets. [ABSTRACT FROM AUTHOR]

Adaptive optics is being applied widely to a range of microscopies in order to improve imaging quality in the presence of specimen‐induced aberrations. We present here the first implementation of wavefront‐sensorless adaptive optics for a laser‐free, aperture correlation, spinning disk microscope. This widefield method provides confocal‐like optical sectioning through use of a patterned disk in the illumination and detection paths. Like other high‐resolution microscopes, its operation is compromised by aberrations due to refractive index mismatch and variations within the specimen. Correction of such aberrations shows improved signal level, contrast and resolution. [ABSTRACT FROM AUTHOR]

Environmental sustainability importance increases, the environmental degradation problems attract the discussions around the world concerning many areas of life, consumption and disposition of goods. Green marketing and advertisement have become a popular choice by companies to influence the consumer buying process. This study aimed to examine the moderated role of green advertisement among the influence of environmental concern, social impact and self-image on green purchase behaviour. The survey sample is 458 responses from various consumers. The methodological tools were quantitative research methods. The study applied confirmatory factor analysis and moderation analysis to evaluate the six hypotheses developed and proposed in the conceptual framework. The authors performed regression and moderation analysis after achieving the model fit indices and Cronbach's alpha. The regression analysis reveals that independent variables environmental concern, self-image and social impact had a significant influence on green purchase behaviour. Furthermore, environmental concern and selfimage have a more substantial influence on green purchase behaviour. The moderation results revealed that green advertisement has a positive and significant moderated relationship among environmental concern, self-image, social influence and green purchase behaviour. The obtained results indicated that marketers could increase green purchase behaviour. In turn, it will allow supporting the green environment by taken above determinants of green purchase behaviour into consideration for developing green policies and strategies which are mutually beneficial for the consumer as well as for the safe environment. The findings of this study suggest that green advertisement could increase green purchase behaviour. Herewith, people become greener and environment-conscious in their routine life. The authors suggested practical implications for the strategists and marketers who are willing to go green. The study results present an overview on how marketers could devise more effective strategies and advertisement to endorse the green purchase behaviour. [ABSTRACT FROM AUTHOR]

Saturable Absorber (SA) is a key element of any passive mode-locked laser system to provide ultrashort laser system. So far various materials have been proposed that could be used for this purpose. However, the field is still looking for new ways to make the fabrication process easier and cost-effective. Another challenge in testing mode-locked laser systems using various SA samples is the lack of knowledge in preparing these by laser physicists given this is outside their remit of expertise. In this study, we have proposed a novel method to produce these SAs from plastic materials and glycol. Our new method relies upon increase in thickness up to a value where the modulation depth is enough to give stable ultrashort pulses. Although we have shown this method for four materials; similar approach could be applied to any material. This will open the door of unlimited families of SAs that could be easily prepared and applied without any prior knowledge in material sciences. [ABSTRACT FROM AUTHOR]

The role of energy transition amidst the energy crisis and how policymakers can drive down emissions while focusing on energy security are critical. Given the geo-political situation, energy crisis volatility, energy shortage and climate change all affect the green transition and the short-term priorities for energy companies and policymakers. Energy security is not an isolated issue but has widespread implications as various sectors depend on energy supply to function properly. Governments around the world are faced with this trilemma, how to balance energy security with energy sustainability while also considering energy affordability. Sustainability has been in focus for about a decade. However, energy security is suddenly becoming one of the most important priorities that policymakers need to consider. Unfortunately, the renewable energy infrastructure is not yet ready to replace the growing volume of energy demand from hydrocarbon, which the world has been dependent on. This means, for now, a surge in energy generation through hydrocarbon to meet the existing energy demand deficit. However, it is important not to lose focus on the challenge of energy sustainability and climate change adaption and mitigation. Where trends like carbon capture and storage; solar, wind, hydro, green hydrogen, etc.; renewable energy infrastructure and integrations, with supply chain and engineering services consideration [in aspect for the growing market in this space] need better attention with regards to investment and full-scale implementation. This paper aims to analyze this 1st energy crisis of green transition with a priori on energy poverty with consideration of major influences and associated impacts. Furthermore, it proposes a specific framework for inclusive investigations, which considers the entire energy ecosystem with consideration of major influences, to enable the policymakers to better drive the green transition. This involves formulating energy policies that are not entirely conservative towards renewable energy sources but instead promote investments in both green and relatively more environmentally benign energy sources compared to high emission hydrocarbons. In this regard, this paper renders exhaustive prospects and recommendations. • The nexus between energy transition, energy poverty and their impacts are investigated. • Influence of climate change, COVID-19, geo-politics, and energy infrastructure on energy transition. • Exhaustive prospects and recommendations are rendered. • Requirement for inclusive investigations and recognition of energy poverty. [ABSTRACT FROM AUTHOR]

The broadcast storm problem causes redundancy, contention and collision of messages in a network, particularly in vehicular ad hoc networks (VANETs) where number of participants can grow arbitrarily. This paper presents a solution to this problem in which a node is designated as a master through an election process. Moreover, an algorithm is proposed for asynchronous VANETs to select a master node, where the participants (i.e., vehicles) can communicate with each other directly (single-hop). The proposed algorithm is extrema-finding in a way that a node having maximum signal strength is elected as a master node and each vehicle continues communication with the master until the master node keeps its signal strength at the highest level and remains operational too. This paper further presents the Petri net-based modeling of the proposed algorithm for evaluation which is going to be presented for the first time in leader election algorithm in VANETs. Verification of the proposed algorithm is carried out through state space analysis technique. [ABSTRACT FROM AUTHOR]

The proliferation in the deployment of electric vehicles has brought up several challenges in the planning and operation of power systems. The health of power system components and the satisfaction of electric vehicle users are becoming equally important with the increasing presence of electric vehicles on the power grid. The charging strategy is crucial as it plays a significant role in shaping users' behavior to deal with the adverse effects of electric vehicles on grid components. There is a lack of a proper unified framework for the charging management, which considers both the system perspectives such as transformer overloading, shortage of power, and users perspectives such as satisfaction, charging urgency, and privacy. This paper presents a fully decentralized cooperative approach/strategy for charging electric vehicles. The solution is achieved by developing a decentralized iterative algorithm based on a consensus concept to protect the privacy of EV owners. The charging priority factor is incorporated to address the charging urgency of electric vehicles. Multiple electric vehicles can be charged simultaneously without a central decision-maker or coordinator using the proposed strategy. As a result, electric vehicles fulfill their charging needs by the deadline to maximize user satisfaction while respecting their constraints and limitations imposed by the available charging power and charging station capacity. The convergence of the proposed approach is demonstrated by observing the charging of multiple electric vehicles. The results are benchmarked against the centralized approach to verify the effectiveness of the proposed approach. • A decentralized approach is based on peer-to-peer communication among neighbors. • Each EV user maximizes satisfaction and fulfills the charging requirements. • The charging priority factor addresses the charging urgency. • Decentralized and centralized approaches for EV charging are benchmarked. [ABSTRACT FROM AUTHOR]

Energy retrofitting for buildings has gained great attention in recent years due to the significant benefits of reducing environmental impacts and costs. Generally, retrofit planning involves considering criteria such as energy savings, emissions savings, and cost savings. However, the rapid transition of climate and extreme climate forces integrate climate adaptation into retrofit practices. This study examines strategies and modifications necessary to transform the current climate mitigation-based retrofit processes into frameworks that simultaneously improve climate mitigation and adaptation. A comprehensive literature review is conducted on building energy retrofit studies based on climate adaptation. It was found that future climate events are anticipated to be more related to overheating than under-cooling in many regions. Passive retrofit strategies can reduce overheating risks substantially, while the dependency on active cooling is unavoidable in warm climatic regions in future climates. Retrofit strategies designed based on traditional approaches may not be effective and may even worsen the risks of overheating in the future climate due to drastic climate change. Climate resiliency criteria must be adapted to the building retrofit field. Climate modelling and energy simulation have a substantial role in designing retrofits, and yet significant research and development is required to penetrate the current retrofit practices. The synthesis of the findings guides the systematic and careful designing and implementation of building energy retrofits. These findings offer valuable insights to policymakers, urban planners, and researchers seeking to improve buildings' energy performance and resiliency and act as a guideline to implement climate change adaptation strategies. • Reviewed climate adaptation-based retrofit designing and planning studies. • Revealed a significant gap in existing retrofit frameworks that consider both climate mitigation and adaptation. • Highlighted careful design and implementation of retrofits is necessary to overcome future climate risks. • Presented a comprehensive guideline to integrate climate adaptation aspects into current retrofit practices. • Suggested future research directions, including the development of climate resilience benchmarks. [ABSTRACT FROM AUTHOR]

• A summary of clinical background of MDD and BD, Brain structure and EEG based brain activity, • EEG experimental protocols study for mental disorders diagnosis, • An extensive study of state-of-the-art shallow and deep neural networks models for EEG based clinical depression detection, • An extensive study of neural networks based approaches for Biploar Disorder diagnosis using EEG signals, • A discussion of state-of-the-art methods limitations and giving valuable recommendations for future research Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals. [ABSTRACT FROM AUTHOR]

• Adaptive regression model-based real-time optimal control was developed for air conditioning systems. • Regression models of the air conditioning process were adopted in this study. • Similarity analysis was carried out to find the optimal length of training data. • The proposed strategy is able to improve the building energy efficiency. • The proposed strategy is able to reduce the computational load. Model-based, real-time optimal control is an effective tool to improve the energy efficiency of central air-conditioning systems. However, its performance relies heavily on the accuracy of the system models, whereas the development of accurate models for central air-conditioning systems is not easy due to their complex dynamics and non-linearities. This study presents an adaptive regression model-based real-time optimal control strategy for central air-conditioning systems. In the proposed strategy, regression models are adopted to describe the relationship between the power consumption of the system and the variables that are optimised. Their simple structures enable a low computation load for model updating and real-time optimisation. The length of the training data (for model updating) is investigated, and a suitable length is found using a similarity check-based method. Case studies were carried out to assess the performance of the proposed strategy, and they demonstrated that (1) a week was the optimal length of the training data for the case system, (2) the proposed strategy saved energy use by 3.48–10.59% when compared with a benchmark system with no optimisation, and (3) the proposed method reduced the computational load by 85% when compared with a simplified physical model-based optimal control without adaptive modelling. [ABSTRACT FROM AUTHOR]

• Tunable bound solitons (BS) from carbon nanotube saturable absorber (SA) are presented. • Tunable filter helped us to obtain wavelength and pulse duration tunable BS. • These tunable BS could be used in applications where bunches of ultrashort pulses are required. • Results presented in this study will help us to achieve tunable BS in other SAs and gain materials. In this report we investigate the generation of Bound Solitons (BS) by using Carbon nanotubes (CNT) Saturable Absorber (SA) by using an Erbium (Er) doped fibre laser. In this study two cases are presented, one with a tunable intracavity filter and another without. When we did not use a tunable filter, we could generate two types BS in CNT. When we used a tunable filter, we could still generate two BS, but this time they were wavelength tunable and were longer in time domain due to introduction of filter. To best of our knowledge this is the first time when wavelength and time tunable BS are presented by using a saturable absorber. This will open more avenues where these BS could be applied notability telecommunication and material processing. [ABSTRACT FROM AUTHOR]

Cross-Cloud Federation (CCF) is beneficial for heterogeneous Cloud Service Providers (CSPs) for leasing additional resources from each other. Despite the benefits of on-demand scalability and enhanced service footprints for better service quality, the adoption of CCF is however mainly hindered due to the lack of a comprehensive trust model. The basic aim of such a model should be to address the security and performance concerns of a home CSP on its foreign peers before placing its users’ data and applications in their premises. A transitivity of users’ trust on home CSP and home CSP’s trust on its foreign CSPs marks the uniqueness of trust paradigm in CCF. Addressing the concerns of cloud-to-cloud trust paradigm is inevitable to achieve users’ trust in a federation. Various trust models have been proposed in literature for conventional and multi-cloud computing environments. They focus on user requirements but none on federation perspective. Their applicability to CCF for addressing the concerns of cloud-to-cloud trust paradigm requires further consideration. For this reason, we have first outlined the general characteristics of CCF as being dynamic, multi-level and heterogeneous. Afterwards, cloud-to-cloud trust paradigm is proposed based on a set of unique principles identified as (i) trust bi-directionality, (ii) trust composition, (iii) delegation control, and (iv) Resource awareness. An insightful review of Trust Management Systems (TMS) proposed in literature reveals their shortcomings in addressing the requirements of cloud-to-cloud trust paradigm. To overcome these shortcomings, we suggest that some challenges can be merely addressed by aligning the existing methods to the nature of CCF. The remaining challenges require entirely new mechanisms to be introduced. A demonstration of this concept is presented in the form of a requirement matrix suggesting how the characteristics and properties of both CCF and the TMS are influenced by each other. This requirement matrix reveals the potential avenues of research for a TMS aimed specifically for CCF. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) face resource scarcity challenges owing to limited energy and computational resources. Mobile cloud computing (MCC) offers a resource-rich environment to MDs for offloading compute-intensive tasks encountering resource scarcity challenges. However, users are unable to exploit its full potential owing to challenges of distance, limited bandwidth, and seamless connectivity between the remote cloud (RC) and MDs in the conventional MCC model. The cloudlet-based solution is widely used to address these challenges. The response of the cloudlet-based solution is faster than the conventional mobile cloud-computing model, rendering it suitable for the Internet of Things (IoT) and Smart Cities (SC). However, with the increase in devices and workloads, the cloudlet-based solution has to deal with resource-scarcity challenges, thus, forwarding the requests to remote clouds. This study has been carried out to provide an insight into existing cloudlet-based mobile augmentation (CtMA) approaches and highlights the underlying limitations for resource optimization. Furthermore, numerous performance parameters have been identified and their detailed comparative analysis has been used to quantify the efficiency of CtMA approaches. [ABSTRACT FROM AUTHOR]

The building sector is material-intensive and is responsible for consuming large quantities of local and international resources. In extreme climates, the consumption of these resources increases manifold to ensure comfortable indoors. Contemporary buildings have predominantly used energy efficiency as the main criterion to define sustainability, which has led to the consumption of more abiotic materials. In contrast, indigenous buildings are based on local context and are guided by geographical, environmental, and economic constraints. This work aims to critically review indigenous architectural practices across four climatic zones, focusing on their resource efficiency, and identify practices that can be reintroduced in contemporary construction in extremely cold climates. To achieve this objective, literature from 1960 to 2022 was comprehensively reviewed. In addition, contemporary building construction and codes in cold climatic regions were analyzed to assess the current state of practices. Specific case studies from each climatic region were evaluated to assess applicability in residential buildings in cold climates. The main findings from this review and analysis indicate that indigenous practices have the potential to optimize the use of natural resources to create more sustainable climate-resilient buildings. In addition, more empirical studies are needed to tailor these practices and quantitatively assess resource efficiency for large-scale implementation. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has unprecedentedly entangled the physical world with cyber technologies and its integration with building infrastructure (BI) is no different. Integration of IoT can impart BI with upscale features like remote operations, automated management and user-centric facilities by developing an interconnected cognitive building (CB) ecosystem. However, this integration has entered an ambiguous phase of realizing mature adoption and practical utilization of IoT in BI for both academic and industrial domains. This ambiguity restricts the IoT and BI stakeholders to comprehend and acknowledge the full operational competency of IoT in BI. Thus, a significant research gap exists that deeply investigates the practical implementation and mature adoption of IoT in BI. The prime objective of this study is to establish a comprehensive review by exploring the state-of-art academic, technological and industrial research to identify major technological and behavioural interventions that successfully enhance the integration of IoT in BI. Besides, this study also highlights significant technical and non-technical challenges that require substantial research efforts for maturing the adoption of IoT in BI. The findings of the study argue that the full operational competency of IoT in BI is not yet realized and a dedicated effort from both IoT and BI stakeholders is required to provide modern BI with a generic IoT framework having cognitive intelligence and context-aware computing capabilities. The proposed study will assist the researchers in realizing the full operational competency of IoT in BI for more exciting innovations. • Adoption and implementation of IoT in building infrastructure are investigated. • Technological and behavioural interventions for cognitive intelligence in buildings. • Requirement of a seamlessly unified IoT framework for future buildings. • Trends, challenges and future research to effectively implement IoT in buildings. [ABSTRACT FROM AUTHOR]

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants' health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO2 concentration under the set-point with an intermediate power energy utilization. [ABSTRACT FROM AUTHOR]

This paper presents the technical, environmental, and economic evaluation of integrating various combinations of renewable energy sources-based systems in the expansion of a district heating and cooling network of a Technology Park near Barcelona in Spain. At present, a combined heat and power plant running on fossil fuels serves the heating, cooling, and electricity demand of the Park. However, this energy demand is expected to increase substantially in the coming years. EnergyPRO software was used to model the energy demand growth till 2030. Validation of the software application was done by making a base model using real plant data from the year 2014. The software was then used to project the energy supply based on three 15-year scenarios, having different combinations of renewable energy technologies, from 2016 until 2030. Primary energy consumption, CO2 emissions, and the net present value obtained in each scenario were used to decide the best combinations of renewable energy sources. The results of the study showed that presently, biomass boilers combined with absorption chillers and supported with solar thermal cooling are the most competitive technologies in comparison to ground source heat pumps for large DHC networks. This is mainly because of the lower primary energy consumption (624,380 MWh/year in 2030 vs. 665,367 MWh/year), higher net present value (NPV) (222 million € vs. 178 million €), and lower CO2 emissions (107,753 tons/year in 2030 vs. 111,166 tons/year) obtained as a result of the simulations. [ABSTRACT FROM AUTHOR]

Rapidly increasing catastrophic environmental incidents and changes in global climate have compelled researchers to take proactive measures for climate change mitigation. Consequently, reducing greenhouse gas emissions has become one of the most significant global targets. The transportation sector has a massive carbon footprint accounting for 22% of the total carbon dioxide emissions. In this regard, road transport contributes the largest fraction of the total emissions within the transportation sector. Conversely, the railway which is the cleanest form of transportation contributes slightly more than 1% of the total emissions. Moreover, since most of the rail systems are operated by government or private companies, it is relatively convenient to implement policies and replace conventional technology with environmentally friendly ones. This could be achieved by taking certain measures from technical and operational viewpoints for conserving energy to lower the carbon intensity of all transport modalities. Hence, this review paper focuses on investigating the railway sector to encourage a modal shift to the least carbon-intensive option. This study provides a critical review of the past and present rail technologies in terms of the environmental, economic, social, and technical perspectives. Moreover, a comparative assessment of the various locomotive types has been performed. The results reveal that battery-hydrogen hybrid locomotives are the best choice to be considered as a future locomotive technology. Hydrogen locomotives have the potential to be a promising alternative to diesel railway locomotives. However, certain technical barriers need to be overcome to enhance the overall system's performance and reliability to further improve the viability of its deployment in the railway sector. [Display omitted] • A critical review of the various rail technologies is provided through different viewpoints. • The assessment constitutes environmental, economic, social, and technical perspectives. • A decision-support tool for comparison of four rail technologies is also developed. • Battery-hydrogen hybrid locomotive results in the highest score of 1.47. • Standalone hydrogen locomotive results in the second highest score of 1.38. [ABSTRACT FROM AUTHOR]

The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

With all the recent advancements in the electronic world, hardware is becoming smaller, cheaper and more powerful; while the software industry is moving towards service‐oriented integration technologies. Hence, service oriented architecture is becoming a popular platform for the development of applications for distributed embedded real‐time system (DERTS). With rapidly increasing diversity of services on the internet, new demands have been raised concerning the efficient discovery of heterogeneous device services in the dynamic environment of DERTS. Context‐awareness principles have been widely studied for DERTS; hence, it can be used as an additional set of service selection criteria. However, in order to use context information effectively, it should be presented in an unambiguous way and the dynamic nature of the embedded and real‐time systems should be considered. To address these challenges, the authors present a service discovery framework for DERTS which uses context‐aware ontology of embedded and real‐time systems and a semantic matching algorithm to facilitate the discovery of device services in embedded and real‐time system environments. The proposed service discovery framework also considers the associated priorities with the requirements posed by the requester during the service discovery process. [ABSTRACT FROM AUTHOR]

Data centers can go green by saving electricity in two major areas: computing and cooling. Servers in data centers require a constant supply of cold air from on-site cooling mechanisms for reliability. An increased computational load makes servers dissipate more power as heat and eventually amplifies the cooling load. In thermal-aware scheduling, computations are scheduled with the objective of reducing the data-center-wide thermal gradient, hotspots, and cooling magnitude. Complemented by heat modeling and thermal-aware monitoring and profiling, this scheduling is energy efficient and economical. A survey is presented henceforth of thermal-ware scheduling and associated techniques for green data centers. [ABSTRACT FROM AUTHOR]