Teng Wang, Yao Zhang, Jiangguo Liang, Shuai Wang, Shuanggen Liu^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3891-3907, 2024, DOI:10.32604/cmc.2024.048115

Abstract With the widespread data collection and processing, privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals. Support vector machine (SVM) is one of the most elementary learning models of machine learning. Privacy issues surrounding SVM classifier training have attracted increasing attention. In this paper, we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction, called Fed_DPDR-DPML, which greatly improves data utility while providing strong privacy guarantees. Considering in distributed learning scenarios, multiple participants usually hold unbalanced or small amounts of data. Therefore, Fed_DPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted… More >

Peter Kasten^a, Severin Zimmermann^a, Manish K. Tiwari^a, Bruno Michel^b, Dimos Poulikakos^a,*

Frontiers in Heat and Mass Transfer, Vol.1, No.2, pp. 1-10, 2010, DOI:10.5098/hmt.v1.2.3006

Abstract Electronic data center cooling using hot water is proposed for high system exergetic utility. The proof-of-principle is provided by numerically modeling a manifold micro-channel heat sink for cooling microprocessors of a data center. An easily achievable 0.5l/min per chip water flow, with 60°C inlet water temperature, is found sufficient to address the typical data center thermal loads. A maximum temperature difference of ~8°C was found between the solid and liquid, confirming small exergetic destruction due to heat transport across a temperature differential. The high water outlet temperature from the heat sink opens the possibility of waste heat recovery applications. More >

Xiaoqian Ma^a , Mo Yang^a,*, Yuwen Zhang^b

Frontiers in Heat and Mass Transfer, Vol.7, pp. 1-8, 2016, DOI:10.5098/hmt.7.36

Abstract Combustion mechanism of a 300 MW pulverized coal boiler is analyzed and the optimization of the performance of the boiler is carried out. The flow field, temperature field, devolatilization, char combustion and CO generation in the boiler furnace under actual condition is obtained by using Fluent. Three methods to improve the efficiency of boiler combustion are proposed based on the pulverized coal combustion mechanism; their feasibilities are verified through numerical simulation and analysis. The three proposed methods to increase the combustion efficiency may give theoretical reference for air arrangement and combustion optimization of the same type of burners. More >

Yiding Cao

Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-16, 2022, DOI:10.5098/hmt.19.2

Abstract This paper introduces a novel air-water thermal power plant working at low temperatures and employing hot water as a heat-supply fluid to produce utility-scale power with high second-law efficiency. The air-water power plant uses both air and water as working fluids and employs a direct-contact mass and heat transfer packing to facilitate latent heat (in terms of vapor) and sensible heat transfer from the hot water to moist air for expansion in a gas turbine to produce power. A cycle analysis indicates that with a heat source temperature of around 100o C, the power plant could achieve a power capacity… More >

Yiding Cao

Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-12, 2022, DOI:10.5098/hmt.19.1

Abstract This paper introduces utility-scale underground hot water storage (USUHWS) systems and evaluates their performance to enable renewable energy sources to produce dispatchable utility-scale power or supply heat with minimized interruption and impact by weather conditions. The USUHWS systems could retain the thermal energy content of the stored hot water for years so that renewable energy can be extracted and stored at any time year-round and be used whenever needed. Another major objective of this paper is to introduce hot-water power and heat internets to interconnect storage systems, power plants, energy sources, and various water and heat users to efficiently manage… More >

Chien-Ming Chen¹, Zhenzhou Zhang¹, Jimmy Ming-Tai Wu¹, Kuruva Lakshmanna^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463

Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three… More >

Rupali Gangarde^1,2,*, Amit Sharma³, Ambika Pawar⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2171-2190, 2023, DOI:10.32604/cmc.2023.035559

Abstract Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome… More >

Salman Naseer¹, Raheela Saleem², Muhammad Mudasar Ghafoor³, Shahzada Khurram⁴, Shafiq Ahmad⁵, Abdelaty Edrees Sayed⁵, Muhammad Shafiq^6,*, Jin-Ghoo Choi⁶

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1699-1714, 2023, DOI:10.32604/iasc.2023.034032

Abstract The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio… More >

V. A. Vijayakumar^*, J. Shanthini, S. Karthik, K. Srihari

Computer Systems Science and Engineering, Vol.45, No.1, pp. 659-669, 2023, DOI:10.32604/csse.2023.028400

Abstract The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The… More >

Xiuhua Weng^*1, Shaohong Luo^*1, Shen Lin^*, Lixian Zhong^†, Meiyue Li^*, Rao Xin^*, Pinfang Huang^*, Xiongwei Xu^*

Oncology Research, Vol.28, No.2, pp. 117-125, 2020, DOI:10.3727/096504019X15707883083132

Abstract To evaluate the cost–utility of pembrolizumab versus chemotherapy as the first-line setting for metastatic nonsmall cell lung cancer (NSCLC) from the US health care system perspective, a Markov model was developed to compare the lifetime cost and effectiveness of pembrolizumab versus chemotherapy for untreated metastatic NSCLC, based on the clinical data derived from phase III randomized controlled trial (KEYNOTE- 042; ClinicalTrials.gov; NCT02220894). Weibull distribution was fitted to simulate the parametric survival functions. Drug costs were collected from official websites, and utility values were obtained from published literature. Total costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were computed… More >