A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

Optimal Tests for Combining p-Values

Combining information (p-values) obtained from individual studies to test whether there is an overall effect is an important task in statistical data analysis. Many classical statistical tests, such as chi-square tests, can be viewed as being a p-value combination approach. It remains challenging to find powerful methods to combine p-values obtained from various sources. In this paper, we study a class of p-value combination methods based on gamma distribution. We show that this class of tests is optimal under certain conditions and several existing popular methods are equivalent to its special cases. An asymptotically and uniformly most powerful p-value combination test based on constrained likelihood ratio test is then studied. Numeric results from simulation study and real data examples demonstrate that the proposed tests are robust and powerful under many conditions. They have potential broad applications in statistical inference.

Research Progress of Photo-/Electro-Driven Thermochromic Smart Windows

Thermochromic smart windows can automatically control solar radiation according to the ambient temperature. Compared with photochromic and electrochromic smart windows, they have a stronger applicability and lower energy consumption, and have a wide range of application prospects in the field of building energy efficiency. At present, aiming at the challenge of the high transition temperature of thermochromic smart windows, a large amount of innovative research has been carried out via the principle that thermochromic materials can be driven to change their optical performance by photothermal or electrothermal effects at room temperature. Based on this, the research progress of photo- and electro-driven thermochromic smart windows is summarized from VO2-based composites, hydrogels and liquid crystals, and it is pointed out that there are two main development trends of photo-/electro-driven thermochromic smart windows. One is exploring the diversified combination methods of photothermal materials and thermochromic materials, and the other is developing low-cost large-area heating electrodes.

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals

Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.

The Function of cGAS-STING Pathway in Treatment of Pancreatic Cancer

The activation of stimulator of interferon genes (STING) signalling pathway has been suggested to promote the immune responses against malignancy. STING is activated in response to the detection of cytosolic DNA and can induce type I interferons and link innate immunity with the adaptive immune system. Due to accretive evidence demonstrating that the STING pathway regulates the immune cells of the tumor microenvironment (TME), STING as a cancer biotherapy has attracted considerable attention. Pancreatic cancer, with a highly immunosuppressive TME, remains fatal cancer. STING has been applied to the treatment of pancreatic cancer through distinct strategies. This review reveals the role of STING signalling on pancreatic tumors and other diseases related to the pancreas. We then discuss new advances of STING in either monotherapy or combination methods for pancreatic cancer immunotherapy.

Combination therapy with injectable platelet-rich fibrin and microneedling for male androgenetic alopecia: an evaluation of current practice

Introduction: Within the past 20 years, significant advances have taken place within the realm of aesthetic medicine to manage hair loss. Several human blood preparations have been found to contain regenerative properties suitable for hair rejuvenation and, subsequently, have emerged onto the market. An advanced and liquid version of platelet-rich fibrin, injectable platelet-rich fibrin (iPRF) is one such product. This article evaluates the combination of iPRF with microneedling (MN) for the treatment of male androgenetic alopecia (AGA) compared to other therapies. To knowledge, it is the first evaluation of its kind. Methods and materials: Secondary research was conducted via EMBASE, MEDLINE, PubMed and Cochrane Library databases to explore a preferred blood preparation and combination treatment using MN. Results and discussion: Secondary research shows that there is more in-depth evidence that suggests platelet-rich plasma (PRP) is the preferred blood preparation for treating male AGA at present. However, studies show that iPRF has greater regenerative potential. More research currently exists to suggest that MN in combination with minoxidil (MX) therapy is preferred over iPRF combined with MN alone. No validated patient-reported outcome measures (PROMs) currently exist for male AGA, although PROMs do exist for facial aesthetic treatments and alopecia areata. Conclusions: Studies show that iPRF combined with MN is a safe and effective option for the treatment of male AGA, but comparison studies against other combinations are lacking. Other monotherapy and combination methods are generally more evidenced. The secondary research also found that no specific PROM tool for measuring male AGA exists. Unless a specific PROM is developed and validated for male AGA, challenges around treatment comparison will continue to limit evidence.

Tessellation in Architecture from Past to Present

Tessellation, which has examples of use in art and architecture, is the covering of a surface using one or more geometric shapes without overlapping or gaps. Based on Roman mosaics, the tessellation has an important place in architecture since the ancient times. Through the history, different patterns have been used by many cultures for various applications ranging from decorative covering elements to multi-functional latticework screens. The tessellation has still been used in contemporary architecture since it not only allows creating the geometrical surface in an order but also provides multi-functionality to the surface when applied as shading elements. The tessellation can be reviewed under three categories such as regular, semi-regular and demi-regular tessellations. Two- and three-dimensional examples of the tessellations can be seen in contemporary architecture either as façade elements or patterns used for structural elements. Because the tessellation plays a significant role in architecture in terms of geometrical or structural design, the interest on this topic has been increased in recent years. Due to their great potentials, more studies should be conducted on the tessellations. For this reason, within the scope of this paper, the applied examples of the tessellations in buildings from past to present are examined which include both static and kinetic ones. In this paper, the geometric design principles, combination methods and iteration processes of the examples are also presented. As well as providing a deeper understanding of such tessellation methods, this study will serve as a basis of reference for future studies in this field.