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Hydrogen production has been advocated as a promising way for the decarbonization of power systems with renewables. By using the renewable curtailments to produce the hydrogen gas that will be used for power supply when necessary, water electrolysis can work with transmission expansion planning (TEP) to improve the renewable utilization. In this paper, it is found that the nonlinear electrochemical characteristics of water electrolysis can be equivalently formulated as the feasible region that consists of two convex subregions, in the space of the consumed renewable power and produced hydrogen gas. Regarding this finding and the typical application scenario where the hydrogen gas is stored in gasholders and used in hydrogen oxygen fuel cells, TEP with water electrolysis is further established as a mixed integer nonlinear programing (MINLP) problem. In our formulation, two linear relaxation technologies are presented to handle both the complementary constraints of the gasholders and the selection of which subregion of water electrolysis. Numerical results in the Garver 6-bus test system and a 661-bus utility system demonstrate the effectiveness of the presented methods.

Utility companies have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current asset health index based on the current condition data. For long-term preventative asset management, it is highly desirable to predict asset health indices in the next few years, especially for asset classes in which the assets share similar electrical, mechanical and condition degradation characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to predict long-term health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other prediction methods.

The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled images, we propose a Geographical Knowledge-driven Representation learning method for remote sensing images (GeoKR), improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associated with each remote sensing image are regarded as geographical knowledge to provide supervision for representation learning and network pre-training. An efficient pre-training framework is proposed to eliminate the supervision noises caused by imaging times and resolutions difference between remote sensing images and geographical knowledge. A large scale pre-training dataset Levir-KR is proposed to support network pre-training. It contains 1,431,950 remote sensing images from Gaofen series satellites with various resolutions. Experimental results demonstrate that our proposed method outperforms ImageNet pre-training and self-supervised representation learning methods and significantly reduces the burden of data annotation on downstream tasks such as scene classification, semantic segmentation, object detection, and cloud / snow detection. It demonstrates that our proposed method can be used as a novel paradigm for pre-training neural networks. Codes will be available on https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning., Comment: 13 pages, 5 figures

In recent months, the novel coronavirus disease 2019 (COVID-19) pandemic has become a major public health crisis with takeover more than 1 million lives worldwide. The long-lasting existence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not yet been reported. Herein, we report a case of SARS-CoV-2 infection with intermittent viral polymerase chain reaction (PCR)-positive for >4 months after clinical rehabilitation. A 35-year-old male was diagnosed with COVID-19 pneumonia with fever but without other specific symptoms. The treatment with lopinavir-ritonavir, oxygen inhalation, and other symptomatic supportive treatment facilitated recovery, and the patient was discharged. However, his viral PCR test was continually positive in oropharyngeal swabs for >4 months after that. At the end of June 2020, he was still under quarantine and observation. The contribution of current antivirus therapy might be limited. The prognosis of COVID-19 patients might be irrelevant to the virus status. Thus, further investigation to evaluate the contagiousness of convalescent patients and the mechanism underlying the persistent existence of SARS-CoV-2 after recovery is essential. A new strategy of disease control, especially extending the follow-up period for recovered COVID-19 patients, is necessary to adapt to the current situation of pandemic.

CFRP plates are important advanced material used in the aerospace field, high-quality and high-efficiency drilling is the main application processing demand of CFRP. In this paper, a 532 nm nanosec...

Objectives/background Despite the high prevalence of sleep disturbance, stress, and depressive symptoms among patients with episodic migraine, there has been limited prospective research examining how these comorbid symptoms relate to future headache risk. Methods We conducted an a priori secondary analysis of a prospective cohort study of 98 adults with episodic migraine recruited through Harvard-affiliated medical centers and local college student clinics in Boston, MA. At baseline, participants completed validated questionnaires on sleep quality, stress, and depressive symptoms. Over the next 6 weeks, they recorded headaches on twice-daily diaries. We conducted time-to-event analyses to evaluate whether these baseline symptoms were associated with headache recurrence. Results At baseline, 45/98 (46%) participants had poor sleep quality, 51/98 (52%) reported moderate/high stress levels, and 18/98 (18%) had high depressive symptom scores. Over 4,406 person-days, we observed 823 discrete headaches. In multivariable models, the hazard ratios for headache recurrence were: 1.22 (95% CI 1.02, 1.46) for people with baseline poor sleep, 1.12 (95% CI 0.93, 1.35) for those with baseline moderate/high stress compared to lower levels, and 1.31 (95% CI 1.05, 1.65) for the combination of poor sleep and moderate/high stress compared to the combination of good sleep and low stress. There was no association between depression scores and headache risk. Conclusion Among patients with episodic migraine, poor sleep was associated with a higher rate of headache recurrence over the next 6 weeks, especially among those with coexisting moderate/high stress.

A tie-line security region provides all possible tie-line power injections with which constraints in a regional network will not be violated. With the high penetration of renewables, a sharp increase or decrease of renewable power requires a good depiction of the tie-line security region considering time coupling. However, the tie-line power security region with time coupling is a high-dimension polytope, which leads to a considerable computational burden. This paper presents a fast algorithm to approximate the original high-dimension tie-line security region considering time coupling. A few lower-dimension polytopes associated with combinations of different periods are calculated for the approximation. An evaluation method with clear physical explanations is established to measure the accuracy of the approximation. The method converts the evaluation to two linear programming problems based on the mathematical relationship between optimal points and polytope vertices. Under a Monte Carlo framework, the statistical analysis is employed to measure the accuracy using five presented indices. The performance of the proposed methods is verified by the case studies on the IEEE 118-bus test system and a 661-bus utility system.

Background As a secreted adipokine, adipsin has been recently shown to play a pivotal role in metabolic disorders. However, information regarding the association of circulating adipsin with non-alcoholic fatty liver disease (NAFLD) in humans is scant. Methods We recruited 1163 obese adult subjects with waist circumference at least 90 cm in men and 80 cm in women from the community. Circulating adipsin levels were measured by enzyme-linked immunosorbent assay. Results Circulating adipsin levels of NAFLD subjects was decreased compared to those in non-NAFLD (p p p p Conclusions These results demonstrate that circulating adipsin levels in Chinese obese adults are negatively associated with risk of NAFLD, implying that serum adipsin levels may be a potential protective factor in NAFLD.

PurposeBusiness ecosystem is described as a web of loose relationships, thereby posing some challenges to coordination. In attempt to ensure coordination in business relationships, scholars have proposed relational and contractual governance mechanisms. Hence, this study aims to examine the effects of governance mechanisms on coordination in the pharmaceutical business ecosystem (BE), with the potential mediating effects of closeness and opportunistic behavior.Design/methodology/approachThe study adopted quantitative approach with cross-sectional survey research design to collect data from the business ecosystem of pharmaceutical company X in Ghana. A structured questionnaire based on a five-point Likert scale was employed to collect data. Analysis was based on 173 institutions (both local and international) selected from pharmaceutical BE. Various validity and reliability checks were conducted before the presentation of the actual analysis, which was conducted using ordinary least squares (OLS) regression in Statistical Product and Service Solutions (SPSS).FindingsThe results of the study revealed that both relational and contractual governance have a direct positive effect on coordination between pharmaceutical BE members and the focal firm. The effect of contractual governance on coordination is however, partially mediated by opportunistic behavior. Finally, closeness also fully mediated the effect of relational governance on coordination.Originality/valueThe study builds on the governance mechanisms in exchange relationship, which was hitherto largely limited to the dyadic and triadic business network, partnerships and strategic alliances studies. The contribution to BE literature provides further understanding into transaction cost economics and relational exchange theory, which was dominantly applied in dyadic relationships ties such as partnerships, alliances and networks.

Pulmonary vascular disease is associated with poor outcomes in individuals affected by interstitial lung disease. The pulmonary vessels can be quantified with noninvasive imaging, but whether radiographic indicators of vasculopathy are associated with early interstitial changes is not known.Are pulmonary vascular volumes, quantified from CT scans, associated with interstitial lung abnormalities (ILA) in a community-based sample with a low burden of lung disease?In 2,386 participants of the Framingham Heart Study, we used CT imaging to calculate pulmonary vascular volumes, including the small vessel fraction (a surrogate of vascular pruning). We constructed multivariable logistic regression models to investigate associations of vascular volumes with ILA, progression of ILA, and restrictive pattern on spirometry. In secondary analyses, we additionally adjusted for diffusing capacity and emphysema, and performed a sensitivity analysis restricted to participants with normal FVC and diffusing capacity.In adjusted models, we found that lower pulmonary vascular volumes on CT were associated with greater odds of ILA, antecedent ILA progression, and restrictive pattern on spirometry. For example, each SD lower small vessel fraction was associated with 1.81-fold greater odds of ILA (95% CI, 1.41-2.31; P .0001), and 1.63-fold greater odds of restriction on spirometry (95% CI, 1.18-2.24; P = .003). Similar patterns were seen after adjustment for diffusing capacity for carbon monoxide, emphysema, and among participants with normal lung function.In this cohort of community-dwelling adults not selected on the basis of lung disease, more severe vascular pruning on CT was associated with greater odds of ILA, ILA progression, and restrictive pattern on spirometry. Pruning on CT may be an indicator of early pulmonary vasculopathy associated with interstitial lung disease.

Existing deep learning-based remote sensing images semantic segmentation methods require large-scale labeled datasets. However, the annotation of segmentation datasets is often too time-consuming and expensive. To ease the burden of data annotation, self-supervised representation learning methods have emerged recently. However, the semantic segmentation methods need to learn both high-level and low-level features, but most of the existing self-supervised representation learning methods usually focus on one level, which affects the performance of semantic segmentation for remote sensing images. In order to solve this problem, we propose a self-supervised multitask representation learning method to capture effective visual representations of remote sensing images. We design three different pretext tasks and a triplet Siamese network to learn the high-level and low-level image features at the same time. The network can be trained without any labeled data, and the trained model can be fine-tuned with the annotated segmentation dataset. We conduct experiments on Potsdam, Vaihingen dataset, and cloud/snow detection dataset Levir_CS to verify the effectiveness of our methods. Experimental results show that our proposed method can effectively reduce the demand of labeled datasets and improve the performance of remote sensing semantic segmentation. Compared with the recent state-of-the-art self-supervised representation learning methods and the mostly used initialization methods (such as random initialization and ImageNet pretraining), our proposed method has achieved the best results in most experiments, especially in the case of few training data. With only 10% to 50% labeled data, our method can achieve the comparable performance compared with random initialization. Codes are available at https://github.com/flyakon/SSLRemoteSensing.

Nonalcoholic fatty liver disease (NAFLD) is leading chronic liver syndrome worldwide. Gut microbiota dysbiosis significantly contributes to the pathogenesis and severity of NAFLD. However, its role is complex and even unclear. Treatment of NAFLD through chemotherapeutic agents have been questioned because of their side effects on health. In this review, we highlighted and discussed the current understanding on the importance of gut microbiota, its dysbiosis and its effects on the gut-liver axis and gut mucosa. Further, we discussed key mechanisms involved in gut dysbiosis to provide an outline of its role in progression to NAFLD and liver cirrhosis. In addition, we also explored the potential role of probiotics as a treatment approach for the prevention and treatment of NAFLD. Based on the latest findings, it is evident that microbiota targeted interventions mostly the use of probiotics have shown promising effects and can possibly alleviate the gut microbiota dysbiosis, regulate the metabolic pathways which in turn inhibit the progression of NAFLD through the gut-liver axis. However, very limited studies in humans are available on this issue and suggest further research work to identify a specific core microbiome association with NAFLD and to discover its mechanism of pathogenesis, which will help to enhance the therapeutic potential of probiotics to NAFLD.

Cloud detection, as an important preprocessing operation for remote sensing (RS) image analysis, has received increasing attention in recent years. Most of the previous cloud detection methods consider the detection as a pixel-wise image classification problem (cloud versus background), which inevitably leads to a category-ambiguity when dealing with the detection of thin clouds. In this article, starting from the RS imaging mechanism on cloud images, we re-examine the cloud detection under a totally different point of view, i.e., to formulate cloud detection as a mixed energy separation between foreground and background images. This process can be further equivalently implemented under a deep learning-based image matting framework with a clear physical significance. More importantly, the proposed method is capable to deal with three different but related tasks, i.e., “cloud detection,” “cloud removal,” and “cloud cover assessment,” under a unified framework. The experimental results on the three satellite image data sets demonstrate the effectiveness of our method, especially for those hard but common examples in RS images, such as the thin and wispy cloud.

Cutting quality and efficiency have always been important indicators of glass laser cutting. Laser scanning modes have two kinds, namely, the spiral and concentric circle scanning modes. These modes can achieve high-performance hole cutting of thick solar float glass using a 532-nm nanosecond laser. The mechanism of the glass laser cutting under these two different scanning modes has been described. Several experiments are conducted to explore the effect of machining parameters on cutting efficiency and quality under these two scanning modes. Results indicate that compared with the spiral scanning mode, the minimum area of edge chipping (218340 µm2) and the minimum Ra (3.01 µm) in the concentric circle scanning mode are reduced by 9.4% and 16.4% respectively. Moreover, the best cutting efficiency scanning mode is 14.2% faster than that in the spiral scanning mode. The best parameter combination for the concentric circle scanning mode is as follows: Scanning speed: 2200 mm/s, number of inner circles: 6, and circle spacing: 0.05 mm. This parameter combination reduces the chipping area and sidewall surface roughness by 8.8% and 9.6% respectively at the same cutting efficiency compared with the best spiral processing parameters. The range of glass processing that can be achieved in the concentric circle scanning mode is wider than that in the spiral counterpart. The analyses of surface topography, white spots, microstructures, and sidewall surface element composition are also performed. The study concluded that the concentric circle scanning mode shows evident advantages in the performance of solar float glass hole cutting.

Objective Given the lack of studies examining the associations between daily weather and air pollution with nightly objective sleep over multiple weeks, we quantified these associations in a prospective cohort of healthy participants with episodic migraine. Methods Ninety-eight participants completed daily electronic diaries and wore an actigraph for an average of 45 days, and a total 4,406 nights of data were collected. Nightly sleep characteristics including duration, wake after sleep onset (WASO), and efficiency were assessed using wrist actigraphy. Daily weather parameters and air pollution levels were collected from local weather station and ground-level air quality monitors. We used linear fixed effects models adjusting for participant, day of the week, and day of the year (for weather analysis), and additionally adjusted for temperature and relative humidity (for air pollution analysis). Results The participants were 35±12 years old and 86 were women. A 10°F higher daily average temperature was associated with 0.88 (95% CI: 0.06, 1.70) minutes longer WASO and 0.14% (95% CI: -0.01%, 0.30%) lower sleep efficiency on that night. A 14 parts per billion (ppb)(interquartile range) higher daily maximum 8-hour ozone was associated with 7.51 (95% CI: 3.23, 11.79) minutes longer sleep duration on that night. Associations did not differ between cold (October-March) and warm (April-September) seasons. Conclusions Higher daily ozone was associated with longer sleep duration and modest associations were observed between higher temperature and lower WASO and lower efficiency.

Based on Resource Based Theory (RBT), the competitiveness of Small and Medium-sized Enterprises (SMEs) depends on the uniqueness of resources used in the production and delivering of goods and services. Moreover, the innovation capability of SMEs is critical in enhancing their uniqueness. Various factors, however, could potentially influence SME innovation performance. This present study thus focuses on how SME innovation performance could be enhanced through the three dimensions of network embeddedness (relational, structural and cognitive). Founded on Resource Dependency Theory (RDT), the study seeks to demonstrate how SMEs could tap into the rich external resources within the networks they are embedded in. The study was based on 388 SMEs selected using a purposive sampling technique. A structured questionnaire was used for the data collection, with the data analyzed by structural equation modeling performed in Amos (v.20). The findings revealed that relational, structural and cognitive embeddedness had a positive effect on innovation performance, while structural embeddedness had the greatest impact on SME innovation performance. As such, SMEs seeking to improve their innovation performance through networks should pay critical attention to the network tie and density. Past studies on network embeddedness and innovation performance have shown conflicting results, and therefore this present study makes a notable contribution to the ongoing debate. Past studies have indicated a positive, negative, inverted u-shape, and even no significant relationship at all between the various dimensions of network embeddedness and innovation performance.

Based on the value co-creation theory, this study proposed a theoretical model of the effects of value co-creation on the growth of social enterprises. Primary data was obtained using field surveys through a close-ended questionnaire from January to June 2019. The respondents were employees of social enterprises working in the Punjab province of Pakistan. Partial least squares structural equation modeling was used for quantitative data analysis and to verify the statistical significance of the direct link between value co-creation and social enterprise growth, and the negative moderating (substituting) effect of environmental dynamics on the relationship between value co-creation and social enterprise growth.

In today’s world, the demand for cosmetic products is increasing at a faster rate than before, and companies have begun to manufacture cosmetic products to cater to the needs of local and international users. This paper aims to explore the relationship between celebrity endorsement, promotional activities, packaging, brand image, and green consumer buying behavior in the cosmetic industry of Pakistan. It also examines the mediating role of the brand image between celebrity endorsement and green consumer buying behavior. The data was collected primarily through a 5 Likert scale questionnaire from the females of the four renowned Universities of Pakistan. The Partial least square structural equation modeling (PLS-SEM) using ADANCO 2.0.1, used for data analysis, and the sample size contained 190 respondents. The findings revealed a significant direct relationship between celebrity endorsement, promotional activities, packaging of a product, and brand image with the green consumer buying behavior (Green CBB). Partial mediation was found between celebrity endorsement and green consumer buying behavior. This study has implications for practitioners and researchers interested in investing in green consumption in Pakistan.

For the privacy concern, the data exchange between the electricity network and the natural gas network is not always possible. It brings challenges for the co-operation of electricity–gas systems. This paper proposes a static equivalent model that replaces the natural gas network, which lays the foundation for electricity–gas co-operation between separate electricity and gas companies. The influence of the natural gas network on the electricity network is represented in this equivalent model, which consists of a constraint equivalent model and a loss equivalent model. In the constraint equivalent model, a set of optimization models is formulated to approximate the feasible region of natural gas-fired units (NGUs) imposed by the operational constraints of the natural gas network. In the loss equivalent model, the natural gas consumption of compressors contributed by NGUs is considered based on the “shortest path principle” using the gas loss as the weight. The proposed static equivalent model is applied in the optimal power flow of electricity–gas systems. Case studies demonstrate the desired accuracy of the proposed method.

Rationale: Whether critical care improvements over the last 10 years extend to all hospitals has not been described. Objectives: To examine the temporal trends of critical care outcomes in minority and non–minority-serving hospitals using an inception cohort of critically ill patients. Measurements and Main Results: Using the Philips Health Care electronic ICU Research Institute Database, we identified minority-serving hospitals as those with an African American or Hispanic ICU census more than twice its regional mean. We examined almost 1.1 million critical illness admissions among 208 ICUs from across the United States admitted between 2006 and 2016. Adjusted hospital mortality (primary) and length of hospitalization (secondary) were the main outcomes. Large pluralities of African American (25%, n = 27,242) and Hispanic individuals (48%, n = 26,743) were cared for in minority-serving hospitals, compared with only 5.2% (n = 42,941) of white individuals. Over the last 10 years, although the risk of critical illness mortality steadily decreased by 2% per year (95% confidence interval [CI], 0.97–0.98) in non–minority-serving hospitals, outcomes within minority-serving hospitals did not improve comparably. This disparity in temporal trends was particularly noticeable among African American individuals, where each additional calendar year was associated with a 3% (95% CI, 0.96–0.97) lower adjusted critical illness mortality within a non–minority-serving hospital, but no change within minority-serving hospitals (hazard ratio, 0.99; 95% CI, 0.97–1.01). Similarly, although ICU and hospital lengths of stay decreased by 0.08 (95% CI, −0.08 to −0.07) and 0.16 (95% CI, −0.16 to −0.15) days per additional calendar year, respectively, in non–minority-serving hospitals, there was little temporal change for African American individuals in minority-serving hospitals. Conclusions: Critically ill African American individuals are disproportionately cared for in minority-serving hospitals, which have shown significantly less improvement than non–minority-serving hospitals over the last 10 years.

Feature selection aims to find a set of features that are concise and have good generalization capabilities by removing redundant, uncorrelated, and noisy features. Recently, the regularized self-representation (RSR) method was proposed for unsupervised feature selection by minimizing the L2,1 norm of residual matrix and self-representation coefficient matrix. In this paper, we find that minimizing the L2,1 norm of the self-representation coefficient matrix cannot effectively extract the features with strong correlation. Therefore, by adding the minimum constraint on the kernel norm of the self-representation coefficient matrix, a new unsupervised feature selection method named low-rank regularized self-representation (LRRSR) is proposed, which can effectively discover the overall structure of the data. Experiments show that the proposed algorithm has better performance on clustering tasks than RSR and other related algorithms.

Background and aims Bile acids play important roles in lipid metabolism. Several studies have found that patients with coronary artery disease (CAD) have lower bile acid fecal excretion compared to individuals without CAD. However, few studies have focused on the roles of more accessible serum total bile acids (TBA) in the progression of CAD. The aim of this study was to explore the potential relationship between fasting serum TBA and the presence of CAD, myocardial infarction (MI) and severity of coronary lesions. Methods A total of 7438 consecutive patients with suspected CAD, who had undergone coronary angiography, were enrolled. The severity of coronary lesions was evaluated using the Gensini score (GS). The relationships between fasting serum TBA and the presence and severity of CAD were evaluated. Results CAD patients had lower serum TBA than individuals without CAD, and patients with MI had lower TBA than those without CAD. Spline analyses showed an L-shaped relationship of the fasting serum TBA with the presence and severity of CAD, and the breakpoint approximated the normal upper limit (10 μmol/L). A lower TBA concentration (less than the median 3.6 μmol/L) was independently and significantly associated with the presence and severity of CAD, especially for the presence of MI (odds ratios 2.04, 95% confidence interval (1.71–2.44), C-index 0.9269). Conclusions Fasting serum TBA level is highly associated with the presence and severity of CAD in patients undergoing coronary angiography for suspected CAD.

Background Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision. Methods We developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria. Results Based solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case. Conclusions The combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.

Purpose The purpose of this paper is to ascertain how perceived service quality mediates employees’ customer orientation and customer satisfaction in the public utility sector. Design/methodology/approach The study focused on the commercial customers (small and medium-scale enterprises– (SMEs)) of Electricity Company of Ghana Ltd. There were 350 SMEs sampled for the study, and each had no more than 99 employees. Respondents were either owner-managers or employee-managers. Structural equation model (SEM) was used in estimating the effects among the variables studied. Findings Most public institutions have a built-in customer base, and therefore places less emphasis on employees’ customer orientation. This notwithstanding, findings revealed that employees’ customer orientation behaviors significantly impacted customers’ perceived service quality and satisfaction toward public institutions. Similarly, customers’ perceived service quality influenced their satisfaction toward public institutions. SMEs serve as an engine for economic growth in an economy, and therefore public institutions must consider their peculiar needs in the delivery of service to them. Originality/value This study pointed out that, employees’ customer orientation behaviors of public institutions have an influence on customers’ perceived service quality and satisfaction. Previous studies on these concepts have largely focused on the private sector, where there are lots of competition. This study also specifically studied commercial customers (SMEs) of public institution, which is quite novel, especially in relation to the concepts studied. And the contribution of SMEs to economic growth makes their study even much more important.

Several studies have demonstrated that allergic asthma can induce atherosclerosis formation in mice. Moreover, allergic asthma and atherosclerosis have been shown to be strongly associated with dyslipidemia. In this study, we investigated the underlying mechanism of allergic asthma-aggravated atherosclerosis-induced cholesterol metabolism disorder in asthmatic apoE-/- mice. We found that allergic asthma increased the expression of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) in the liver and CD36 in the aorta during the acute and advanced stages of atherosclerosis, respectively. These results indicate that cholesterol biosynthesis is increased during acute atherosclerosis and cholesterol uptake and foam cell formation is increased during advanced atherosclerosis. Simvastatin administration significantly ameliorated the aortic root lesion size of asthmatic mice and significantly decreased HMGCR and CD36 expression. However, the expression of the low-density lipoprotein receptor and ATP-binding cassette transporter A1 was markedly increased, indicating that the beneficial effect of statins in allergic asthma and coronary artery disease was mediated, at least in part, by decreasing cholesterol biosynthesis and foam cell formation. In conclusion, allergic asthma aggravates atherosclerosis by regulating cholesterol metabolism in apoE-/- mice. Allergic asthma selectively promotes cholesterol biosynthesis in acute atherosclerosis and increases foam cell formation in advanced atherosclerosis.

This paper employed a structural equation modeling technique to investigate how Development Oriented Non-Governmental Organizations (DONGOs) could complement government efforts through the concept of shared value creation in the educational sector of Ghana. To address this objective, we analyzed whether DONGOs could create shared value by pursuing both social and economic objectives and also how the growing basic needs of education could influence these relationships. The study concluded that basic needs fully mediate social value to shared value and partially mediate economic value to shared value. Moreover, the study revealed that economic value to shared value creation is statistically significant and also demonstrated a positive and significant relationship between social value and shared value. The results further indicate that basic needs have a positive and significant relationship with shared value.

PurposeCell-free DNA (cfDNA) offers a non-invasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancer.Experimental DesignThree simulation datasets were generated from 26 cancer patients to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n=76) from cancer patients (n=35) with six different cancer types were used for validating the performance of cancer monitoring during various treatments.ResultsWe present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across the known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fraction ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fraction ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods.ConclusionOur results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.Translational RelevanceContinuous cancer monitoring is clinically necessary for cancer patients to detect minimal residual disease (MRD), recurrence, and progression, allowing for early intervention and therapy adjustment. Cell-free DNA (cfDNA) in blood has become an appealing option due to its non-invasiveness. Until now, cfDNA-based cancer monitoring methods have been focused on deep sequencing at a few known mutations, which are however insufficient when tumors evolve or new tumors emerge. We present the method, cfTrack, which for the first time uses whole-exome sequencing (WES) of cfDNA to track the full range of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancer. We demonstrate that, even with very low tumor fractions, cfTrack achieves sensitive and specific monitoring of tumor MRD/recurrence/evolution based on both simulation data and a cohort of cancer patients. These findings demonstrate the clinical utility of cfTrack.

Background Adipsin has been identified as a secreted adipokine that plays a critical pathogenic role in metabolic disorders. However, it is not clear regarding the association of circulating adipsin with cardiovascular disease (CVD). This study will explore the association between circulating adipsin and asymptomatic carotid atherosclerosis in Chinese obese adults. Methods A total of 483 obese adult subjects (aged 40 years or older) were enrolled in this study. Serum adipsin concentrations and carotid intima-media thickness (CIMT) were measured to determine these associations. Results Individuals with increased CIMT and asymptomatic carotid atherosclerosis had lower levels of circulating adipsin than controls (both p p p = 0.059) more likely to have increased CIMT and 2.91 times (p = 0.03) more likely to have asymptomatic carotid atherosclerosis than those in the highest quartile in multivariable logistic regression analyses, adjusting for age, gender, current smoking, alcohol consumption, physical activity, BMI, systolic BP, fasting glucose, total cholesterol, HDL-c, and HOMA-IR. However, such associations with circulating adipsin were not noted for atherosclerotic plaque. Conclusions These findings suggest that circulating adipsin concentrations are a potential marker of risks of increased CIMT and asymptomatic carotid atherosclerosis in obese Chinese adults.

Background: Although the advantages of single-incision laparoscopic surgery have been reported in several meta-analyses, the low quality of studies included in the meta-analyses limits the reliability of such a conclusion. In recent years, the number of randomized controlled trials on the efficacy of SILS in colorectal cancer has been on the rise. This update systematic review and meta-analysis of RCTs aims to compare efficacy and safety of SILS and CLS in the patients with colorectal cancer.Methods: Relevant data was searched on the CNKI, Wanfang, VIP, Sinomed, PubMed, Embase, and Cochrane CENTRAL databases from inception until February 5th, 2021. All RCTs comparing SILS and CLS were included. The main outcomes were 30 days of mortality, postoperative complications, intraoperative complications, whereas secondary outcomes were the number of lymph nodes removed, duration of hospital stay, intraoperative blood loss, abdominal incision length, reoperation, readmission, conversion to laparotomy, operation time and anastomotic leakage.Results: A total of 10 RCTs were included, involving 1,133 participants. The quality of the included studies was generally high. No significant difference was found between SILS and CLS in the 30 days mortality rate. The results showed that SILS group had a lower rate of postoperative complications (RR = 0.67, 95% CI: 0.49–0.92), higher rate of intraoperative complications (RR = 2.26, 95%CI: 1.00–5.10), shorter length of abdominal incision (MD = −2.01, 95% CI:−2.42–1.61) (cm), longer operation time (MD = 11.90, 95% CI: 5.37–18.43) (minutes), shorter hospital stay (MD = −1.12, 95% CI: −1.89–0.34) (days) compared with CLS group. However, intraoperative blood loss (MD = −8.23, 95% CI: −16.75–0.29) (mL), number of lymph nodes removed (MD = −0.17, 95% CI: −0.79–0.45), conversion to laparotomy (RR=1.31, 95% CI: 0.48–3.60), reoperation (RR = 1.00, 95% CI: 0.30–3.33) and readmission (RR =1.15, 95% CI: 0.12–10.83) and anastomotic leakage were not significantly different between the two groups.Conclusion: These results indicate that SILS did not has a comprehensive and obvious advantage over the CLS. Surgeons and patients should carefully weigh the pros and cons of the two surgical procedures. Further RCTs are needed to prove long-term outcomes of SILS in colorectal cancer.

Introduction Suboptimal health status (SHS), an intermediate state between chronic disease and health, is characterized by chronic fatigue, non-specific pain, headaches, dizziness, anxiety, depression, and functional system disorders with a high prevalence worldwide. Although some lifestyle factors (e.g. smoking, alcohol consumption, physical exercise) and environmental factors (e.g. air quality, noise, living conditions) have already been studied, few studies can comprehensively illustrate the associations of lifestyle and environment factors with general, physical, mental, and social SHS. Methods A cross-sectional study was conducted among 6750 urban residents aged 14 years or over in five random cities from September 2017 to September 2018 through face-to-face questionnaires. There were 5881 valid questionnaires with a response rate of 87%. A general linear model and structural equation model were developed to quantify the effects of lifestyle behaviors and environment factors on SHS. Results The detection rates of general, physical, mental, and social SHS were 66.7, 67.0, 65.5, and 70.0%, respectively. Good lifestyle behaviors and favorable environment factors positively affected SHS (P β = − 0.418), but the least on social SHS (β = − 0.274). Environment factors had the largest effect on mental SHS (β = 0.286), but the least on physical SHS (β = 0.225). Conclusions Lifestyle behaviors and environment factors were important influencing factors of SHS. Physical SHS was more associated with lifestyle. Lifestyle and environment were similarly associated with mental and social SHS.

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (, It is possible to call single-nucleotide variant (SNV) in cell-free DNA (cfDNA), but the accuracy of detection is often affected by low tumour cfDNA content. Here, the authors develop a method, cfSNV, and show that it can be used even for medium-coverage whole exome sequencing of cfDNA.

Intestinal mucosal barrier dysfunction induced by myocardial ischemia reperfusion (IR) injury often leads to adverse cardiovascular outcomes after myocardial infarction. Early detection and prevention of remote intestinal injury following myocardial IR may help to estimate and improve prognosis after acute myocardial infarction (AMI). This study investigated the protective effect of myocardial ischemic postconditioning (IPo) on intestinal barrier injury induced by myocardial IR and the underlying cellular signaling mechanisms with a focus on the DJ-1. Adult SD rats were subjected to unilateral myocardial IR with or without ischemic postconditioning. After 30 min of ischemia and 120 min of reperfusion, heart tissue, intestine, and blood were collected for subsequent examination. The outcome measures were (i) intestinal histopathology, (ii) intestinal barrier function and inflammatory responses, (iii) apoptosis and oxidative stress, and (iv) cellular signaling changes. IPo significantly attenuated intestinal injury induced by myocardial IR. Furthermore, IPo significantly increased DJ-1, nuclear Nrf2, NQO1, and HO-1 expression in the intestine and inhibited IR-induced apoptosis and oxidative stress. The protective effect of IPo was abolished by the knockdown of DJ-1. Conversely, the overexpression of DJ-1 provided a protective effect similar to that of IPo. Our data indicate that IPo protects the intestine against myocardial IR, which is likely mediated by the upregulation of DJ-1/Nrf2 pathway.

Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset., 9 pages, 6 figures

This study assessed the role of Green Supply Chain Management practices in enhancing the performance and competitive advantage of firms. The study was based on 385 manufacturing firms. Various validity and reliability checks were conducted before the presentation of the actual analysis, which was conducted using Ordinary Least Squares approach, run using SPSS (v. 20). Findings revealed that, Green Procurement Practices, Green Manufacturing Practices, and Green Distribution Practices, had a significant effect on manufacturing firms’ performance and competitive advantage. Although Reverse Logistics had no significant effect on manufacturing firms’ performance, it had a significant effect on their competitive advantage.

Purpose We aimed to evaluate the role of caffeinated beverage intake as a potential trigger of migraine headaches on that day or on the following day. Methods In this prospective cohort study, 101 adults with episodic migraine completed electronic diaries every morning and evening. Ninety-eight participants completed at least 6 weeks of diaries in March 2016-October 2017. Every day, participants reported caffeinated beverage intake, other lifestyle factors, and the timing and characteristics of each migraine headache. We compared a participant's incidence of migraines on days with caffeinated beverage intake to the incidence of migraines among the same individual on days with no intake, accounting for day of week. We used conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals. Results Among 98 participants (86 women, 12 men) with mean age 35.1 years, 83% white, and 10% Hispanic or Latino, the average age when headaches started was 16.3 years. In total, the participants reported 825 migraines during 4467 days of observation. There was a statistically significant nonlinear association between the number of caffeinated beverages and the odds of migraine headache occurrence on that day (P-quadratic trend=.024), though estimates for each level of intake were not statistically significant. The associations varied according to habitual intake and oral contraceptive use. Conclusions There was a nonlinear association between caffeinated beverage intake and the odds of migraine headache occurrence on that day. This suggests that high levels of caffeinated beverage intake may be a trigger of migraine headaches on that day.

Rationale: Cigarette smoke exposure is a risk factor for many lung diseases, and histologic studies suggest that tobacco-related vasoconstriction and vessel loss plays a role in the development of ...

Background Short-term exposure to air pollution has been associated with cardiovascular events, potentially by promoting endothelial cell activation and inflammation. A few large-scale studies have examined the associations and have had mixed results. Methods We included 3820 non-current smoking participants (mean age 56 years, 54% women) from the Framingham Offspring cohort examinations 7 (1998–2001) and 8 (2005–2008), and Third Generation cohort examination 1 (2002–2005), who lived within 50 km of a central monitoring station. We calculated the 1- to 7-day moving averages of fine particulate matter (PM2.5), black carbon (BC), sulfate (SO42-), nitrogen oxides (NOx), and ozone before examination visits. We used linear mixed effect models for P-selectin, monocyte chemoattractant protein 1 (MCP-1), intercellular adhesion molecule 1, lipoprotein-associated phospholipase A2 activity and mass, and osteoprotegerin that were measured up to twice, and linear regression models for CD40 ligand and interleukin-18 that were measured once, adjusting for demographics, life style and clinical factors, socioeconomic position, time, and meteorology. Results We found negative associations of PM2.5 and BC with P-selectin, of ozone with MCP-1, and of SO42- and NOx with osteoprotegerin. At the 5-day moving average, a 5 µg/m3 higher PM2.5 was associated with 1.6% (95% CI: − 2.8, − 0.3) lower levels of P-selectin; a 10 ppb higher ozone was associated with 1.7% (95% CI: − 3.2, − 0.1) lower levels of MCP-1; and a 20 ppb higher NOx was associated with 2.0% (95% CI: − 3.6, − 0.4) lower levels of osteoprotegerin. Conclusions We did not find evidence of positive associations between short-term air pollution exposure and endothelial cell activation. On the contrary, short-term exposure to higher levels of ambient pollutants were associated with lower levels of P-selectin, MCP-1, and osteoprotegerin in the Framingham Heart Study.

Background Whether cognitive decline is related to a higher risk of death independent of the initial cognitive function is inconclusive. Evidence of the association between cognitive decline and mortality among Chinese older people is limited. We aimed to examine whether cognitive decline, assessed by the rate of decrease in the Mini-Mental State Examination (MMSE) score, was associated with mortality independent of initial cognitive function (baseline MMSE score) among Chinese older people. Methods We established two successive and non-overlapping cohorts of older adults nested within the Chinese Longitudinal Healthy Longevity Survey (CLHLS), an ongoing, open, community-based cohort survey conducted every 2–3 years. Cognitive function was measured using the Chinese version of the MMSE. A total of 11,732 older adults who completed two consecutive cognitive function examinations were included and followed for 3 years. A Cox proportional hazards model was used to examine the association of cognitive decline with mortality after adjusting for sociodemographic characteristics, health behaviours, comorbidities and initial cognitive function. Results The mean age was 82.5 years old, and 44.9% (5264/11732) of participants were men. After adjusting for baseline MMSE scores and other covariates, the rate of change in MMSE scores over 3 years was monotonically and positively associated with subsequent 3-year mortality. Compared to those with stable cognitive function, participants with rapid cognitive decline (decline faster than average, a reduction of MMSE scores > 1.62 points/year) had a 75% higher risk of death (hazard ratio = 1.75, 95% confidence interval 1.57–1.95). The association between cognitive decline and mortality was stronger among relatively younger Chinese older people (aged 65–79 years versus 80 years and over) and those with normal cognitive function at baseline (MMSE scores ≥ 24 versus

Evidence suggests that altered DNA methylation plays a causative role in the pathogenesis of various cancers, including hepatocellular carcinoma (HCC). Thus, methylated differently expressed genes (MDEGs) could potentially serve as biomarkers and therapeutic targets in HCC. In the present study, screening four genomics profiling datasets (GSE62232, GSE84402, GSE73003 and GSE57956) enabled us to identify a total of 148 MDEGs. A signature was then established based on the top four MDEGs (BRCA1, CAD, CDC20 and RBM8A). Taking clinical variables into consideration, we constructed a risk score system consisting of the four-MDEG signature and the patients’ clinical features, which was predictive of prognosis in HCC. The prognostic value of the HCC risk score system was confirmed using TCGA HCC samples. The scores were then used to construct a nomogram, performance of which was evaluated using Harrel’s concordance index (C-index) and a calibration curve. The signature-based nomogram for prediction of overall survival in HCC patients exhibited good performance and was superior to traditional staging systems (C-index: 0.676 vs 0.629, P< 0.05). We have thus established a novel risk score system that is predictive of prognosis and is a potentially useful guide for personalized treatment of HCC patients.

Given the progressively deeper integration of distributed energy resources (DERs), evaluating the potential unintentional islanding hazards in distribution networks becomes increasingly important for distribution system planning and operations. In this paper, a rigorous theoretical analysis is used to devise a DER-driven nondetection zone (D $^{2}$ NDZ) method, which is then implemented through a data-driven learning-based approach. Test results indicate that D $^{2}$ NDZ can quickly and effectively estimate the nondetection zones for any given distribution feeders, while avoiding numerous and time-consuming electromagnetic transient simulations. D $^{2}$ NDZ software has been deployed in Eversource Energy, a major power utility company in the northeastern U.S. In practice, D $^{2}$ NDZ reduces utilities engineers’ case study time from months to just a few minutes.

Some innovative works have been done to probabilistic power flow (PPF) analysis for hybrid HVAC and LCC-VSC HVDC system in this paper. Firstly, a unified method considering precise model of converters is proposed to solve a general deterministic power flow (DPF) calculation including hybrid LCC (Line Current Converter) and VSC (Voltage Source Converter), the pure VSC-MTDC (Voltage Source Converter-Multiple Terminal Direct Current) and pure LCC system. Meanwhile, with a large amount of renewable energy sources integrated to the main grid through DC grids, it will impose a stochastic impact on the secure operation of such hybrid AC/DC grids. Therefore, it becomes necessary to model the probabilistic uncertainties and analyze their effects on the operation of hybrid AC/DC systems under different control modes. Nevertheless, most power flow analysis methods for hybrid AC/DC system are still deterministic in nature. Therefore, a probabilistic method based on the combination of Nataf transformation and Latin hypercube sampling (LHS) is developed and proposed to solve this complex PPF problem in an efficient manner, which considers correlated various probabilistic uncertainties, e.g. wind speeds, solar radiations and loads following different types of probability distribution. Finally, the effectiveness of the unified DPF method is validated in a modified IEEE 14-bus system, while the proposed PPF is verified in a modified IEEE 118-bus system and the effects of uncertainties on the diverse operation modes of hybrid AC/DC grids are discussed as well.

Background: Decay products of radioactive materials may attach to ambient fine particles and form radioactive aerosol. Internal ionizing radiation source from inhaled radioactive aerosol may contribute to the fine particulate matter (PM2.5)-inflammation pathway. However, few studies in humans have examined the associations. Objectives: To examine the associations between particle radioactivity and biomarkers of oxidative stress and inflammation among participants from the Framingham Offspring and Third Generation cohorts. Methods: We included 3996 participants who were not current smokers and lived within 50 km from our central air pollution monitoring station. We estimated regional mean gross beta radioactivity from monitors in the northeastern U.S. as a surrogate for ambient radioactive particles, and calculated the 1- to 28-day moving averages. We used linear regression models for fibrinogen, tumor necrosis factor α, interleukin-6, and myeloperoxidase which were measured once, and linear mixed effect models for 8-epi-prostaglandin F2α, C-reactive protein, intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), P-selectin, and tumor necrosis factor receptor-2 that were measured up to twice, adjusting for demographics, individual- and area-level socioeconomic positions, time, meteorology, and PM2.5. We also examined whether the associations differed by median age, sex, diabetes status, PM2.5 levels, and black carbon levels. Results: The mean age was 54 years and 54% were women. An interquartile range (3 × 10−3 pCi/m3) higher beta radioactivity level at the 7-day moving average was associated with 5.09% (95% CI: 0.92, 9.43), 2.65% (1.10, 4.22), and 4.71% (95% CI: 3.01, 6.44) higher levels of interleukin-6, MCP-1, and P-selectin, but with 7.01% (95% CI: −11.64, −2.15) and 2.70% (95% CI: −3.97, −1.42) lower levels of 8-epi-prostaglandin F2α and ICAM-1, respectively. Conclusions: Regional mean particle radioactivity was positively associated with interleukin-6, MCP-1, and P-selectin, but negatively with ICAM-1 and 8-epi-prostaglandin F2α among our study participants. Keywords: Gross beta radiation, Particle radioactivity, Epidemiology, Environment, Inflammation, Oxidative stress

Integrated electrical, natural gas and district heating (IENGDH) systems considering solar thermal CHP (STCHP) plants and electric boilers, are considered one of the effective way to alleviate the problem of wind and solar curtailments. With the randomness of wind, solar and load taken into account, this paper proposes a risk assessment method for analyzing the IENGDH systems with STCHP plants and electric boilers. In the proposed method, unified steady-state power flow considering the coupling conversion characteristics of STCHP plants and electric boilers is analyzed. Then, an optimization model is achieved to minimize the integrated wind, solar and load curtailments by controlling the characteristics of STCHP plants and electric boilers. The risk levels of wind, solar and load curtailments are effectively assessed and considered in the proposed method. Finally, seven risk indices are proposed to quantify the severity of wind, solar and load curtailments in IENGDH systems. The simulation results of two case studies demonstrate the effectiveness of the proposed method.

The data is not always shared among sub-networks due to concerns about information privacy or difficulties in synchronous information exchange. It is difficult or even impossible to obtain the probabilistic power flow (PPF) by the centralised analysis. In this paper, an equivalent model considering static power-frequency characteristics (SPFCs) and renewable uncertainties is proposed. In this model, SPFCs of equivalent loads and generators are derived to retain SPFCs of original external loads and generators, respectively. In addition, probabilistic characteristics and correlations of equivalent loads are formulated based on Cholesky decomposition to preserve original external renewable uncertainties. The proposed model is further applied to PPF and then PPF can be solved via Monte Carlo simulation method, which makes that the PPF results can be obtained when the detailed data of the original external network cannot be shared. Owing to efficiently preserving the external SPFCs and renewable uncertainties in the proposed equivalent model, the accuracy of PPF results can be guaranteed. Simulation results of IEEE 14-bus and IEEE-118 bus systems demonstrate the effectiveness and superiority of the proposed equivalent model and its applications to PPF, compared with existing well known equivalent models and their applications to PPF.

Process energy consumption and environmental impact have been considered as important performance indicators for the sustainable electrical discharge machining (EDM) process. This paper presents a sustainable manufacturing technique known as magnetic field-assisted EDM (MF-EDM) to enhance the machine characteristics for the purpose of reducing the energy consumption and environmental hazards of the conventional EDM machining Ti6Al4V. Firstly, the principles of energy consumption, machining noise impact, and MF-EDM are respectively described. Thereafter, a set of experiments is carried out to investigate the effects of the main process parameters on the electrode wear rate ( E W R ), energy consumption ( S E C ), and environmental impacts (including carbon emission and machining noise) extensively, using the Taguchi method. The results indicate that the pulse on time (ranging from 100 to 200 μs) and magnetic field intensity (ranging from 0.05 to 0.10 T) are the two most significant factors affecting the MF-EDM sustainable manufacturing performance. Furthermore, the optimal process parameters were obtained by optimizing the MF-EDM process economically and environmentally using the modified non-dominated neighbor immune algorithm (M-NNIA) method. Compared to the minimum outputs of the experimental results, the optimal solutions of the E W R , S E C , and machining noise were significantly decreased, by 18.30%, 61.43%, and 20.95%, respectively. Therefore, it can be concluded from the above research that the proposed hybrid MF-EDM technique offers significant advantages and potential for applications in the sustainable manufacturing field.