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Abstract Background Human amniotic membrane (AM) transplantation has been applied to treat ocular surface diseases, including corneal trauma. The focus of much deliberation is to balance the mechanical strength of the amniotic membrane, its resistance to biodegradation, and its therapeutic efficacy. It is commonly observed that the crosslinked human decellularized amniotic membranes lose the functional human amniotic epithelial cells (hAECs), which play a key role in curing the injured tissues. Methods and results In this study, we crosslinked human decellularized amniotic membranes (dAM) with genipin and re-planted the hAECs onto the genipin crosslinked AM. The properties of the AM were evaluated based on optical clarity, biodegradation, cytotoxicity, and ultrastructure. The crosslinked AM maintained its transparency. The color of crosslinked AM deepened with increasing concentrations of genipin. And the extracts from low concentrations of genipin crosslinked AM had no toxic effect on human corneal epithelial cells (HCECs), while high concentrations of genipin exhibited cytotoxicity. The microscopic observation and H&E staining revealed that 2 mg/mL genipin-crosslinked dAM (2 mg/mL cl-dAM) was more favorable for the attachment, migration, and proliferation of hAECs. Moreover, the results of the CCK-8 assay and the transwell assay further indicated that the living hAECs’ tissue-engineered amniotic membranes could facilitate the proliferation and migration of human corneal stromal cells (HCSCs) in vitro. Conclusions In conclusion, the cl-dAM with living hAECs demonstrates superior biostability and holds significant promise as a material for ocular surface tissue repair in clinical applications.

Hepatocellular carcinoma (HCC) represents the most prevalent form of primary liver cancer and has a high mortality rate. Caspase-8 plays a pivotal role in an array of cellular signaling pathways and is essential for the governance of programmed cell death mechanisms, inflammatory responses, and the dynamics of the tumor microenvironment. Dysregulation of caspase-8 is intricately linked to the complex biological underpinnings of HCC. In this manuscript, we provide a comprehensive review of the regulatory roles of caspase-8 in apoptosis, necroptosis, pyroptosis, and PANoptosis, as well as its impact on inflammatory reactions and the intricate interplay with critical immune cells within the tumor microenvironment, such as tumor-associated macrophages, T cells, natural killer cells, and dendritic cells. Furthermore, we emphasize how caspase-8 plays pivotal roles in the development, progression, and drug resistance observed in HCC, and explore the potential of targeting caspase-8 as a promising strategy for HCC treatment.

Abstract sulfur-containing amino acids have been reported to patriciate in gene regulation, DNA methylation, protein synthesis and other physiological or pathological processes. In recent years, metabolism-related molecules of sulfur-containing amino acids affecting the occurrence, development and treatment of tumors have been implicated in various disorders, especially in leukemia. Here, we summarize current knowledge on the sulfur-containing amino acid metabolism pathway in leukemia and examine ongoing efforts to target this pathway, including treatment strategies targeting (a) sulfur-containing amino acids, (b) metabolites of sulfur-containing amino acids, and (c) enzymes and cofactors related to sulfur-containing amino acid metabolism in leukemia. Future leukemia therapy will likely involve innovative strategies targeting the sulfur-containing amino acid metabolism pathway.

Abstract In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the pivotal role of microglia in AD pathology, our analysis revealed 11 distinct microglial subclusters, with 4 exhibiting obviously alterations in AD and HC groups. The investigation of cell–cell communication networks unveiled intricate interactions between AD-related microglia and various cell types within the central nervous system (CNS). Integration of WGCNA and scRNA-seq facilitated the identification of critical genes associated with AD-related microglia, providing insights into their involvement in processes such as peptide chain elongation, synapse-related functions, and cell adhesion. The identification of 9 hub genes, including USP3, through the least absolute shrinkage and selection operator (LASSO) and COX regression analyses, presents potential therapeutic targets. Furthermore, the development of a CNN-based model showcases the application of deep learning in enhancing diagnostic accuracy for AD. Overall, our findings significantly contribute to unraveling the molecular intricacies of microglial responses in AD, offering promising avenues for targeted therapeutic interventions and improved diagnostic precision.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system’s capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Water allocation can be evaluated from various economic, environmental, food security, and climate aspects. This paper was conducted to develop an optimal water allocation strategy with an economic framework. Game theory has been used to determine the planning structure. A co-operative technique based on the core solution, Shepley value, and nucleolus solution has been incorporated into the decision-making system and the results of the proposed model have been calculated based on the long-term information of the downstream cropping pattern in the Zhanghe irrigation network, Hubei Province, China. Five components of rice planting including growth period, yield production, water consumption, selling price, and cultivated area have been evaluated to find the optimal scenarios. Results showed that the application of co-operative game theory can improve the economic index and water-saving potential in agriculture. Furthermore, the proposed simulation confirmed that the economic effects were important for understanding and planning for irrigation efficiency and water-saving potential. HIGHLIGHTS Water allocation can be evaluated from various economic, environmental, food security, and climate aspects.; An optimal water allocation strategy has been developed with an economic framework.; Game theory has been used to determine the planning structure.;

Objective To investigate the correlation between pulmonary hypertension (PH) and echocardiographic parameters in patients with chronic kidney disease (CKD). Methods PubMed, Embase, Web of Science, Cochrane, VIP, CNKI, and Wanfang databases were systematically searched for articles published from inception to 19 May 2023. Study quality was estimated using the Quality Assessment of Case-Control Studies tool. Forest plots were drawn using R language software. The “metacor” function in the “meta” package was utilized for meta-analysis of the r-values and their standard errors. Heterogeneity and sensitivity analyses were carried out, with the main outcomes as r-value, p-value, and I2 value. Results Eleven studies were included, with 1,809 CKD patients. The correlations between 12 echocardiographic parameters and PH were analyzed. Except for FS and LVEF which were negatively correlated with CKD-PH, the other 10 parameters were positively correlated with CKD-PH. Among them, LA was highly correlated with CKD-PH (0.70 < r < 0.89); LVDD, RA, RV, LVMI, and LVDS were moderately correlated with CKD-PH (0.40 < r < 0.69); while PA, IVS, LVPW, SV, FS, and LVEF were lowly correlated with CKD-PH (0.20 < r < 0.39). The synthesized estimates were stable against heterogeneity. Conclusion CKD-PH patients may have large cardiac chambers, thickened septal tissue on both sides of the chambers, reduced pulmonary artery flow rates, and decreased left ventricular function.

Background: Anti-pandemic fatigue has inevitably set in owing to the high intensity and prolonged presence of pandemic preventive measures. Globally, COVID-19 remains severe; however, pandemic fatigue may lead to less efficient viral control. Methods: A total of 803 participants residing in Hong Kong interviewed via telephone using a structured questionnaire. Linear regression was employed to test the corelates of anti-pandemic fatigue and the moderators that could potentially impact the appearance of fatigue. Results: When confounding effects of demographic factors (e.g., age, gender, educational attainment, and economic activity status) were avoided, daily hassles were found to be a core factor associated with anti-pandemic fatigue (B =0.369, SE =0.049, p = 0.000). For people with a higher level of pandemic-related knowledge and fewer obstacles brought about by preventive measures, the impact of daily hassles on pandemic fatigue weakened. Moreover, when pandemic-related knowledge was high, there was no positive association between adherence and fatigue. Conclusions: This study confirms that daily hassles can lead to anti-pandemic fatigue, which can be mitigated by improving the general public’s understanding of the virus and developing more convenient measures.

BackgroundSystemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.MethodsIn this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA–miRNA–mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.ResultsThe pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.ConclusionsThis study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

Relationship satisfaction is at the core of a robust social life and is essential to mental health. The positive and negative semantic dimensions of the relationship satisfaction (PN-SMD) scale is considered in the field of relationship studies to be a reliable tool for assessing the quality of a person’s interpersonal relationships. This study evaluated the psychometric properties of the PN-SMD scale by conducting multidimensional item response theory (MIRT) and differential item functioning (DIF) analyses, both of which are emerging assessment methods that focus on individual items. We recruited 511 Chinese undergraduate students for this study. Construct validity, internal consistency, and concurrent validity were assessed, and MIRT and DIF analyses were conducted. Five of the 14 items were found to have gender-based DIF traits, affecting the scale’s construct validity. A revised nine-item scale (DIF items excluded) had a significantly better model fit and demonstrated comparable concurrent validity to the original scale. The implications of our results and future research directions are discussed.

To study the biological functions and applications of human amniotic epithelial cell-derived extracellular vesicles (hAEC-EVs), the cargos of hAEC-EVs were analyzed using miRNA sequencing and proteomics analysis. The hAECs and hAEC-EVs in this study had specific characteristics. Multi-omics analyses showed that extracellular matrix (ECM) reorganization, inhibition of excessive myofibroblasts, and promotion of target cell adhesion to the ECM were their primary functions. We evaluated the application of hAEC-EVs for corneal alkali burn healing in rabbits and elucidated the fundamental mechanisms. Slit-lamp images revealed that corneal alkali burns induced central epithelial loss, stromal haze, iris, and pupil obscurity in rabbits. Slit-lamp examination and histological findings indicated that hAEC-EVs facilitated re-epithelialization of the cornea after alkali burns, reduced scar formation and promoted the restoration of corneal tissue transparency. Significantly fewer α-SMA-positive myofibroblasts were observed in the hAEC-EV-treated group than the PBS group. HAEC-EVs effectively promoted the proliferation and migration of hCECs and hCSCs in vitro and activated the focal adhesion signaling pathway. We demonstrated that hAEC-EVs were excellent cell-free candidates for the treatment of ECM lesion-based diseases, including corneal alkali burns. HAEC-EVs promoted ECM reorganization and cell adhesion of target tissues or cells via orderly activation of the focal adhesion signaling pathway.

With the increase in renewable energy penetration, energy deviation settlement penalties are imposed on incremental distribution and retail companies (IDRCs). Most IDRCs are at financial risk. Given this background, a bidding model of the two-stage dynamic alliance is proposed to maximize IDRCs’ profits. In the first stage, potential alliances of IDRCs are established by the gravity model. In the second stage, the bi-level bidding model is modeled in energy and primary frequency regulation markets, where the upper level maximizes alliance profits by considering the cooperation costs and alliance constraints, and the lower level simulates market clearing. Then, the bi-level model is transformed into a mixed-integer linear programming model by the strong duality theory, Karush–Kuhn–Tucker conditions, and large M method. Verified by the complex network theory, results show that a complex alliance network has a high transmission efficiency and capacity to resist risk. Moreover, IDRCs with large capacity and superior location are important in the alliance. In addition, the proposed model increases the satisfaction rate by 20%, which increases IDRCs’ profits and reduces cooperation costs.

Pancreatic cancer is accompanied by poor prognosis and accounts for a significant number of deaths every year. Since Psoralea corylifolia L. (PCL) possesses a broad spectrum of bioactivities, it is commonly used in traditional Chinese medicine. The study explored potential antitumor agents of PCL and underlying mechanisms in vitro and vivo. Based on network pharmacology, bioinformatics, and molecular docking, we considered isobavachalcone (IBC) as a valuable compound. The activity and potential mechanisms of IBC were investigated by RT-qPCR, immunohistochemistry, immunofluorescence, and flow cytometry. It was confirmed that IBC could inhibit Panc 02 cell proliferation and induce apoptosis via increasing the production of reactive oxygen species. IBC could attenuate the weight of solid tumors, increase CD8+ T cells, and reduce M2 macrophages in the tumor tissue and spleen. Another promising finding was that IBC alleviated the proportion of myeloid-derived suppressor cells (MDSCs) in the tumor tissue but had no change in the spleen. The study of pharmacological effects of IBC was carried out and suggested IBC restrained M2-like polarization of RAW 264.7 cells by inhibiting the expression of ARG1 and MRC1 and suppressed the expression of ARG1 and TGF-β in bone marrow-derived MDSC. In summary, this research screened IBC as an antineoplastic agent, which could attenuate the growth of pancreatic cancer via activating the immune activity and inducing cell apoptosis. It might be a reference for the antitumor ability of IBC and the treatment of the tumor microenvironment in pancreatic cancer.

Abstract Background Systemic sclerosis (SSc) is a disease that features severe fibrosis of the skin and lacks effective therapy. Bone marrow mesenchymal stem cell (BMSC)-derived extracellular vesicles (EVs) are potential stem cell-based tools for the treatment of SSc. Methods BMSCs were isolated from the bone marrow of mice and identified with surface markers according to multilineage differentiation. EVs were isolated from the BMSC culture medium by ultracentrifugation and identified with a Nanosight NS300 particle size analyzer, transmission electron microscopy (TEM), and western blot. The microRNAs (miRNAs) of BMSC-derived EVs (BMSC-EVs) were studied via miRNA sequencing (miRNA-seq) and bioinformatic analysis. An SSc mouse model was established via subcutaneous bleomycin (BLM) injection, and the mice were treated with BMSCs or BMSC-derived EVs. Skin tissues were dissociated and analyzed with H&E staining, RNA sequencing (RNA-seq), western blot, and immunohistochemical staining. Results Evident pathological changes, like fibrosis and inflammation, were induced in the skin of BLM-treated mice. BMSCs and BMSC-EVs effectively intervened such pathological manifestations and disease processes in a very similar way. The effects of the BMSC-EVs were found to be caused by the miRNAs they carried, which were proven to be involved in regulating the proliferation and differentiation of multiple cell types and in multiple EV-related biological processes. Furthermore, TGF-β1-positive cells and α-SMA-positive myofibroblasts were significantly increased in the scleroderma skin of BLM-treated mice but evidently reduced in the scleroderma skin of the EV-treated SSc group. In addition, the numbers of mast cells and infiltrating macrophages and lymphocytes were evidently increased in the skin of BLM-treated mice but significantly reduced by EV treatment. In line with these observations, there were significantly higher mRNA levels of the inflammatory cytokines Il6, Il10, and Tnf-α in SSc mice than in control mice, but the levels decreased following EV treatment. Through bioinformatics analysis, the TGFβ and WNT signaling pathways were revealed to be closely involved in the pathogenic changes seen in mouse SSc, and these pathways could be therapeutic targets for treating the disease. Conclusions BMSC-derived EVs could be developed as a potential therapy for treating skin dysfunction in SSc, especially considering that they show similar efficacy to BMSCs but have fewer developmental regulatory requirements than cell therapy. The effects of EVs are generated by the miRNAs they carry, which alleviate SSc pathogenic changes by regulating the WNT and TGFβ signaling pathways.

In this study, the cooperative resilience of cyber–physical power systems under hybrid attacks is investigated. First, a detection model of physical attacks depending on the residual of the output impedance angle is established. Second, by analyzing the encrypted communication between physical and cyber systems, a detection algorithm for cyberattacks is proposed. Then, by using an enumeration method, islanded cyber–physical power systems are built with non-attacked and repaired parts. Moreover, to save resilient resources, cooperative optimization is established after the individual optimization of islanded cyber and physical systems. Since the building and optimization are executed alternately, the topology of the systems is dynamic. Finally, simulation results demonstrate the effectiveness of the proposed method.

The output of wind turbine is volatile and difficult to predict. Energy storage can help wind turbine offset the deviation between forecast and actual output. Based on the concept of sharing economy, there will be more alliance for wind turbines and energy storage in the electricity market. However, an open question is how the wind-energy storage alliance’s participation affects market clearing and the profits of market participants. Therefore, a stochastic bi-level optimization model is proposed to describe the bidding behavior of wind-energy storage alliances in energy and frequency regulation markets. At the same time, a new quantitative index of bidding behavior is defined—regulation participation ratio. Considering the uncertainty of wind turbine output, the profits of wind-energy storage alliance are maximized in the upper level. The lower level minimizes the power purchase cost of distribution system operator (DSO) for the joint market clearing. The bi-level model is transformed into a mixed integer linear programming (MILP) model by Karush-Kuhn-Tucker (KKT) conditions, strong duality theory and large M method. Regulation participation ratio is set to different values in the case analysis, so as to analyze the influence of the alliance’s bidding behavior on market. Moreover, the economic impact of alliance on wind turbine and energy storage is compared.

Pichia kudriavzevii is an emerging non-conventional yeast which has attracted increased attention for its application in food and biotechnology areas. It is widespread in various habitats and often occurs in the spontaneous fermentation process of traditional fermented foods and beverages. The contributions of P. kudriavzevii in degrading organic acid, releasing various hydrolase and flavor compounds, and displaying probiotic properties make it a promising starter culture in the food and feed industry. Moreover, its inherent characteristics, including high tolerance to extreme pH, high temperature, hyperosmotic stress and fermentation inhibitors, allow it the potential to address technical challenges in industrial applications. With the development of advanced genetic engineering tools and system biology techniques, P. kudriavzevii is becoming one of the most promising non-conventional yeasts. This paper systematically reviews the recent progress in the application of P. kudriavzevii to food fermentation, the feed industry, chemical biosynthesis, biocontrol and environmental engineering. In addition, safety issues and current challenges to its use are discussed.

Stigmatization associated with the coronavirus disease 2019 (COVID-19) is expected to be a complex issue and to extend into the later phases of the pandemic, which impairs social cohesion and relevant individuals' well-being. Identifying contributing factors and learning their roles in the stigmatization process may help tackle the problem. This study quantitatively assessed the severity of stigmatization against three different groups of people: people from major COVID-19 outbreak sites, those who had been quarantined, and healthcare workers; explored the factors associated with stigmatization within the frameworks of self-categorization theory and core social motives; and proposed solutions to resolve stigma. The cross-sectional online survey was carried out between April 21 and May 7, 2020, using a convenience sample, which yielded 1,388 valid responses. Employing data analysis methods like multivariate linear regression and moderation analysis, this study yields some main findings: (1) those from major COVID-19 outbreak sites received the highest level of stigma; (2) factors most closely associated with stigmatization, in descending order, are objectification and epidemic proximity in an autonomic aspect and fear of contracting COVID-19 in a controllable aspect; and (3) superordinate categorization is a buffering moderator in objectification–stigmatization relationship. These findings are important for further understanding COVID-19-related stigma, and they can be utilized to develop strategies to fight against relevant discrimination and bias. Specifically, reinforcing superordinate categorization by cultivating common in-group identity, such as volunteering and donating for containment of the pandemic, could reduce objectification and, thus, alleviate stigma.

Multiple atrial septal defects (ASDs) are one type of secundum ASD, most of which have an atrial septal aneurysm or long interdefect distance. In our retrospective single-center study, we reviewed different closure strategies for multiple ASDs. We analyzed 50 patients who underwent percutaneous transcatheter closure from May 2011 to July 2019. Information on the patients' characteristics, operation procedure, occluder selection, and complications was collected. According to the feature of the defects and device choice, multiple ASDs were divided into five groups. A successful operation was achieved in every patient. A total of 50 patients were implanted with 58 devices, with 26 patients implanted with a single standard ASD occluder (ASDO); six patients were implanted with double standard ASDOs, and only one patient was implanted with three standard ASDOs. There were 17 patients whose closure was made using the small-waist–big-edge ASDO. Seventy-six percent of the patients (38/50) had an immediate residual shunt. During the mean follow-up of 25.76 ± 22.53 months, the complete closure rate was 92%. Except for two patients with a transient atrioventricular block, individualized experience with percutaneous transcatheter closure for multiple ASDs was effective in a single-center study. After a mid- to long-term follow-up, the multiple ASDOs and small-waist–big-edge ASDO had no serious adverse events or complications.

Type 1 diabetes mellitus (T1DM) causes cognitive changes in children, which may be due to deficits in brain functions. It is unclear whether T1DM children will have brain functional changes during the initial stage of the disease. We aimed to investigate the changes in the functional brain network topology in children with new-onset T1DM. In this study, 35 new-onset T1DM children and 33 age-, sex-matched healthy controls underwent resting-state fMRI. The whole brain functional connectivity density (FCD) analysis and seed-based functional connectivity (FC) analysis were performed to investigate the changes in functional brain networks in new-onset T1DM children when compared with the controls. Pearson correlational analysis was used to explore the correlation between FCD value of differential brain areas and clinical variables in T1DM children. Compared with the controls, children with new-onset T1DM exhibited significantly decreased FCDs of the right inferior temporal gyrus (ITG) and the right posterior cingulate cortex (PCC). In the subsequent FC analysis, decreased FC was found between right PCC and right cuneus and increased FC was found between right ITG and left orbital part of inferior frontal gyrus in children with new-onset T1DM compared to the controls. The FCD values of right ITG and PCC did not correlate with HbA1c, blood glucose level before imaging, and full-scale intelligence quotient (IQ) in T1DM children. These results revealed that T1DM affect the functional activity of the immature brain at the initial stage. These findings also indicate a decrease in regional brain function and abnormalities in temporal-frontal and limbic-occipital circuitry in children with new-onset T1DM, and highlight the effects of T1DM on children's brain networks involved in visual process and memory, which may contribute to the cognition impairments observed in children with T1DM.

Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. Traditional solutions typically use relational databases to manage electric power data. However, relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules. We evaluate the performance of our system through a series of experiments. We also show how HBase’s parameters can be tuned to improve the efficiency of our system.

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs' dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.

Based on four thawing methods (still air, still water, ultrasonic wave, and microwave) and single-factor tests, we established a four-factor three-level response surface methodology for a regression model (four factors: pH, drip loss rate, cooking loss rate, protein content). The optimal combined thawing method for beef rib-eye is: microwave thawing (35 s work/10 s stop, totally 170 s) until beef surfaces soften, then air thawing at 15°C until the beef centre temperature reaches -8°C, and finally ultrasonic thawing at 220 W until the beef centre temperature rises to 0°C. With this method, the drip loss rate is 1.9003%, cooking loss rate is 33.3997%, and protein content is 229.603 μg, which are not significantly different from the model-predicted theoretical results (P ≥ 0.05).

Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server’s network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server’s free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server’s data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL’s specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server’s free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds.