Effect of wall-disruption on nutrient composition and in vitro digestion of camellia and lotus bee pollens

The nutrient digestion,absorption and biological activity of bee pollen may be limited due to the complex pollen wall.Here,the effect of superfine grinding technology on the release of nutrients from bee pollen were investigated,and their antioxidant activities and in vitro digestion were explored in this study.Results showed that the content of nutrients in bee pollen increased after wall disruption.Among them,fat content increased by 22.55%-8.31%,protein content increased by 0.54%-4.91%,starch content increased by 36.31%-48.64%,soluble sugar content increased by 20.57%-29.67%,total phenolic acid content increased by 11.73%-86.98%and total flavonoids content increased by 14.29%-24.79%.At the same time,the antioxidant activity increased by 14.84%-46.00%.Furthermore,the active components such as phenolic compounds in the wall-disruption bee pollen were more readily to be released during the in vitro digestion,and easier to be absorbed because of their higher bioaccessibility.Antioxidant activities during in vitro digestion were also improved in walldisruption bee pollen.These findings provide evidence that bee pollen wall disruption was suggested,thus,it is more conducive to exerting the value of bee pollen in functional foods.

Improved training framework in a neural network model for disruption prediction and its application on EXL-50

A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling.To mitigate these issues,an improved training framework has been proposed.In this approach,soft labels from previous training serve as teachers to supervise the further learning process;this has lead to a significant improvement in predictive model performance.Notably,this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism.This improved training framework introduces an instance-specific label smoothing method,which reflects a more nuanced model assessment on the likelihood of a disruption.It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.

AGV Scheduling Optimization of Automated Port Based on Disruption Management

The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.

The AI Paradox: Mapping the Unintended Disruptions in IT and Beyond

In the realm of Artificial Intelligence (AI), there exists a complex landscape where promises of efficiency and innovation clash with unforeseen disruptions across Information Technology (IT) and broader societal realms. This paper sets out on a journey to explore the intricate paradoxes inherent in AI, focusing on the unintended consequences that ripple through IT and beyond. Through a thorough examination of literature and analysis of related works, this study aims to shed light on the complexities surrounding the AI paradox. It delves into how this paradox appears in various domains, such as algorithmic biases, job displacement, ethical dilemmas, and privacy concerns. By mapping out these unintended disruptions, this research seeks to offer a nuanced understanding of the challenges brought forth by AI-driven transformations. Ultimately, its goal is to pave the way for the responsible development and deployment of AI, fostering a harmonious integration of technological progress with societal values and priorities.

Circadian rhythm disruption and endocrine-related tumors

This review delved into the intricate relationship between circadian clocks and physiological processes,emphasizing their critical role in maintaining homeo-stasis.Orchestrated by interlocked clock genes,the circadian timekeeping system regulates fundamental processes like the sleep-wake cycle,energy metabolism,immune function,and cell proliferation.The central oscillator in the hypothalamic suprachiasmatic nucleus synchronizes with light-dark cycles,while peripheral tissue clocks are influenced by cues such as feeding times.Circadian disruption,linked to modern lifestyle factors like night shift work,correlates with adverse health outcomes,including metabolic syndrome,cardiovascular diseases,infec-tions,and cancer.We explored the molecular mechanisms of circadian clock genes and their impact on metabolic disorders and cancer pathogenesis.Specific associ-ations between circadian disruption and endocrine tumors,spanning breast,ovarian,testicular,prostate,thyroid,pituitary,and adrenal gland cancers,are highlighted.Shift work is associated with increased breast cancer risk,with PER genes influencing tumor progression and drug resistance.CLOCK gene expression correlates with cisplatin resistance in ovarian cancer,while factors like aging and intermittent fasting affect prostate cancer.Our review underscored the intricate interplay between circadian rhythms and cancer,involving the regulation of the cell cycle,DNA repair,metabolism,immune function,and the tumor microenvir-onment.We advocated for integrating biological timing into clinical consider-ations for personalized healthcare,proposing that understanding these connec-tions could lead to novel therapeutic approaches.Evidence supports circadian rhythm-focused therapies,particularly chronotherapy,for treating endocrine tumors.Our review called for further research to uncover detailed connections between circadian clocks and cancer,providing essential insights for targeted treatments.We emphasized the importance of public health interventions to mitigate lifestyle-related circadian disruptions and underscored the critical role of circadian rhythms in disease mechanisms and therapeutic interventions.

Disruption of sleep patterns among secondary school adolescents

Objectives:This study aims to assess sleep disorders among secondary school adolescents and explore the relationship between sociodemographic factors(age,gender,household income,and sleep duration)and the occurrence of these disorders.Methods:A quantitative,descriptive,cross-sectional study,was conducted from November 20th,2022,to May 25th,2023,involving 200 secondary school students selected through convenience sampling.Data collection utilized a structured questionnaire divided into sociodemographic and sleep disorder sections.Validity was ensured by a panel of ten experts,and reliability was confirmed using Cronbach’s Alpha(0.77).Statistical analysis employed SPSS version 26.Results:Findings revealed that a majority of participants(70.5%)had low-level sleep disorders,followed by moderate disorders represented(29%).Significant associations were found between sleep disorders and gender(P=0.000),economic status for family(P=0.020),and nightly sleep duration(P=0.016).However,no significant relationship was observed between sleep disorders and family structure or age(P>0.05).Conclusions:The study highlights that most secondary school students experience mild sleep disorders,followed by moderate disorders.Notably,gender,income,and sleep duration showed significant correlations with sleep disorders.

Emerging roles of astrocytes in blood-brain barrier disruption upon amyloid-beta insults in Alzheimer's disease

Blood-brain barrier disruption occurs in the early stages of Alzheimer’s disease.Recent studies indicate a link between blood-brain barrier dysfunction and cognitive decline and might accelerate Alzheimer’s disease progression.Astrocytes are the most abundant glial cells in the central nervous system with important roles in the structural and functional maintenance of the blood-brain barrier.For example,astrocytic cove rage around endothelial cells with perivascular endfeet and secretion of homeostatic soluble factors are two major underlying mechanisms of astrocytic physiological functions.Astrocyte activation is often observed in Alzheimer’s disease patients,with astrocytes expressing a high level of glial fibrillary acid protein detected around amyloid-beta plaque with the elevated phagocytic ability for amyloid-beta.Structural alte rations in Alzheimer’s disease astrocytes including swollen endfeet,somata shrinkage and possess loss contribute to disruption in vascular integrity at capillary and arte rioles levels.In addition,Alzheimer’s disease astrocytes are skewed into proinflammatory and oxidative profiles with increased secretions of vasoactive mediators inducing endothelial junction disruption and immune cell infiltration.In this review,we summarize the findings of existing literature on the relevance of astrocyte alte ration in response to amyloid pathology in the context of blood-brain barrier dysfunction.First,we briefly describe the physiological roles of astrocytes in blood-brain barrier maintenance.Then,we review the clinical evidence of astrocyte pathology in Alzheimer’s disease patients and the preclinical evidence in animal and cellular models.We further discuss the structural changes of blood-brain barrier that correlates with Alzheimer’s disease astrocyte.Finally,we evaluate the roles of soluble factors secreted by Alzheimer’s disease astrocytes,providing potential molecular mechanisms underlying blood-brain barrier modulation.We conclude with a perspective on investigating the therapeutic potential of targeting astrocytes for blood-brain barrier protection in Alzheimer’s disease.

Disruption prediction based on fusion feature extractor on J-TEXT

Predicting disruptions across different tokamaks is necessary for next generation device.Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem.The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other tokamaks.Based on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks.Furthermore,an FFE-based disruption predictor on J-TEXT is demonstrated.The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics.Strong inductive bias on tokamak diagnostics data is introduced.The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning method.Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.

Development of electromagnetic pellet injector for disruption mitigation of tokamak plasma

Disruption remains to be a serious threat to large tokamaks like the International Thermonuclear Experimental Reactor(ITER).The injection speed of disruption mitigation systems(DMS)driven by high pressure gas is limited by the sound speed of the propellant gas.When extrapolating to ITER-like tokamaks,long overall reaction duration and shallow penetration depth due to low injection speed make it stricter for plasma control system to predict the impending disruptions.Some disruptions with a short warning time may be unavoidable.Thus,a fast time response and high injection speed DMS is essential for large scale devices.The electromagnetic pellet-injection(EMPI)system is a novel massive material injection system aiming to provide rapid and effective disruption mitigation.Based on the railgun concept,EMPI can accelerate the payload to over 1000 m/s and shorten the overall reaction time to a few milliseconds.To verify the injection ability and stability of the EMPI,the prototype injector EMPI-1 has been designed and assembled.The preliminary test has been carried out using a 5.9 g armature to propel a dummy pellet and the results suggest that the EMPI configuration has a great potential to be the DMS of the large scale fusion devices.

Comparison of different noble gas injections by massive gas injection on plasma disruption mitigation on Experimental Advanced Superconducting Tokamak

Massive gas injection(MGI)is a traditional plasma disruption mitigation method.This method directly injected massive gas into the pre-disruption plasma and had been developed on the Experimental Advanced Superconducting Tokamak(EAST).Different noble gas injection experiments,including He,Ne,and Ar,were performed to compare the mitigation effect of plasma disruption by evaluating the key parameters such as flight time,pre-thermal quench(pre-TQ),and current quench(CQ).The flight time was shorter for low atomic number(Z)gas,and the decrease in flight time by increasing the amount of gas was insignificant.However,both pre-TQ and CQ durations decreased considerably with the increase in gas injection amount.The effect of atomic mass on pre-TQ and CQ durations showed the opposite trend.The observed trend could help in controlling CQ duration in a reasonable area.Moreover,the analysis of radiation distribution with different impurity injections indicated that low Z impurity could reduce the asymmetry of radiation,which is valuable in mitigating plasma disruption.These results provided essential data support for plasma disruption mitigation on EAST and future fusion devices.

The Effects of Disruptive Thinking on the Food Industry*

This study aimed to understand disruptive thinking and how its ideas can change the food industry. This was achieved by identifying, studying, and understanding the impacts, current trends, and different disruptive ideas and innovations emerging in the food industry. The study was conducted through interpretive research philosophy by carrying out secondary data collection processes, where both qualitative and quantitative information was presented. Deductive approaches were also selected to apply existing theories and models, which were used to construct research hypotheses and present detailed findings. The study finds that, with disruptive thinking, enhancements in the product life cycle, new flavors, and improvements in food packaging have been possible. The supply chain, which is always considered a complex part of the food industry, has been streamlined, offering greater transparency and real-time tracking and improving quality control across distribution systems.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Scheduling on tractor and trailer transportation considering the influence of disrupted events based on the contract net and simulated annealing algorithm

To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.

Effects of Blood Cell Membrane Disruption by Ultrasonic Technology on the Quality of Duck Blood Tofu

[Objective] The aim was to improve the quality of the duck blood tofu by ultrasonic technology. [Method] Ultrasonic technology was used to break the blood cell membrane. The optimal ultrasonic condition was determined by single factor de- sign, and the differences in sensory quality and nutritional value between the duck blood tofu treated with and without ultrasonication were studied. [Result] The optimal ultrasonic power was 400 W and the optimal ultrasonic time was 6 min. Under these conditions, the content of duck blood haemoglobin increased by 43.46%, the content of iron increased by 10.85%, and the breakage rate was 100%. The a＊ value of the duck blood tofu increased significantly under ultrasonic treatment. Both internal micro-structure and texture parameters proved that the ultrasonic contributed to the formation of gel structure. After treatment by artificial gastric juice and artifi- cial intestinal juice, the total amino acids in the digestive juice were 9.25% higher than that of the duck blood tofu without ultrasonic treatment. [Conclusion] The duck blood tofu under ultrasonic treatment would be beneficial to human body absorption, and the nutrition value would be higher as well.

Supply chain coordination mechanisms under asymmetric information with retailer cost disruptions

A two-level supply chain model involving one supplier and one retailer with linear demand is developed, and supply chain coordination mechanisms under asymmetric information （the retailer＇ s cost structure is asymmetric information） are proposed by employing game theory in two scenarios： coordination mechanisms under asymmetric information in a regular scenario （without disruption）; and coordination mechanisms under asymmetric information in an irregular scenario （ with retailer cost disruptions）. It is optimal for the supply chain to maintain the original production plan and to guarantee a steadily running system if variations of retailer costs are sufficiently low and do not exceed an upper bound. This shows that the original production plan has certain robustness under disruptions. Decisions must be re-made if a retailer＇ s cost change is greater and exceeds an upper bound. Impacts of retailer cost disruptions on the order quantity, the retail price, the wholesale price and each party＇ s as well as the system＇ s expected profits are investigated through numerical analyses.

Coordinating pricing strategies in supply chain under asymmetric information and disruptions

The coordinating pricing strategies with asymmetric cost information under disruptions are investigated in a one-supplier-one-retailer supply chain system.While the retailer's cost structure is asymmetric information,supply chain pricing contract models(a wholesale price contract and an all-unit quantity discount contract)under asymmetric information are proposed by employing the principal-agent principle in a regular scenario.When the retailer's cost distribution is fluctuated by disruptions,we obtain the optimal emergency strategies of the supply chain under asymmetric information by considering deviation costs and show how to effectively handle the cost uncertainty.Using numerical methods,impacts of cost disruptions on the optimal wholesale price,the retailer price,the order quantity and the expected profits of the retailer,the supplier,as well as the total system are analyzed.It is found that the all-unit quantity discount policy can obtain better performance than the wholesale pricing policy.

SUPPLY CHAIN DISRUPTION MANAGEMENT WITH MULTIPLE COMPETING RETAILERS

As for the supply chain consisting of a manufacturer and multiple competing retailers, the disruption management decision is considered for the demand disruption that both the investment sensitivity coefficient and the investment-elasticity are changed simultaneously. Meanwhile, the corresponding solutions are given for different cases of the disruption, and the characteristics of the solutions are analyzed. The production plan is proved to he robustness under certain conditions, and the production plan of the coordination strategy is investigated for the disruption with appropriate contractual arrangement, i. e. , a subside rate schedule. Mutual restraints exist between the changed investment sensitivity coefficient and the investment-elasticity when the coordination mecha- nism is used. And the more the number of the retailers in the system, the more the subside provided by the suppler on the coordinaton strategy.

DISRUPTION MANAGEMENT FOR SUPPLY CHAIN COORDINATION WITH EXPONENTIAL DEMAND FUNCTION

The coordination problem of a supply chain comprising one supplier and one retailer under market demand disruption is studied in this article. A novel exponential demand function is adopted, and the penalty cost is introduced explicitly to capture the deviation production cost caused by the market demand disruption. The optimal strategies are obtained for different disruption scale under the centralized mode. For the decentralized mode, it is proved that the supply chain can be fully coordinated by adjusting the price discount policy appropriately when disruption occurs. Furthermore, the authors point out that similar results can be established for more general demand functions that represent different market circumstances if certain assumptions are satisfied.

Supply Chain Coordination with Demand Disruptions under Convex Production Cost Function

This paper investigates the problem of how to handling demand disruptions ina one-supplier-one-retailer supply chain, where production cost is a convex function of productionquantity andprice-demand relationship is linear. Our results show that, ifdemand is disrupted, underthe new price-demand relationship, all-unit wholesale quantity discount policies combiningcapacitated linear pricingpolicies can also fully coordinate the supply chain.

Endocrine Disruption Activity of 30-day Dietary Exposure to Decabromodiphenyl Ethane in Balb/C Mouse

Objective This study aimed to evaluate the hepatotoxicity, metabolic disturbance activity and endocrine disrupting activity of mice treated by Decabromodiphenyl ethane （DBDPE）. Methods In this study, Balb/C mice were treated orally by gavage with various doses of DBDPE. After 30 days of treatment, mice were sacrificed; blood, livers and thyroid glands were obtained, and hepatic microsomes were isolated. Biochemical parameters including 8 clinical chemistry parameters, blood glucose and hormone levels including insulin and thyroid hormone were assayed. The effects of DBDPE on hepatic cytochrome P450 （CYP） levels and activities and uridinediphosphate-glucuronosyltransferase （UDPGT） activities were investigated. Liver and thyroid glands were observed. Results There were no obvious signs of toxicity and no significant treatment effect on body weight, or liver-to-body weight ratios between treatment groups. The levels of ALT and AST of higher dose treatment groups were markedly increased. Blood glucose levels of treatment groups were higher than those of control group. There was also an induction in TSH, T3, and f T3. UDPGT, PROD, and EROD activities were found to have been increased significantly in the high dose group. Histopathologic liver changes were characterized by hepatocyte hypertrophy and cytoplasmic vacuolization. Our findings suggest that DBDPE can cause a certain degree of mouse liver damage and insufficiency. Conclusion DBDPE has the activity of endocrine disruptors in Bal/C mice, which may induce drug-metabolizing enzymes including CYPs and UDPGT, and interfere with thyroid hormone levels mediated by Ah R and CAR signaling pathways. Endocrine disrupting activity of DBDPE could also affect the glucose metabolism homeostasis.