On-spot preparation of EDTA solution for the treatment of band keratopathy:a case report

Dear Editor,We report a case of band keratopathy who experienced several ocular surgeries,secondary uveitis and glaucoma which eventually led to severe band keratopathy.We use commonly-used vacuum blood collection tubes with ethylenediaminetetraacetic acid dipotassium salt(EDTA-K2)to prepare chelation solution on-spot to remove the calcium deposits on the corneal surface.It is proved to be relatively safety,efficacy,cost-effective.

Common Artifacts and Remedies in Histological Preparations

The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Progress in preparation of AlN-reinforced magnesium matrix composites:A review

As a ceramic material,AlN has very good thermophysical and mechanical properties.In addition,AlN is an effective refining agent for Mg alloys because it has a lattice constant similar to that of Mg.Therefore,AlN is an ideal reinforcement for magnesium matrix composites(MMCs),and is attracting increasing attention.This review addresses the development of preparation technologies for AlN-reinforced Mg matrix composites.The mainstream preparation techniques include stir casting,melt infiltration,powder metallurgy,and in-situ methods.In addition,the advantages and disadvantages of these techniques are analyzed in depth,and it is pointed out that the next direction for the preparation of high-performance AlN-reinforced MMCs is less aluminization and multiple technologies integration.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Insight into demand-driven preparation of single-atomic mediators for lithium–sulfur batteries

Lithium-sulfur(Li-S) batteries have attracted considerable attention as one of the most appealing energy storage systems.Strenuous efforts have been devoted to tackling the tremendous challenges,mainly pertaining to the severe shuttle effect,sluggish redox kinetics and lithium dendritic growth.Single-atomic mediators as promising candidates exhibit impressive performance in addressing these intractable issues.Related research often utilizes a trial-and-error approach,proposing solutions to fabricate single-atomic materials with diversified features.However,comprehensive review articles especially targeting demand-driven preparation are still in a nascent stage.Inspired by these considerations,this review summarizes the design of single-atomic mediators based on the application case-studies in LiS batteries and other metal-sulfur systems.Emerging preparation routes represented by chemical vapor deposition technology are introduced in a demand-oriented classification.Finally,future research directions are proposed to foster the advancement of single-atomic mediators in Li-S realm.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Effects of site preparation methods on soil physical properties and outplanting success of coniferous seedlings in boreal forests

This study assessed the effect of patch scarification and mounding on the physical properties of the root layer and the success of tree planting in various types of forests.This study was conducted on 12 forest sites in taiga forests of the European part of Russia.A total of 54 plots were set up to assess seedling survival;root collar diameter,height,and heigh increment were measured for 240 seedlings to assess growth.In the rooting layer,240 soil samples were taken to determine physical properties.The study showed that soil treatment methods had no effect on bulk density and total porosity in Cladina sites.However,reduced soil moisture was noted,particularly in mounds,resulting in increased aeration.In Myrtillus sites,there were increased bulk density,reduced soil moisture,and total porosity in the mounds.Mounding treatment in Polytrichum sites resulted in reduced soil moisture and increased aeration porosity.In the Myrtillus and Polytrichum sites,patch scarification had no effects on physical properties.In Polytrichum sites,survival rates,heights,and heigh increments of bareroot Norway spruce seedlings in mounds were higher than in patches;however,the same did not apply to diameter.In Cladina and Myrtillus sites,there was no difference in growth for bareroot and containerised seedlings with different soil treatments.Growing conditions and soil types should be considered when applying different soil treatment methods to ensure high survival rates and successful seedling growth.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

Latest advances and progress in the microbubble flotation of fine minerals:Microbubble preparation,equipment,and applications

In the past few decades,microbubble flotation has been widely studied in the separation and beneficiation of fine minerals.Compared with conventional flotation,microbubble flotation has obvious advantages,such as high grade and recovery and low consumption of flotation reagents.This work systematically reviews the latest advances and research progress in the flotation of fine mineral particles by microbubbles.In general,microbubbles have small bubble size,large specific surface area,high surface energy,and good selectivity and can also easily be attached to the surface of hydrophobic particles or large bubbles,greatly reducing the detaching probability of particles from bubbles.Microbubbles can be prepared by pressurized aeration and dissolved air,electrolysis,ultrasonic cavitation,photocatalysis,solvent exchange,temperature difference method(TDM),and Venturi tube and membrane method.Correspondingly,equipment for fine-particle flotation is categorized as microbubble release flotation machine,centrifugal flotation column,packed flotation column,and magnetic flotation machine.In practice,microbubble flotation has been widely studied in the beneficiation of ultrafine coals,metallic minerals,and nonmetallic minerals and exhibited superiority over conventional flotation machines.Mechanisms underpinning the promotion of fine-particle flotation by nanobubbles include the agglomeration of fine particles,high stability of nanobubbles in aqueous solutions,and enhancement of particle hydrophobicity and flotation dynamics.

A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

This paper presents an operational framework of unstructured decision-making approach involving quality function deployment(QFD)in an uncertain linguistic context.Firstly,QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment.Secondly,hesitant fuzzy linguistic term sets(HFLTSs),which facilitate the management and handling of information equivocality,are designed to construct a house of quality(HoQ)in the product planning process.The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets.Thirdly,a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization.The inter-relationships of cooperative partners are directly matched with a back propagation neural network(BPNN)to construct the multi-enterprise manufacturing network.The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives.Finally,a real-world example,namely,the prototype manufacturing of an automatic transmission for a vehicle,is provided to illustrate the effectiveness of the proposed decision-making approach.

Factors affecting the quality of bowel preparation for colonoscopy in hard-to-prepare patients:Evidence from the literature

Adequate bowel cleansing is critical for a high-quality colonoscopy because it affects diagnostic accuracy and adenoma detection.Nevertheless,almost a quarter of procedures are still carried out with suboptimal preparation,resulting in longer procedure times,higher risk of complications,and higher likelihood of missing lesions.Current guidelines recommend high-volume or low-volume polyethylene glycol(PEG)/non-PEG-based split-dose regimens.In patients who have had insufficient bowel cleansing,the colonoscopy should be repeated the same day or the next day with additional bowel cleansing as a salvage option.A strategy that includes a prolonged low-fiber diet,a split preparation regimen,and a colonoscopy within 5 h of the end of preparation may increase cleansing success rates in the elderly.Furthermore,even though no specific product is specifically recommended in the other cases for difficult-to-prepare patients,clinical evidence suggests that 1-L PEG plus ascorbic acid preparation are associated with higher cleansing success in hospitalized and inflammatory bowel disease patients.Patients with severe renal insufficiency(creatinine clearance<30 mL/min)should be prepared with isotonic high volume PEG solutions.Few data on cirrhotic patients are currently available,and no trials have been conducted in this population.An accurate characterization of procedural and patient variables may lead to a more personalized approach to bowel preparation,especially in patients undergoing resection of left colon lesions,where intestinal preparation has a poor outcome.The purpose of this review was to summarize the evidence on the risk factors influencing the quality of bowel cleansing in difficult-to-prepare patients,as well as strategies to improve colonoscopy preparation in these patients.

Hyponatremic encephalopathy due to polyethylene glycol-based bowel preparation for colonoscopy:A case report

BACKGROUND Adequate bowel preparation is critical for colonoscopy screening.At present,the most widely used intestinal cleaner recommended at home and abroad is Polyethylene glycol(PEG).Intestinal cleansers can cause electrolyte disturbances and hyponatremia.However,hyponatremic encephalopathy due to hyponatremia induced by PEG solution,although rare,can lead to serious irreversible sequelae and even death.CASE SUMMARY In this report,we discuss a case of neurological dysfunction due to hyponatremia,also known as hyponatremic encephalopathy,observed in a 63-year-old woman who underwent PEG-based bowel preparation for colonoscopy.She was eventually transferred to our intensive care unit for treatment due to her Glasgow Coma Scale score of 9/15(Eye opening 2;Verbal response 1;Motor response 6)and abnormal laboratory tests.CONCLUSION Physicians should be thoroughly familiarized with the patient’s history before prescribing PEG for bowel preparation,and timely identification of patients with hyponatremic encephalopathy is essential as delayed treatment is associated with poor neurological outcomes.An intravenous infusion of 3%sodium chloride is recommended at the onset of early symptoms.The goal of treatment is to adequately treat cerebral edema while avoiding serum sodium correction beyond 15 to 20 m Eq/L within 48 h of treatment to prevent osmotic demyelination syndrome.