Effect Relationship Research Articles

Enhancing Retail Transactions: A Data-Driven Recommendation Using Modified RFM Analysis and Association Rules Mining

Retail transactions have become an integral part of the economic cycle of every country and even on a global scale. Retail transactions are a trade sector that has the potential to be developed continuously in the future. This research focused on building a specified and data-driven recommendation system based on customer-purchasing and product-selling behavior. Modified RFM analysis was used by adding two variables, namely periodicity and customer engagement index; clustering algorithm such as K-means clustering and Ward’s method; and association rules to determine the pattern of the cause–effect relationship on each transaction and four types of classifiers to apply and to validate the recommendation system. The results showed that based on customer behavior, it should be split into two groups: loyal and potential customers. In contrast, for product behavior, it also comprised three groups: bestseller, profitable, and VIP product groups. Based on the result, K-nearest neighbor is the most suitable classifier with a low chance of overfitting and a higher performance index.

Pharmacokinetics, pharmacodynamics and urinary recovery of oral lysergic acid diethylamide administration in healthy participants.

Lysergic acid diethylamide (LSD) is currently investigated for several neurological and psychiatric illnesses. Various studies have investigated the pharmacokinetics and the pharmacokinetic-pharmacodynamic relationship of LSD in healthy participants, but data on urinary recovery and confirmatory studies are missing. The present study characterized the pharmacokinetics, pharmacokinetic-pharmacodynamic relationship and urinary recovery of LSD at doses of 85 and 170 μg administered orally in 28 healthy participants. The plasma concentrations and subjective effects of LSD were continuously evaluated over a period of 24 h. Urine was collected during 3 time intervals (0-8, 8-16 and 16-24 h after LSD administration). Pharmacokinetic parameters were determined using compartmental modelling. Concentration-subjective effect relationships were described using pharmacokinetic-pharmacodynamic modelling. Mean (95% confidence interval) maximal LSD concentrations were 1.8ng/mL (1.6-2.0) and 3.4ng/mL (3.0-3.8) after the administration of 85 and 170 μg LSD, respectively. Maximal concentrations were achieved on average after 1.7h. Elimination half-lives were 3.7h (3.4-4.1) and 4.0h (3.6-4.4), for 85 and 170 μg LSD, respectively. Only 1% of the administered dose was recovered from urine unchanged within the first 24 h, 16% was eliminated as 2-oxo-3-hydroxy-LSD. Urinary recovery was dose proportional. Mean (±standard deviation) durations of subjective effects were 9.3±3.2 and 11 ±3.7h, and maximal effects (any drug effects) were 77 ± 18% and 87 ± 13% after 85 and 170 μg of LSD, respectively. The present novel study validates previous findings. LSD exhibited dose-proportional pharmacokinetics and first-order elimination kinetics and dose-dependent duration and intensity of subjective effects. LSD is extensively metabolized and shows dose-proportional urinary recovery.

Impact Analysis of Traffic Factors on Urban Bus Bunching

Due to the mixed right-of-way and varying traffic conditions, urban bus operations are often subject to random delays and eventually bus bunching, undermining the schedule reliability. The existing studies have proposed different models to emulate bus bunching and control strategies to mitigate bus bunching, yet few of them considered the effect of the three important traffic factors: intersections with signal coordination, varying traffic volume, and passenger demand. To fill in the research gap, we first define the stop-level frequency of bus bunching events for a bus route in this study. We proceed to present a simulation-based approach to quantify the impact of the three traffic factors on bus bunching. Numerical experiments based on different scenarios are carried out to reveal the cause–effect relationship between these factors and bus bunching events. Contributors to bus bunching are evaluated, and the effect of control delays is examined through statistical measurements. Finally, a real-world case study based on bus route 51 in Singapore is performed, and some insights are provided to alleviate the bus bunching phenomenon.

A rising trend in eco-friendly products: A health-conscious approach to green buying

This study aims to develop and test the hypothesis of the relationship between green perceived value, green perceived risk, green trust, and green purchase intentions especially the moderator effect of gender on relationship of green trust and green intention to clarify their correlation. This study focuses on the interdependence between the four factors mentioned (green perceived value, green perceived risk, green trust, and green purchase intentions) in order to increase green purchase intentions in modern society, which is an important factor to be concerned about in this environmental era. The technique of research applied in this study is nonprobability sampling with snowball sampling method. Structural Equation Modeling (PLS-SEM) was used to test the cause and effect relationships in the research. A survey conducted by 214 respondents who concerned in buying eco-friendly product were asked to fill the questionnaire to analyze the hypothesis. The analyzed results show that the moderator effect of gender on relationship of green trust and green intention, green perceived value has a positive effect on both green trust and green purchase intentions. Then, green perceived risk positively affects green trust but has no impact on green purchase intentions. In comparison, green trust is an important factor that leads to green purchase intentions.

Low Employability of Graduate Engineers – Ascertaining Role of Industry by leveraging Industry Total Interpretive Structural Modelling.

Abstract—The objective of this study is to conceptualize a Total Interpretive Structural Model (TISM) of factors leading to low employability of graduate engineers, at entry level, in context to Indian IT Industry. The paper focuses on factors emerging due to lack or low participation of IT Industry in overall academic activities contributing to low employability. Authors have developed Total Interpretive Structural Model (TISM) to understand the interplay of industry induced factors, leading to low employability of graduate engineers. Factors considered are limited to the role of IT industry in academia. Qualitative Semi Structured interviews & survey inputs gathered from academic experts including Senior Faculties, Heads of the Departments, Principals and Training and Placement officers from 10 engineering colleges in Maharashtra (India), are considered to arrive at factors highlighting the wanting contribution of IT industry. Total interpretive structural model has been developed for the factors leading to low employability. This model provides a hierarchical structure to the identified factors at four different levels based on their driving power and dependence. The model also portrays factor’s interplay with the help of direct and transitive linkages. Based on the research authors recommend key initiatives at fundamental factors which can have a cascading impact on other dependent factors. This research has implications for IT Industry, academia and academic governing authorities. The study provides an indicative list of strategic factors and the synergistic effect created by their interplay. The relationships amongst these factors are being modeled providing an insight, in terms of cause and effect relationship, prioritizing the areas to be addressed on priority. The proposed model, examining the factors of low employability due to IT industry’s wanting role in academic context is a fresh approach & pioneering one. To the best of authors’ knowledge, this is the first study that analyzes the factors of low employability of graduate engineers using TISM approach, focusing on IT industry and not on academia, as being done historically. Keywords—Total Interpretive Structure Modeling Employability; Factors; IT Industry

Simulation calculation of electric field distribution in the rat brain irradiated by electromagnetic pulses

Based on the spectrum distribution of electromagnetic pulses (EMPs), we constructed a dispersive electromagnetic model of rats with a fine division of tissues and organs and calculated the internal electric field distribution in the rat brain exposed to EMPs. The results showed that EMP radiation was capable of generating electric field distribution with good uniformity in the rat brain and that the peak field intensity in the rat brain was about 1/250 of that in the spatial domain. The electric field in the cerebral cortex was slightly higher than that in the deep hippocampus, striatum, and thalamus, and it was probably the lowest in the hypothalamus. In the cortex, the electric field was low in the prefrontal cortex, while it was relatively high in the parietal and temporal lobes near the middle and at the back of the brain. The data provide a theoretical quantitative basis for establishment of the dose–effect relationship between the electric field in the rat brain and biological effects of the pulse. In addition, it serves as a reference for simulation calculation of the effect of EMPs in living organisms.

The Biomechanical Links Between Pelvic Floor Dysfunction and Testicular Pain: A Clinical Review

Chronic scrotal content pain affects 100,000 men in the United States annually. Up to 50% of these cases do not resolve by following conventional treatment algorithms and are deemed to be idiopathic. There is little peer-reviewed literature supporting the specific cause and effect relationship between pelvic floor dysfunction and chronic scrotal content pain. Additionally, the specificity of the physical exam in these types of patients is not present in the literature. Overall, the literature is deficient in proposed treatment algorithms that address the large number of cases that are deemed to be idiopathic. Patients presenting with chronic scrotal content pain may benefit from an osteopathic diagnostic and treatment approach. In these types of patients, we recommend osteopathic manipulative therapy (OMT) or pelvic floor manual therapy prior to surgical intervention. This conservative approach may reduce the large portion of cases that aredeemed to be idiopathic. The emphasis on structure and function within osteopathic medical education places osteopathic family physicians in a unique position to be able to properly diagnose and treat this type of pain. Since most cases of chronic scrotal content pain are initially addressed in the primary care setting, it is important for osteopathic primary care physicians to remain vigilant in considering musculoskeletal dysfunction when evaluating these types of patients. This clinical review is underscored by a unique case presentation of a male collegiate athlete who helps demonstrate the larger gap that is present in the literature on male pelvic floor and scrotal content pain.

Spectrum–effect relationship combined with bioactivity evaluation to discover the main anxiolytic active components of Baihe Dihuang decoction

Ethnopharmacological relevanceAnxiety disorders leads to a decline in quality of life and increased risk of morbidity and mortality. The Baihe Dihuang decoction (BDD) is a classic Chinese medical formula that has been widely used to treat anxiety disorders for thousands of years in China. However, the pharmacodynamic material that is responsible for the antianxiety of BDD remains unclear. Aim of the studyTo screen the main ingredients of anti-anxiety in BDD based on the establishment of spectrum-effect relationship and verified experiment. MethodsThe UPLC-Q-TOF/MS technique was utilized to establish fingerprints of various fractions of BDD and identify the main compounds. The anti-anxiety effects of BDD were comprehensively evaluated through multiple assessments, including the open field test, elevated plus maze test, and neurotransmitters tests. Then, the spectrum-effect relationship was established through Pearson correlation analysis, gray correlation analysis, orthogonal partial least squares regression analysis. The spectrum-effect relationship results were confirmed through various measures on an anxiety condition cell model, induced by a corticosterone and lipopolysaccharide intervention. These measures included assessing neuronal cell viability, morphology, apoptosis, synaptic damage, and the expression of neurotransmitters and inflammatory factors. ResultsIn the UPLC-Q-TOF-MS fingerprint, 46 common peaks were identified. The pharmacological results indicated that different fractions of BDD have strong effects on improving anxiety-like behavior and regulating neurotransmitters. Among them, butanol fraction has the highest comprehensive evaluation score of anti-anxiety efficacy, which is main active fraction of BDD for anti-anxiety. The analysis of the spectrum-effect relationship revealed that the 46 peaks exhibited varying degrees of correlation with the anti-anxiety efficacy indicators of BDD. Among them, 14 components have a high correlation with the anti-anxiety efficacy indicators, which may be the potential anti-anxiety efficacy components of BDD. The in vitro activity verification of active components verified our prediction, regaloside A, B, C, D, H, acteoside, and isoacteoside improved neuronal cell viability, cell morphology, apoptosis, and synaptic damage. Additionally, regaloside A, B, C, D, H and acteoside regulated the neurotransmitter levels, while regaloside A, B, C, D, acteoside and isoacteoside inhibited the levels of inflammatory cytokines. ConclusionThe butanol fraction was found to be the main active fraction of BDD, and 14 compounds were the major anxiolytic active components. The results of verifying the major active components were consistent with the predicted results of the spectrum-effect analysis. The developed spectrum-effect analysis in this study demonstrates high accuracy and reliability for screening active components in TCMs.

Decoding the enigma of 100-year record-breaking rainfall over Tamil Nadu using wavelet analysis

There have been increasing trends of short-intensive rainstorm events, which lead to urban flood disasters in many coastal areas of India. The relationship between regional rainfall and global large-scale climate indices has shown high teleconnections in terms of recurrence and covariance. We have examined the teleconnection between the November and December 2015 rainstorm events (100-year record breaking rainfall) with the Oceanic Nino Index using the dataset from the Indian Meteorological Organisation (NEMR) and National Oceanic and Atmospheric Administration (ONI). The study has found out not only the existence of the cause-and-effect relationship but also the recurrences of these high rainfall events. The climatological trend of northeast monsoon rainfall (NEMR) and Oceanic Nino Index (ONI) is calculated, and the correlation for a period of 71 years (1951–2021) is quantified using Pearson's correlation method. We observed that Chennai, Nagapattinam, Cuddalore, Kancheepuram, Thiruvarur, and adjoining coastal districts are more prone to torrential rains during El Niño years as compared to La-Niña years which is unusual but not exceptional. Wavelet analysis is performed on the NEMR and ONI signals to decipher the teleconnections at different temporal scales. Wavelet coherence shows an in-phase relationship between the ONI and NEMR signal and high covariance (0.8–0.9 at 5% significance level using Monte Carlo generated noise), between the two in 2015 the year which broke the 100 years rainfall record for Chennai. The study highlights the crucial linkage (cause and effect relationship) between the regional rainstorms and the large-scale global index (ONI), and wavelet analysis can explain the distant relationship in the northeast monsoon. The study provides quantified insights especially in the present era of climate change, where the hydrological extremes are persistent. The study does not only connect ONI with NEMR but also quantifies the in-phase strength of the relationship which can guide the scientific community towards accurate predictions and develop disaster mitigation strategies.

Artificial Intelligence in Toxicology and Pharmacology

Methods that utilize machine learning and artificial intelligence have transformed a wide variety of fields, including the field of toxicology. Physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases are just some of the areas that are covered in this review. By leveraging machine learning and artificial intelligence approaches, it is now possible to develop PBPK models for hundreds of chemicals in an efficient manner, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with In vivo animal experiments, and to analyze a large amount of data of various types (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was previously impossible. This is an improvement over the previous situation The field of toxicological sciences faces a number of challenges that must be overcome before it can make further progress. These challenges include the following: (1) not all machine learning models are equally useful for a particular type of toxicology data; therefore, it is important to test different methods to determine the optimal approach; (2) the current toxicity prediction is primarily based on bioactivity classification (yes/no); therefore, additional studies are required to predict the intensity of effect or dose-response relationship.

Causal maps in the analysis and unsupervised assessment of the development of expert knowledge: Quantification of the learning effects for knowledge management purposes

This study proposes an application of cognitive maps in the representation of cognitive structures of the experts and assessment of their development/modification as a result of a (computer or expert system-assisted) learning process. It strives to identify information needed for the guidance of the process of creation and management of expert knowledge by formal modeling tools. Changes in experts’ cognitive structures are assumed to stem from individual and collaborative (group-level) learning. The novel approach to assessing the outcomes of learning reflected as changes in the cognitive structures of experts or groups of experts, modeled by cognitive maps, does not assume any correct or desired outcome of the learning process to be known in advance. Instead, it identifies and analyzes the changes in (or robustness of) the constituents of the cognitive maps from different points of view and allows for quantifying and visualizing the actual effect of the learning. The proposed methodology can identify changes in cognitive diversity, causal structures in terms of causal relations and concepts, and the perceived importance of strategic issues over the learning period. It can also detect which cause–effect relationships have appeared/disappeared considering the pre-/post-mapping design. Thus, it provides an exploratory account on the changes in the cognitive structures of the expert(s) as a result of learning. The applicability of the proposed methods is illustrated in the assessment of the learning outcomes of a group of 71 graduate students who participated in an eight-week business simulation task. The results of the empirical analysis confirm the viability of the proposed methodology and indicate that the students’ understanding of the utilized concepts and associated relationships in the decision-making process improved throughout the learning activity, ultimately showing that the course learning has considerably improved students’ perception and knowledge. Based on the results, it can be concluded that the proposed approach has the potential to be effective in assessing learning outcomes in teaching–learning activities.

Analyzing the Impact of Social Media on Students using SPSS: Insights and Implications

Students may lose concentration and productivity in their academics by spending hours scrolling through their feeds, participating in online debates, or viewing videos’ students' mental health may be greatly impacted by social media sites. Continual exposure to curated and idealized versions of others' lives can contribute to feelings of inadequacy, low self-esteem, and anxiety. The effect of social media on pupils has recently generated a lot of interest and discussion. Examining how these online spaces affect students' lives is crucial as platforms like Facebook, Instagram, Twitter, Snapchat, and Tikor become more popular and influential. This essay will go over five major issues about how social media affects students: Academic Results: Excessive social media use frequently causes distractions and deterioration in academic results. Studying how social media affects students has broad academic implications and is crucial in the current digital era. Understanding and assessing this influence can help different stakeholders, such as educators, parents, lawmakers, and mental health specialists, gain insightful information. Here are some significant factors emphasizing the importance of the study: Academic Policies and Interventions: The establishment of successful policies and interventions can be influenced by research on how social media affects students' academic performance. Teachers can use measures to improve focus, time management, and digital literacy among pupils by pinpointing the causes of distraction and decreased productivity. Mental Health Support: It is vital for mental health experts to look into the relationship between social media use and results in terms of mental health. A thorough and organized technique is required while researching the effects of social media on pupils in order to collect pertinent information and produce insightful findings. An example of a potential research approach is given below: Select the most appropriate research design for the study, such as mixed techniques, qualitative, or quantitative. To choose the best strategy, take into account the research questions, objectives, and resources at hand. Conduct a detailed analysis of the body of knowledge regarding how social media affects kids. Create a solid theoretical framework for the study by identifying important theories, concepts, and findings from prior research. Taken as input parameters is Age, Gender, program, what type of family background, where do you from? how much does using social media cut your time of regular sleep? Does using social media effect on your studies?, how many hours a day do you spend on social media/, on a scale how social media effect relationship?, on scale how social media have positive impact/negative impact on students?, how social media effect communication and connectivity?.

Public Debt and the Nigerian Economy: 1987 - 2020

This study investigated the impact of public debt on the Nigerian economy from 1987 to 2020. The specific objectives were to determine the impact of external debt on economic growth and to examine the impact of domestic debt on economic growth in Nigeria. The ex-post facto research design was adopted to enable the researcher make use of secondary data to determine the cause- effect relationship of public debt on economic growth in Nigeria. The dependent and independent variables were observed over the period, 1987 to 2020. Empirical investigation was carried out on the basis of time series data collected from the Central Bank of Nigeria Statistical Bulletin.The unit root test performed on each of the variables in the model using the Augmented Dickey-Fuller (ADF) unit root tests showed that the variables were stationarityat level and data analyzed with Ordinary Least Square. (OLS). Findings from this study revealed that external debt impacted negatively on economic growth of Nigeria while domestic debt impacted positively on economic growth of Nigeria. Following these findings, this study recommended that policy makers should discourage external borrowing by all means and encourage domestic borrowing, while integrating appropriate measures to ensure efficient management of domestic debts.

ThTP5.2 Outcome of polypharmacy in frail patients post emergency laparotomy

Abstract Aims The causal effect relationship between frailty and polypharmacy is not clearly established. Medications are used to treat co- morbidities that cause frailty but the side effects of the drugs could also be the stressors leading to frailty. Moreover, high risk of mortality is associated with frail emergency laparotomy patients. We aim to explore the association between 30- day mortality and frailty with polypharmacy. Methods A prospective database of emergency laparotomy (EmLap) patients in a busy district hospital was analysed. Patients ≥40 years old who underwent EmLap as per NELA criteria were included. Frailty was assessed via Clinical Frailty Score with score ≥4 as frail. Polypharmacy was defined as ≥5 daily medications. Statistical analysis was performed using logistic regression and p value of <0.05 was considered significant. Results 100 patients were included with 41% male and median age of 66 years. The overall 30- day mortality rate was 9%. Frailty was present in 49% and mortality rate was 16.3% (OR 79.76, p= 0.0121). Polypharmacy was found in 36% and mortality rate was 16.7% (OR 4.07, p= 0.045). There was significant association between frailty and polypharmacy (p=0.012). When both frailty and polypharmacy were present (25%), the mortality rate was 20% (OR 5.55, p= 0.026). Conclusion Frailty increases risk of polypharmacy and polypharmacy poses risk of worsening frailty, Mortality rate post EmLap is higher when EmLap patients are both frail and have polypharmacy. It is important to rationalise and review patient’s drug therapy in the community as well as in hospital.

Are Whiplash-Associated Disorders and Temporomandibular Disorders in a Trauma Related Cause and Effect Relationship? A Review.

Background: Whiplash is associated with a wide variety of clinical manifestations, including headache, neck pain, cervical rigidity, shoulder and back pain, paresthesia, vertigo, and temporomandibular disorders (TMDs). Previous studies reported that TMDs are more common in individuals with chronic whiplash-associated disorders (WAD) than in the general population; however, the pathophysiology and mechanism of this relationship are still not well understood. Methods: A PubMed and Ovid EMBASE review was performed to identify all studies addressing the trauma related cause and effect relationship between WAD and TMDs from January 2003 to March 2023. Results: After screening for eligibility and inclusion criteria, a total of 16 articles met the selection criteria. The various included studies discussed different aspects of the association between WDA and TMDs, including changes in the coordination and amplitude of jaw opening, the severity of the associated symptoms/signs in cases of WAD, the degree of fatigue and psychological stress, difficulty in feeding, cervical and myofascial pain, changes in the MRI signal at various muscle points, muscle tenderness, and quality of life. Conclusions: In this review, we summarized the clinical evidence of any trauma related cause and effect relationship between whiplash and TMDs. An accurate screening of the previous literature showed that, in conclusion, the relationship between whiplash and TMDs is still unclear.

Microwave-coordinated KOH directionally modulated N/O co-doped porous biochar from Enteromorpha and its structure–effect relationships in efficient CO2 capture

To develop the CO2 adsorption capacity of Enteromorpha-based biochar, N/O co-doped porous biochar (NOBC) was directionally modulated by microwave-coordinated KOH, and the CO2 capture mechanism was explored by structure-effect relationship and density functional theory (DFT) calculations. NOBC with different porous structures and heteroatom contents were prepared by optimization of key parameters (microwave heating temperature, heating time, and KOH ratio) via Response Surface Methodology. The results showed that NOBC(700–20-3) had the maximum CO2 adsorption capacity of 6.09 mol/kg (273 K and 1 bar) and the CO2/N2 selectivity of 78.88 (298 K and 1 bar). After 5 cycles, the CO2 adsorption capacity of NOBC(700–20-3) was still up to 5.98 mol/kg (273 K and 1 bar), showing good cycling performance. The structure-effect relationship analysis showed that the CO2 capture performance of NOBC was proportional to the specific surface area, micropore ratios, graphitization degree, and heteroatoms contents, and inversely proportional to the mean pore diameter. DFT indicated that the synergistic effect between N and O elements developed the CO2 adsorption ability by boosting the affinity and electron transfer between NOBC and CO2. This work contributed to providing a theoretical basis and technical support for the targeted modulation of porous structures and improved surface properties of biochar for efficient CO2 adsorption.

The Causal Relationship of Anti-Cancer Effect with Physical Activity Evinced by the Consistent Anti-Cancer Effect of the Ou MC Decrescendo Phenomenon.

A causal relationship between physical activity and cancer prevention has not been firmly established in the medical literature. The Ou MC decrescendo phenomenon treatment (OuDPt) is a self-administered protocol in which placing the contralateral hand on or near the affected area of the body produces a zone of decreased pain or inflammation. OuDPt has also been shown to elicit an anti-cancer effect that consistently induces tumor regression in several cancer types, including uterine, ovarian, and pancreatic cancer, with documented apoptosis and squamous metaplasia in uterine endometrial cancer. The anti-cancer effects of OuDPt are associated with factors such as the frequency, duration, and intensity of treatment, as well as the accessibility and susceptibility of the tumor. This relationship mirrors the dynamics between antibiotics and bacterial infections, where similar factors come into play. Given that OuDPt is self-administered and easy to perform, and produces consistent anti-cancer effects, this procedure could be potentially harnessed for cancer prevention. Further study of the use of OuDPt for cancer prevention is warranted.

Comparative proteomics of proliferative diabetic retinopathy in people with Type 2 diabetes highlights the role of inflammation, visual transduction, and extracellular matrix pathways.

To explore the vitreous humor proteome from type 2 diabetes subjects with proliferative diabetic retinopathy (PDR) in the Indian population. We performed mass spectrometry-based label-free quantitative analysis of vitreous proteome of PDR (n = 13) and idiopathic macular hole (IMH; control) subjects (n = 14). Nine samples of PDR and 10 samples of IMH were pooled as case and control, respectively, and compared. Four samples each of PDR and IMH were analyzed individually without pooling to validate the results of the pooled analysis. Comparative quantification was performed using Scaffold software which calculated the fold changes of differential expression. Bioinformatics analysis was performed using DAVID and STRING software. We identified 469 proteins in PDR and 517 proteins in IMH vitreous, with an overlap of 172 proteins. Also, 297 unique proteins were identified in PDR and 345 in IMH. In PDR vitreous, 37 proteins were upregulated (P < 0.05) and 19 proteins were downregulated compared to IMH. Protein distribution analysis clearly demonstrated a separation of protein expression in PDR and IMH. Significantly upregulated proteins included fibrinogen gamma chain, fibrinogen beta chain, and carbonic anhydrase 1 and downregulated proteins included alpha-1-antitrypsin, retinol-binding protein 3, neuroserpin, cystatin C, carboxypeptidase E and cathepsin-D. Diabetic retinopathy pathogenesis involves proteins which belong to inflammation, visual transduction, and extracellular matrix pathways. Validation-based experiments using enzyme-linked immunosorbent assay (ELISA) or western blotting are needed to establish cause and effect relationships of these proteins to the disease state, to develop them as biomarkers or drug molecules.

Synthesis of novel flower-like Bi4Ti3O12/BiOCl heterojunctions with excellent piezoelectric photocatalytic performance

Photocatalysis technology can not only degrade pollutants by renewable solar energy, but also has enormous promise for resolving environmental issues. The piezoelectric field inside the piezoelectric material can be used as the driving force of charge separation to improve the photocatalytic activity. In order to study the catalytic effect of piezoelectric materials on photocatalysis, a flower-like Bi4Ti3O12/BiOCl (BTO/BOC) composite catalyst is prepared in this paper. The photogenerated carrier separation efficiency is characterized by electrochemical, PL and ESR tests. BOC's built-in electric field helps to facilitate the separation of photogenerated carrier, reducing the recombination rate and improving photocatalytic efficiency. The piezoelectric effect caused by ultrasonic vibration is coupled with the photocatalysis, which realizes the high efficiency co-catalysis of BTO/BOC piezoelectric photocatalyst. The polarized BTO/BOC heterojunction catalysts achieved a very high degradation efficiency (99.15 %) under piezo-photocatalysis. The degradation rates (k = 0.7494 min−1) are 1.74 and 8.19 times higher than photocatalysis (k = 0.4304 min−1) and piezoelectric catalysis (k = 0.0915 min−1), respectively. The investigation of the piezoelectric effect and photocatalysis's synergistic relationship is very significant.

Fault propagation analysis of computer numerically controlled machine tools

Computer numerically controlled machine tools are the key equipment of the intelligent manufacturing industry, unscheduled shutdowns can substantially cause equipment damage and production loss. Fault propagation analysis, as a prominent basis of reducing unscheduled downtime, can locate the harmful parts and reveal the fault propagation path. To improve reliability, this paper proposes a novel fault propagation analysis method that can accurately and timely evaluate fault propagation coefficients of components in interior computer numerically controlled machine tools. To depict the latent cause–effect relationships of components, a quantitative causal diagram is built with the aid of design information and topology. An inverse PageRank algorithm is proposed to assess the fault propagation risks of components. Unlike the traditional PageRank algorithm, the idea of inverse PageRank is that a node is crucial if it directs to other important nodes. Subsequently, the fault propagation coefficients of components in the quantitative causal diagram are derived and the fault propagation path is identified. The proposed method is validated on the computer numerically controlled machine tool, and the results are basically consistent with the typical cascading events and conform to expert judgment.