Global Performance Research Articles

A novel enhanced flow regime algorithm using opposition-based learning

Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, an ontology alignment application study demonstrate that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems.

Strategi Penguatan Profil Pelajar Pancasila Dimensi Berkebhinekaan Global Di Sekolah Dasar

The Pancasila Student Profile has six dimensions: faith and devotion to God, diversity in the world, independence, mutual cooperation, critical thinking, and creativity. Global diversity is an appreciation of diversity that comes from the awareness that Indonesia is a big nation with various ethnicities, traditions and cultures that accompany it. The purpose of this study is to explain how SDIT Nur Hidayah Surakarta has strengthened the global diversity aspect of the Pancasila Student Profile. The phenomenological research method is used in the qualitative research methodology. The results of research conducted at SDIT Nur Hidayah Surakarta regarding the strategy of strengthening the profile of Pancasila students with the dimensions of global performance diversity that the strategy is implemented by integrating it into learning activities such as intra-curricular activities and then packaging it into project activities to strengthen student profiles such as introducing Pancasila Teachers using habituation exercises to help students develop a stronger appreciation of cultural diversity around the world. The teachers set an example by not picking friends at school, getting along with everyone in the school community regardless of skin color, religion, or other characteristics, setting an example of tolerance, and carrying out the flag ceremony every Monday, and respecting differences of opinion, can be seen as strengthening the profile of Pancasila students with global diversity dimensions at SDIT Nur Hidayah Surakarta
 Keywords: Elementary school ; Global Diversity ; Pancasila Student Profile

The Importance of Tissue Doppler Imaging and M-Mode Ultrasonography in Fetuses With Preterm Premature Rupture of Membranes: A Case-Control Study.

To compare the cardiac functions of fetuses with preterm premature rupture of membranes (PPROM) between their control groups and investigate its relationship with perinatal outcomes. This prospective study was conducted with 102 pregnant women. Pregnant women with PPROM were divided into two subgroups Group A, between 26 and 30 weeks, and Group B, between 30 and 34 weeks. A control group was formed by randomly including one healthy pregnant woman for each study patient. Sociodemographic, obstetric data, tissue Doppler imaging, and M-mode imaging results were compared. The relationship between echocardiographic parameters and perinatal outcomes was also investigated. Tricuspid annular plane systolic excursion (TAPSE), S', and ET' of systolic cardiac parameters were shortened in both groups compared with their controls. Diastolic function indicator E'/A', and global function indicator myocardial performance index' increased in both groups. Isovolumetric contraction time' did not change between groups. A correlation was found between myocardial performance index', and the length of neonatal intensive care unit stay in Group A and TAPSE and duration of respiratory support and length of neonatal intensive care unit stay in Group B. The fetal cardiac function seems to be affected by PPROM, and these changes are associated with neonatal outcomes. Therefore, administering fetal cardiac function evaluation in pregnancies complicated by PPROM may help physicians establish more appropriate clinical management protocols in this special population.

A multidomain intervention against cognitive decline in an at-risk-population in Germany: Results from the cluster-randomized AgeWell.de trial.

We investigated the effectiveness of a multidomain intervention to preserve cognitive function in older adults at risk for dementia in Germany in a cluster-randomized trial. Individuals with a Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) risk score ≥ 9 aged 60 to 77 years were recruited. After randomization of their general practitioner (GP), patients received a multidomain intervention (including optimization of nutrition and medication, and physical, social, and cognitive activity) or general health advice and GP treatment as usual over 24 months. Primary outcome was global cognitive performance (composite z score, based on domain-specific neuropsychological tests). Of 1030 participants at baseline, n=819 completed the 24-month follow-up assessment. No differences regarding global cognitive performance (average marginal effect=0.010, 95% confidence interval: -0.113, 0.133) were found between groups at follow-up. Perceived restrictions in intervention conduct by the COVID-19 pandemic did not impact intervention effectiveness. The intervention did not improve global cognitive performance. Overall, no intervention effects on global cognitive performance were detected. The multidomain intervention improved health-related quality of life in the total sample. In women, the multidomain intervention reduced depressive symptoms. The intervention was completed during the COVID-19 pandemic.

Global Research Performance and Development of Social Networks in Sociology: A Bibliometric Analysis

Global Research Performance and Development of Social Networks in Sociology: A Bibliometric Analysis

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.

A Novel FRP Retrofit Solution for Improved Local and Global Seismic Performance of RC Buildings: Development of Fragility Curves and Comparative Cost-Benefit Analyses

ABSTRACT This paper evaluates the effectiveness of a novel capacity-design based retrofit for improving both the local and global behaviour of existing reinforced concrete buildings using fibre-reinforced polymers. The approach was previously tested on beam-column joints and is here extended to the building level. A new fibre-element model for the global retrofit is proposed and validated against full-scale experiments. Non-linear push-over analyses confirm the adequacy of the retrofit and fragility analyses are used to compare it to a simpler local retrofit. Finally, cost-benefit analyses for three levels of seismicity highlight the cost-effectiveness of the global retrofit for moderate to high seismicity.

An anatomy of Nepal’s remarkable export decline: A note

This paper documents and breaks down a remarkable decline in Nepal’s exports over the 2000–2019 period. This decline is due to a collapse in the country’s top export products and is largely uniform across destinations and products. Furthermore, it is not on account of the poor global performance of the sectors Nepal was specialized in but is something more specific to Nepal. Industry-level statistics show that gross output increased in major exporting industries while value-added grew slower or declined. The increase in gross output together with the decline in exports implies a movement towards producing for domestic consumption using more imported inputs. These patterns are broadly consistent with the expected effects of the surge in remittances Nepal experienced over this time period. Remittances would cause the economy to transition towards domestic consumption and re-orient domestic production accordingly.

Study of fast-ion-driven toroidal Alfvén eigenmodes impacting on the global confinement in TCV L-mode plasmas

Following recent observations of unstable Toroidal Alfvén Eigenmodes (TAEs) in a counter-current Neutral Beam Injection (NBI) scenario developed in TCV, an in-depth analysis of the impact of such modes on the global confinement and performance is carried out. The study shows experimental evidence of non-degradation of ion thermal confinement despite the increasing of auxiliary power. During such an improved confinement period, Toroidal Alfvén Eigenmodes (TAEs) driven by fast ions generated through Neutral Beam Injection (NBI) are found unstable. Together with the TAEs, various instabilities associated with the injection of the fast neutrals are observed by multiple diagnostics, and a first characterization is given. Nonlinear wave-wave couplings are also detected through multi-mode analysis, revealing a complex picture of the stability dynamics of the TCV scenario at hand. The measurements provided by a short-pulse reflectometer corroborate the identification and radial localization of the instabilities. A preliminary, but not conclusive, analysis of the impact of TAEs on the amplitude of the electron density fluctuations is carried out. Local flux-tube gyrokinetic simulations show that the dominant underlying instabilities in the absence of fast ions are Trapped Electron Modes (TEM), and that these modes are effectively suppressed by zonal flows. Attempts to simulate the simultaneous presence of fast-ion driven TAEs and TEM turbulence show that elongated streamers develop up to the full radial extent of the flux-tube domain, thereby invalidating the local assumption and indicating that a global approach is mandatory in these TCV plasmas.

The organization of ambulance decontamination during the COVID-19 pandemic: a process analysis based on the Lean Thinking philosophy

The COVID-19 pandemic led to massively increased emergency medical services (EMS) activity. The need to decontaminate emergency vehicles after conveying a suspected or confirmed patient to the hospital represented a critical step, slowing the activities and impacting the number of available ambulances. This brief paper analyzes the flow of EMS processes according to the Lean Thinking management approach, which focuses on reducing waste in a production cycle. The different steps of the whole process (arrival to the Emergency Department, handover phase, decontamination, return to service, and the required transfers) and a series of strategies are discussed. The organization (centralized or delocalized), number, and location of the decontamination centers impact transfers and waiting times and, consequently, the availability of ambulances. Optimizing these processes may lead to a global performance improvement, reducing transfers and time, with greater availability of emergency vehicles.

The Multiple Frequency Conversion Sinusoidal Chaotic Neural Network and Its Application

Aiming at the problem that the global search performance of a transiently chaotic neural network is not ideal, a multiple frequency conversion sinusoidal chaotic neural network (MFCSCNN) model is proposed based on the biological mechanism of the brain, including multiple functional modules and sinusoidal signals of different frequencies. In this model, multiple FCS functions and Sigmoid functions with different phase angles were used to construct the excitation function of neurons in the form of weighted sum. In this paper, the inverted bifurcation diagram, Lyapunov exponential diagram and parameter range of the model are given. The dynamic characteristics of the model are analyzed and applied to function optimization and combinatorial optimization problems. Experimental results show that the multiple frequency conversion sinusoidal chaotic neural network has better global search performance than the transient chaotic neural network and other related models.

Pathophysiologic and prognostic importance of cardiac power output reserve in heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a syndrome characterized by multiple cardiac reserve limitations during exercise. Cardiac power output (CPO) is an index of global cardiac performance and can be estimated non-invasively by echocardiography. We hypothesized that CPO reserve during exercise would be associated with impaired cardiovascular reserve, exercise intolerance, and adverse outcomes in HFpEF. Exercise stress echocardiography was performed in 425 dyspnoeic patients [217 HFpEF and 208 non-heart failure (HF) controls] to estimate CPO at rest and during exercise. We classified patients with HFpEF based on the median value of changes in CPO from rest to peak exercise (ΔCPO >0.49 W/100 g). Patients with HFpEF and a lower CPO reserve had poorer biventricular systolic function, impaired chronotropic response during exercise, and worse aerobic capacity than controls and those with a higher CPO reserve. During a median follow-up of 358 days, a composite outcome of all-cause mortality or HF events occurred in 30 patients. Patients with a lower CPO reserve had four-fold and nearly 10-fold increased risks of the outcomes compared with those with a higher CPO reserve and controls, respectively [hazard ratio (HR) 4.05, 95% confidence interval (CI) 1.16-10.1, P = 0.003 and HR 9.61, 95% CI 3.58-25.8, P < 0.0001]. We further found that a lower CPO reserve had an incremental prognostic value over the H2FPEF score and exercise duration. In contrast, resting CPO did not predict clinical outcomes in patients with HFpEF. A lower CPO reserve was associated with biventricular systolic dysfunction, chronotropic incompetence, exercise intolerance, and adverse outcomes in patients with HFpEF.

Association between vitamin B12 levels and cognitive impairment in older adults.

Whether vitamin B12 deficiency is associated with cognitive impairment remains controversial. To determine the association between vitamin B12 serum levels and cognitive performance. Two-hundred and forty-one adults aged ≥ 60 years who had serum vitamin B12 serum levels measurement were included. Physical and cognitive evaluation was carried out, and three groups were formed: normal cognition (NC), mild cognitive impairment (MCI) and dementia. Vitamin B12 levels were classified as sufficiency (> 400 pg/mL), subclinical deficiency (201-400 pg/mL), and absolute deficiency (≤ 200 pg/mL). Multivariate linear regression analysis was used to evaluate the association between cognitive function and vitamin B12 levels after controlling for confounding variables. Mean age was 81.4 ± 8.0 years; 68% were females; 17.8 % and 39.8% had absolute and subclinical vitamin B12 deficiency, respectively; 80 individuals (33%) met the criteria for MCI, and 70 (29%), for dementia. Those with MCI and dementia had lower vitamin B12 levels in comparison with those with NC after adjusting for age, gender and educational level (p = 0.019). A statistically significant association was observed between global cognitive performance and levels of vitamin B12.

A machine-learning method to accurately recognize the leakage pressure-drop signals in trunk natural gas pipelines

A block valve installed along a trunk natural gas pipeline can automatically shut down when it detects a pressure-drop rate that is over a threshold value for a specified duration, indicating that leakage accidents have occurred. However, current methods have difficulties on identifying the differences among the pressure-drop rate signals caused by small-hole leakage, compressor startup, and block valve emergency shut-down conditions. This usually leads to improperly shutting down the block valve. Based on an optimized support vector machine, a machine-learning method is proposed to recognize the pressure-drop rate signals through the eigenvalue of the pressure-drop curve instead of only using the pressure-drop signal and its duration. Firstly, the singular decomposition method and the support vector machine method are applied to initially classify the pressure-drop rate signals of three operating conditions: the gas leakage, compressor startup, and block valve emergency shut-down conditions. Secondly, a teaching-learning-based optimization method is applied to optimize the penalty factor and kernel function parameter in the support vector machine model. Particularly, the tent chaotic maps and adaptive inertia weight methods are applied to improve the teaching-learning-based optimization method to balance its local and global search performance. Thirdly, an improved teaching-learning-based support vector machine method is established. Finally, 960 sets of simulated pressure-drop signals taken from three trunk natural gas pipelines and pressure-drop rate signals collected from an actual pipeline are applied to verify the accuracy of the proposed model. The results show that the improved teaching-learning-based support vector machine model achieved high classification and recognition accuracy. Specifically, it achieved accuracy rates of 99.4%, 100%, 98.3%, and 100% for the pressure-drop rate signals generated by pipelines A, B, C, and Cangzhou branch in the presence of pipeline leakage (the leak hole diameter is from 25 to 125 mm). Additionally, it demonstrated an average recognition accuracy of 97.42% for the pressure-drop rate signals generated by pipelines under other operating conditions. Through cooperating with Supervisory Control And Data Acquisition system, this method provides a more relevant approach to determine the shutdown conditions of a block valve while preventing mistaken actions.

Effects of constructional tolerances on lateral resistance of steel–concrete composite modular buildings with various connection rigidity

This study uses the non-linear static (pushover) analysis to investigate the lateral resistance of mid-rise and high-rise composite modular buildings with out-of-verticality resulting from constructional tolerances. A series of 40 full-scale simulation tests are conducted using a 3-D finite element model. Based on the results, three distinctive failure modes are identified for the multi-storey modular buildings subjected to lateral load. A comparison between the lateral responses of modular buildings with different deviations suggests that the standard constructional tolerances do not largely impair the overall performance of the modular buildings, while the exaggerated constructional tolerances result in average reductions of up to 9% in terms of global stiffness and load capacity. The relative significance of the connection rigidity is also assessed, including the connections within and between the modular units, namely the intra-module and the inter-module connections. It shows that the rotational stiffness of intra-module connections strongly influences global structural performance, while the rotational stiffness of inter-module connections has minor impacts. Moreover, the results indicate that the translational forces at inter-module connections may increase by up to 131% when constructional tolerances are introduced, which should be carefully addressed to facilitate the safety design of such connections.

Investigation of Neurocognitive Deficits, Quality of Life, and Functional Performance in Ultra-High-Risk Individuals Compared to Familial High-Risk Individuals for Schizophrenia.

Objective: This study aimed to investigate neurocognitive functioning, quality of life, and global functional performance in Ultra-High Risk (UHR) individuals compared to Familial High-Risk (FHR) individuals for developing schizophrenia. Method : An observational cross-sectional study was conducted using a convenient sampling method at Roozbeh Hospital in Tehran, Iran, from June 2017 to January 2020. The study included 40 UHR individuals based on the Structured Interview for Psychosis Syndrome (SIPS) interview, as well as 34 FHR individuals due to genetic risk. Neurocognitive functioning, quality of life, and global functional performance were assessed by using the Cambridge Automated Neuropsychological Test Battery (CANTAB) and Controlled Oral Word Association Test (COWAT), Quality of Life Scale (QLS), and Global Assessment of Functioning (GAF). Results: UHR individuals for schizophrenia demonstrated significant lower scores in phonemic and semantic verbal fluency (t = 6.218, P < 0.001; t = 4.184, P < 0.001, respectively), more total errors for spatial working memory (t = -5.874, P < 0.001), and fewer problems solved in minimum moves in Stocking of Cambridge (SOC) (t = -2.706, P < 0.01) compared to FHR individuals. Intra-Extra Dimension (IED) did not differ significantly between the two groups. Moreover, the study indicated significant GAF decline (F = 79.257, P < 0.001) and lower total score on the QLS (t = -10.655, P < 0.001) in UHR compared to FHR individuals. Conclusion: It is possible to differentiate UHR individuals from FHR individuals through neurocognitive, quality of life, and global functioning assessment.

FedSIGN: A sign-based federated learning framework with privacy and robustness guarantees

Federated learning enables clients to train a global model jointly without sharing their private local datasets. Despite its benefits, due to the untrustworthiness of clients and the server, traditional federated learning faces the risk of privacy leakage and poisoning attacks. Privacy-preserving methods change the original model parameters whereas robust aggregation algorithms required accurate parameters. To solve such a dilemma, we propose a new framework named FedSIGN. On the one hand, it utilizes the sign of local model update to update the global model to protect privacy and improve efficiency. On the other hand, focus on the Sybil-based poisoning attack: malicious client controlled by a single adversary who directs those clients to launch a poisoning attack, we design a Poisoning Attack Detector to identify malicious clients based on the similarity between sign vectors. Experimental results show that FedSIGN resists privacy and poisoning attacks while maintaining better global model performance. Especially, FedSIGN is not affected by the number of malicious clients and is effective in both the IID and non-IID scenarios.

An objective structured practical examination for laboratory skills in a pharmacy technician programme

Background: Pharmacy technician education is typically at the pre-degree level and comprises instruction in scientific and clinical disciplines. The assessment of practical laboratory skills often utilises attainment-referenced methods, which are not always appropriate for vocationally-focused programmes. Methods: An objective structured practical examination (OSPE) was introduced to assess student competency in three key areas (accurate weighing, calibrating a pH meter and performing a dilution). Students were assessed using weighted criterion-based assessment criteria and an overall global performance rating, which allowed cut scores to be determined using a borderline regression method. Student opinions were collected using online questionnaires on a five-point Likert scale. Results: The move to OSPEs did not significantly alter the distribution of student results from previous years (mean ± SD, OSPE vs legacy: 77 ± 19% vs 73 ± 21%), suggesting that academic integrity was maintained. There was a high level of consistency in Likert score responses (Cronbach’s α = 0.823), with students clearly favouring the OSPE approach. Conclusion: The move to an OSPE-based assessment was successful and provided a basis for the development of similar assessment strategies in the pharmacy technician programme.

Changes in automotive value chain participation on the integrated periphery – The case of Czechia and Hungary

AbstractCentral and Eastern European countries, including Czechia and Hungary, have become parts of the integrated periphery in the automotive industry. Through input-output analysis, company data and interviews, the article reveals the determining role of the industry in both economies and their deep integration in global value chains (GVCs). In addition to these similarities, the analysis reveals that domestic, simple and complex global value chain performances, ownership structures, the scale and types of upgrading tendencies as well as the consequences of the appearance of newcomers in the industry show different patterns of GVC structures over time. Due to these, the development paths of the two countries widely differ.

Effects of Circumferential and Longitudinal Ribs and Grooves in Swirl Cooling on Characteristics of Pressure Drop and Heat Transfer

The impact of roughened swirl cooling chamber walls on heat transfer and fluid flow characteristics is explored in this study. This study compares ten roughened wall shapes against a smooth wall in a swirl cooling chamber. The roughened wall shapes are circumferential and longitudinal with and without cuts. The circumferential cases consist of four cases. The first case, designated as a circumferential groove (CG), contains eight grooves along the surface. The second case, designated as a circumferential groove cut (CGC), has eight cut grooves along the surface. The third case is called a circumferential rib (CR), and the fourth case is called a circumferential rib cut (CRC), and it has eight ribs without and with surface cuts. While the longitudinal cases consist of six cases. The first four cases, which have six longitudinal grooves and ribs with and without cuts, are longitudinal groove (LG), longitudinal groove cut (LGC), longitudinal rib (LR), and longitudinal rib cut (LRGC). The final two longitudinal cases, longitudinal rib groove (LRG) and Longitudinal rib and groove cut (LRGC), have hybrid grooves and ribs without and with surface cuts. Bulk temperature averaged circumferential heat transfer ratio, friction factor, global thermal performance factor, and turbulent kinetic energy are among the variables that were examined. Simulations are performed using CFD codes. These examined parameters are contrasted with results from previous studies. The findings demonstrate that heat transfer is boosted by roughly 2.7, 2.6, and 2.5 times higher in cases with LG, CG, and CGC, respectively, than in the smooth case. The use of rough walls in the swirl cooling chamber boosts the global thermal performance factor; the LG case has a 2.8 times higher global thermal performance factor than a smooth case. In comparison with alternative internal cooling technologies, a grooved wall, as shown in the LG case, has the best heat transfer characteristics.