Basis Of Measurements Research Articles

Repetitive assembly basic action detection and standard work measurement based on deep learning

ABSTRACT Researchers extensively use deep learning for assembly task action recognition due to its superior feature representation. However, current methods fail to integrate assembly actions with basic human movements, resulting in poor generalization. Moreover, most research focuses on estimating operation times without computing standard work times. To address this issue, this study uses a deep learning method to detect basic repetitive assembly actions and compute their normalized time. This paper uses the TadTR model to determine each assembly operation’s average observation time and operation category. MS-G3D was then used to recognize the MOD action pairs and obtain the basic MOD actions and PTS times for each operation. The synthetic evaluation method was subsequently employed to obtain the evaluation coefficient, ultimately determining standard working hours.This study shows that the accuracy of the left and right MS-G3D models is 0.8804 and 0.7957, respectively, and the deviation of observation hours is less than 10%. Supplementary experiments further validated the proposed method’s flexibility, showing an observation time deviation of less than 5%. Thus, the standard work time measurement method proposed in this study provides finer-grained recognition of assembly actions and a more objective measure of standard working hours.

The Evolution of Ransomware: Tactics, Techniques, and Mitigation Strategies

Ransomware has developed into one of the most dangerous cyber threats. It is developed for the purpose of encrypting the data, and the owner is to pay the specified amount of money for the decoder. From simple rudimentary to developed sort with advanced encryption methods and modern distribution techniques, this research article is a complete historical journey of ransomware. Some of the specific aspects of ransomware development include the process of how the attackers act, starting from the traditional approach of spraying the malware to another strategic approach of singling out industries and organizations for attacks.The article also analyzes how the world came to know about ransomware-as-a-service (RaaS). The analysts have attributed the docket to the fact that it has also made it easy for inexperienced and unskilled hackers to embrace the commodity space by merely launching ransomware attacks. Current ransomware strategies that hackers employ include double extortion, where they steal information and threaten to publish the stolen data publicly, besides demanding a ransom for the encrypted information. The use of coins in the payment of ransom is examined with regard to their anonymity and the emergence of targeting of key infrastructure and other giant entities.Tactics that can allow for minimizing the attacks’ impact are equally crucial in the fight against ransomware. Some of the extensive strategies highlighted in this article are basic cyber hygiene measures, backup and restore methodologies, and endpoint detection and prevention measures. Implementing frequent training exercises to sensitize users to the risks of ransomware attacks is underlined, as is the need to have a clearly outlined incident response procedure in case of infections to enable the speedy handling of the attacks.This paper argues that an ability to comprehend the complexity of today’s ransomware, along with multiple layers of a defense system, will help organizations improve their readiness against this adaptive and continuous threat. This paper contains an analysis of the current state of ransomware and measures that need to be taken to curb the threat posed by this cyber threat; thus, it can serve as a valuable resource to cybersecurity experts.

2024 RECOVER Guidelines: Monitoring. Evidence and knowledge gap analysis with treatment recommendations for small animal CPR.

To systematically review evidence on and devise treatment recommendations for patient monitoring before, during, and following CPR in dogs and cats, and to identify critical knowledge gaps. Standardized, systematic evaluation of literature pertinent to peri-CPR monitoring following Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Prioritized questions were each reviewed by Evidence Evaluators, and findings were reconciled by Monitoring Domain Chairs and Reassessment Campaign on Veterinary Resuscitation (RECOVER) Co-Chairs to arrive at treatment recommendations commensurate to quality of evidence, risk:benefit relationship, and clinical feasibility. This process was implemented using an Evidence Profile Worksheet for each question that included an introduction, consensus on science, treatment recommendations, justification for these recommendations, and important knowledge gaps. A draft of these worksheets was distributed to veterinary professionals for comment for 4weeks prior to finalization. Transdisciplinary, international collaboration in university, specialty, and emergency practice. Thirteen questions pertaining to hemodynamic, respiratory, and metabolic monitoring practices for identification of cardiopulmonary arrest, quality of CPR, and postcardiac arrest care were examined, and 24 treatment recommendations were formulated. Of these, 5 recommendations pertained to aspects of end-tidal CO2 (ETco2) measurement. The recommendations were founded predominantly on very low quality of evidence, with some based on expert opinion. The Monitoring Domain authors continue to support initiation of chest compressions without pulse palpation. We recommend multimodal monitoring of patients at risk of cardiopulmonary arrest, at risk of re-arrest, or under general anesthesia. This report highlights the utility of ETco2 monitoring to verify correct intubation, identify return of spontaneous circulation, evaluate quality of CPR, and guide basic life support measures. Treatment recommendations further suggest intra-arrest evaluation of electrolytes (ie, potassium and calcium), as these may inform outcome-relevant interventions.

Metric cake shop: A serious game for supporting education on ISO/IEC/IEEE 15939:2017 – Systems and software engineering – Measurement process in the context of an undergraduate software engineering course

The educational use of serious games has increased in recent years and their pedagogical benefits have been widely documented, especially for teaching specialized knowledge on Software Engineering at undergraduate level. The results achieved by several studies in this area show that it has been possible to increase the student interest and motivation in learning complex topics that tend to be difficult to understand when knowledge is imparted through traditional lectures, as is the case with software process standards. In this regard, the ISO/IEC/IEEE 15939:2017 standard describes a software measurement process, one of the main topics that must be addressed in any curriculum for undergraduate degree programs in Software Engineering. However, despite their relevance in industry, many students are leaving university without basic software measurement skills. With the aim of identifying serious games for teaching the software measurement process at undergraduate level, a Systematic Literature Review was conducted in order to collect, classify and analyze information which enabled the authors of this study to propose a serious game that improves the understanding and education on the ISO/IEC/IEEE 15939:2017 standard. The results from an empirical evaluation involving Computer Science undergraduates provided evidence that positive learning experiences occurred when playing the created serious game.

Efficacy of topical agents in oral mucositis prevention: Systematic review and network meta-analysis.

Oral mucositis (OM) is considered one of the most common side effects of patients undergoing cancer therapy. OM prevention plays a crucial role in the effectiveness of cancer treatment and the patient's quality of life. Different preventive treatments have been proposed in clinical trials, however with inconclusive results. A systematic review search was conducted in PubMed, Scopus, Web of Science, and Cochrane Database to answer the PICO question: in cancer patients, do specific topical agents compared to standard treatments or placebo reduce the onset and severity of oral mucositis? The risk of bias was assessed, and a network meta-analysis was conducted. Of 2913 results, 30 randomized clinical trials were considered suitable for inclusion. A total of 2564 patients were analyzed, of which 1284 belonged to the test group and 1280 belonged to the control group. Natural products were the most used, followed mainly by antimicrobial agents, coating agents, and basic oral care measures. Topical sucralfate resulted in the most powerful intervention for the OM prevention (OR = 0.04, 95%C.I. = 0.01-0.25, p-value = 0.001). Due to its cytoprotective action, low cost, ease of administration, and safety, sucralfate could become a potential ally to prevent the onset of OM during cancer therapy.

The Outcomes of Competency-Based Program About Language for Communication for Early Primary School Students

This study aims to achieve three primary objectives: firstly, to develop a competency-based program tailored for communication purposes; secondly, to examine developmental strides in language proficiency for effective communication; and thirdly, to compare the language proficiency among early primary school students, both pre-and post-implementation of the program. The sample consisted of third-grade students from Anuban Chantarat School, Sahatsakhan District, Kalasin Province, Thailand, who were enrolled in the second semester of the academic year 2022. The total number of participants was 20 by cluster random sampling. The data collection instruments used in this study were: 1) a program usage manual, 2) a lesson plan, and 3) a language communication competence assessment test. Data analysis involved basic statistical measures, including frequency, percentage, mean, standard deviation, and hypothesis testing using the t-test (Dependent Samples). The study findings revealed that: 1. Competency-based program for early primary school students’ language communication, comprising six components: 1) Principles, 2) Objectives, 3) Content, 4) Development Processes, 5) Teaching and Learning Materials, and 6) Assessment, underwent quality assessment by seven experts. The results indicated a high level of conformity and appropriateness. 2. Students who participated in the language competency-based program demonstrated enhanced language communication competence across all learning units. 3. The language communication competence of students after using the program was statistically significantly higher than before using the program at a significance level of .05. The language competence was found to be at a developing level of A2 proficiency.

Protection of public health at the level of supranational organizations

Emergence of international integration processes raises new issues of the development of the legislative and regulatory framework at the supranational level for public health benefit. The paper explains the need to actively protect the interests of public health at the supranational level based on the analysis of relevant aspects of the policy, structure, functioning and experience of the largest regional integration structures – the Eurasian Economic Union and the European Union. The review of the basic principles, approaches and measures is carried out, using the example of ensuring food safety, which is directly related to solving the problem of prevention and control of infectious and non-communicable diseases. The relevant regulatory framework of the World Trade Organization (WTO), as well as the development of technical regulation, are discussed. The relevance of cooperation between supranational structures and the World Health Organization and other relevant UN organizations in the field of public health is emphasized. Specialists in this field are called upon to participate in the regulation of international integration processes, including the activities of the WTO, trade and investment agreements, and tariff policy.

Factor structure and measurement invariance of the Depression anxiety stress scale (DASS-21) in Chinese left-behind and non-left-behind children: an exploratory structural equation modeling approach

BackgroundComprehensive data has shown that adolescents often suffer from depression, anxiety, and low self-esteem, and are in a particularly fragile stage of psychological, physiological, and social development. Left-behind children in particular tend to have significantly higher, state anxiety and depression compared to non-left-behind children. The Depression, Anxiety, and Stress Scale (DASS-21) is an effective tool for evaluating depression, anxiety, and stress, and is used to measure levels of depression, anxiety, and stress in groups from a variety of backgrounds. The purpose of this study was to determine the effectiveness, reliability, and measurement invariance of the DASS-21 in Chinese left-behind children.MethodThe test and re-test method was used (N = 676), and the exploratory structural equation model (Mplus v.8.3) used to verify basic measurement models. For measurement invariance, the configural, weak, strong, and strict models were tested. The reliability of the DASS-21 was also tested using the collected data.ResultsAnalysis results showed that the DASS-21 had a stable three-factor structure in the sample of left-behind children in China. The measurement invariance test showed that gender and time not only had strong invariance, but also strict invariance. The results of cross left and non-left invariance indicated a lack of strict invariance. Finally, the McDonald’s omega coefficient of the DASS-21 total scale was 0.864, and the internal consistency of each subscale was also good.ConclusionsThe DASS-21 is shown to be an effective and reliable tool for measuring depression, anxiety and stress in Chinese left-behind children.

BODYFAT: a new calculator to determine the risk of being overweight validated in Spanish children between 11 and 17 years of age

The assessment of body fat of children in primary care requires consideration of the dynamic changes in height, weight, lean mass, and fat mass during childhood growth. To achieve this, we aim to develop a predictive equation based on anthropometric values, with optimal diagnostic utility. This is a cross-sectional observational study, involving schoolgoers aged 11–17 years in the Vigo metropolitan area. Out of 10,747 individuals, 577 were randomly recruited. Variables: age, sex, ethnicity/country of origin, weight, height, 8 skinfolds, 3 diameters, 7 perimeters, and 85% percentile of body fat mass as the gold standard. Generalized additive regression was selected by cross-validation and compared using receiver operating characteristic curves (ROC curves). Sensitivity, specificity, positive and negative predictive values, true positive and true negative values, false positive and false negative values, accuracy, and positive and negative likelihood ratios were calculated. Two models were identified. The optimal model includes sex, weight, height, leg perimeter, and arm perimeter, with sensitivity of 0.93 (0.83–1.00), specificity of 0.91 (0.83–0.96), accuracy of 0.91 (0.84–0.96), and area under the curve (AUC) of 0.957 (0.928–0.986). The second model includes sex, age, and body mass index, with sensitivity of 0.93 (0.81–1.00), specificity of 0.90 (0.80–0.97), accuracy of 0.90 (0.82–0.96), and an AUC of 0.944 (0.903–0.984).Conclusion: Two predictive models, with the 85th percentile of fat mass as the gold standard, built with basic anthropometric measures, show very high diagnostic utility parameters. Their calculation is facilitated by a complementary online calculator.What is Known:• In routine clinical practice, mainly in primary care, BMI is used to determine overweight and obesity. This index has its weaknesses in the assessment of children.What is New:• We provide a calculator whose validated algorithm, through the determination of fat mass by impedanciometry, makes it possible to determine the risk of overweight and obesity in the community setting, through anthropometric measurements, providing a new practical, accessible and reliable model that improves the classification of overweight and obesity in children with respect to that obtained by determining BMI.

Prediction of post-delivery hemoglobin levels with machine learning algorithms

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05–0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80–0.85]) and fibrinogen levels (B:0.01 [0.01–0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30–1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB. The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.

Modern asthma management

Symptom prevention and remission are the goals of asthma treatment. Precise phenotyping of the patients, including history, lung function, allergology and measurement of type 2 biomarkers, is the essential prerequisite for treatment success. Basic measures, treatment with DMAADs ("disease-modifying anti-asthmatic drugs": predominantly inhaled corticosteroids, biologics, and allergen immunotherapy) and treatment of comorbidities are the cornerstones of modern asthma management. The treatment of upper airway diseases such as allergic rhinitis (often associated with early-onset asthma) or chronic rhinosinusitis (often associated with adult-onset asthma, with 2 forms: either with nasal polyps, CRSwNP, or without nasal polyps, CRSsNP) is of major importance in this regard.

Turkish earthquake death tolls: lessons from downward counterfactual analysis and informal construction

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaraş earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Non-compliance is often hard to identify, but construction amnesties make non-compliance more transparent and trackable. The disastrous outcome of the Kahramanmaraş earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance.

Modified Delphi RAND/UCLA Consensus on Recommendations for Preventing Surgical Site Infections in Colorectal Surgery

Abstract Surgical site infections (SSIs) are a major health care issue, with worse outcomes and higher costs in Latin America than in other countries. To reduce SSIs in colorectal surgery (CRS), we conducted a mixed-method study between May and December 2021, which included a literature review, Delphi expert consensus panel, and SSI reduction recommendations. The literature review identified 150 relevant articles, deriving 19 recommendations based on the GRADE framework. A panel of 14 experts from Argentina, Chile, and Uruguay, specializing in infection control, participated in a modified Delphi consensus process. They evaluated the recommendations using a Likert scale and assessed 13 criteria to reach consensus. After rounds of evaluation and expert discussions, 6 recommendations achieved consensus and were deemed appropriate for SSI prevention in CRS: presurgical bathing, surgical antibiotic prophylaxis, mechanical colon preparation, preincision antisepsis with alcohol and antisepsis, monitoring of hyperglycemic patients, and control of normothermia. Implementing such a multifaceted set of recommendations presents challenges, and a bundled approach has been suggested to improve the adherence to complex prevention strategies. Previous research has underscored the importance of effective implementation strategies, including engagement, education, execution, and evaluation, as well as fostering a culture of safety and readiness for change. This study offers expert recommendations for preventing SSIs in CRS, particularly in Latin America. These are crucial for regional health care organizations. Compliance with basic infection prevention measures remains a concern. A bundled approach was suggested to evaluate adherence to health care protocols in Argentina and Uruguay. Aligning guidelines and practices can increase patient safety and reduce the impact of SSIs on Latin American health care.

Standardization in paediatric echocardiographic reporting and critical interpretation of measurements, functional parameters, and prediction scores: a clinical consensus statement of the European Association of Cardiovascular Imaging of the European Society of Cardiology and the Association for European Paediatric and Congenital Cardiology.

This document has been developed to provide a guide for basic and advanced reporting in paediatric echocardiography. Furthermore, it aims to help clinicians in the interpretation of echocardiographic measurements and functional data for estimating the severity of disease in different paediatric age groups. The following topics will be reviewed and discussed in the present document: (i) the general principle in constructing a paediatric echocardiographic report, (ii) the basic elements to be included, and (iii) the potential and limitation of currently employed tools used for disease severity quantification during paediatric reporting. A guide for the interpretation of Z-scores will be provided. Use and interpretation of parameters employed for quantification of ventricular systolic function will be discussed. Difficulties in the adoption of adult parameters for the study of diastolic function and valve defects at different ages and pressure and loading conditions will be outlined, with pitfalls for the assessment listed. A guide for careful use of prediction scores for complex congenital heart disease will be provided. Examples of basic and advanced (disease-specific) formats for reporting in paediatric echocardiography will be provided. This document should serve as a comprehensive guide to (i) structure a comprehensive paediatric echocardiographic report; (ii) identify the basic morphological details, measures, and functional parameters to be included during echocardiographic reporting; and (iii) correctly interpret measurements and functional data for estimating disease severity.

Green fuel production from waste fried sunflower oil using reusable mesoporous COP-KOH catalyst with a kinetic study

Declining fossil fuel reserves and fluctuating petroleum prices have prompted global research efforts to develop clean, renewable, and sustainable alternatives to traditional fossil fuels. In this study, a reusable and eco-friendly activation catalyst was designed by combining KOH with a polymer. A series of three polymer catalysts were synthesized, and their catalytic activities were compared with those of COP-KOH, COP-K3PO4 and COP-KF. It was found that the prepared COP-KOH catalyst showed the highest activity. The catalyst was characterized by FT-IR, which confirmed the presence of potassium support successfully coordinated on the COP. Additional analyses included N2 and BET, XPS, SEM, mapping, and basicity measurement by ICP analysis. The prepared catalysts a possessed mesoporous structure, high surface area of 4.2961 m2/g−1, pore volume of 2.2439 m2/g−1, and high basicity. Here, the activity and stability of the potassium modified mesoporous polymer COP-KOH catalyst in green fuel synthesis from waste fried sunflower oil was evaluated. The green fuel conversion rate of 96.42 % was achieved under optimal reaction conditions, with a molar ratio of 6:1 methanol to fried sunflower oil, a catalyst concentration of 85 wt%, a reaction temperature of 61 °C, and a reaction time of 4.5 h. A satisfactory yield of 92.1 % was obtained after use for four consecutive cycles without significant deactivation, and after the fourth cycle catalyst was reactivated with KOH. The characteristic differences in the FT-IR results of the fresh catalyst, used catalyst, and re-activated COP-KOH catalyst, indicated the mode of interaction between the catalyst and reactants. The kinetics of the transesterification reaction was also determined, and the activation energy was found to range from 20.9 to 23.4 kJ.mol−1. The properties of the green fuel were found to be in good agreement with the ASTM D6571-02 standard. The catalyst is recyclable, and using inexpensive feedstock makes the overall process more cost-effective and environmentally friendly.

150 PDC on workplace air exposure assessment using freeware apps Part I: Basic characterisation and measurement processing

150 PDC on workplace air exposure assessment using freeware apps Part I: Basic characterisation and measurement processing

A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies. The pre-processing of data has been performed effectively and a host of ML methods have been applied to classify the outputs into normal, regular node or untrustable /malicious node. We have evaluated the analysis results in presence of attacks as well as without presence of attacks, supervised machine learning techniques basic measurements like accuracy, True positive, False positive etc. Our proposed trust model shows better accuracy, nearby 98% and we have shown that our proposed machine learning based security model performs better to get rid of DDoS attacks on integrated LEO satellite-terrestrial networks without compromising on the packet routing efficiency. We are able to improve routing speed and improve network security against distributed denial of service (DDoS) attacks by integrating an ensemble-based trust model trained on NSL-KDD+STIN+Exata Simulated resultant dataset with ACO for routing decisions. In dynamic network scenarios, as trustworthiness is an essential criterion in route decision-making, this proposed approach signifies resilient and adaptable routing.

The impact of livelihood resilience and climate change perception on farmers' climate change adaptation behavior decision

PurposeAdopting adaptive behavior has become a basic measure for farmers because the increasingly severe climate change is affecting agricultural production. Perception is a critical first step in adopting adaptive behaviors. Livelihood resilience represents a farmer's ability to adapt to climate change. Therefore, this article aims to explore the impact of livelihood resilience and climate change perception on the climate change adaptation behavior of farmers in the Qinling Mountains region of China.Design/methodology/approachIn this study, 443 micro-survey data of farmers are obtained through one-on-one interviews with farmers. The Logit model and Poisson regression model are used to empirically examine the impact of farmers' livelihood resilience and climate change perception on their climate change adaptation behaviors.FindingsIt was found that 86.68% of farmers adopt adaptive behaviors to reduce the risks of facing climate change. Farmers' perception of extreme weather has a significant positive impact on their adaptive behavior under climate change. The resilience of farmers' livelihoods and their perception of rainfall have a significant positive impact on the intensity of their adaptive behavior under climate change. Climate change adaptation behaviors are also different for farmers with different levels of livelihood resilience.Originality/valueBased on the results, policy recommendations are proposed to improve farmers' perception of climate change, enhance the sustainability of farmers' adaptive behavior to climate change, strengthen emergency management and infrastructure construction and adjust and upgrade farmers' livelihood models.

Stress testing for IInd pillar life-cycle pension funds using hidden Markov model

AbstractThis paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

Strong Bases and beyond: The Prominent Contribution of Neutral Push-Pull Organic Molecules towards Superbases in the Gas Phase.

In this review, the principles of gas-phase proton basicity measurements and theoretical calculations are recalled as a reminder of how the basicity PA/GB scale, based on Brønsted-Lowry theory, was constructed in the gas-phase (PA-proton affinity and/or GB-gas-phase basicity in the enthalpy and Gibbs energy scale, respectively). The origins of exceptionally strong gas-phase basicity of some organic nitrogen bases containing N-sp3 (amines), N-sp2 (imines, amidines, guanidines, polyguanides, phosphazenes), and N-sp (nitriles) are rationalized. In particular, the role of push-pull nitrogen bases in the development of the gas-phase basicity in the superbasicity region is emphasized. Some reasons for the difficulties in measurements for poly-functional nitrogen bases are highlighted. Various structural phenomena being in relation with gas-phase acid-base equilibria that should be considered in quantum-chemical calculations of PA/GB parameters are discussed. The preparation methods for strong organic push-pull bases containing a N-sp2 site of protonation are briefly reviewed. Finally, recent trends in research on neutral organic superbases, leaning toward catalytic and other remarkable applications, are underlined.