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Let p_[1,r;t] be defined by ∑n=0∞p[1,r;t](n)qn=(E1Er)t, where t is a non-zero rational number, r ≥ 1 is an integer and Er=∏n=0∞(1−qr(n+1)) for |q| < 1. The function p_[1,r;t](n) is the generalisation of the two-colour partition function p_[1,r;−1](n). In this paper, the authors prove some new congruences modulo odd prime ℓ by taking r = 5, 7, 11 and 13, and non-integral rational values of t.

Using q-series expansion/identities, the authors established general congruence modulo prime number for two-colour partition function.

In the paper, the authors study congruence properties of two-colour partition function for fractional values. The authors also give some particular cases as examples.

The partition functions for fractional value is studied in 2019 by Chan and Wang for Ramanujan's general partition function and then extended by Xia and Zhu in 2020. In 2021, Baruah and Das also proved some congruences related to fractional partition functions previously investigated by Chan and Wang. In this sequel, some congruences are proved for two-colour partitions in this paper. The results presented in the paper are original.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Lockdowns and the forced closure of certain industries during the COVID-19 pandemic severely impacted workers, particularly entrepreneurs, who were financially and emotionally involved in their businesses. Two studies have shown that entrepreneurs have a lower willingness to get vaccinated against COVID-19 than employees. In this study, the authors try to replicate the vaccination gap between the two groups. Second, the authors study whether the difference persists when controlling for demographics, vaccination attitudes and the COVID-19 context, including the financial impact of the pandemic, its effect on the wellbeing of workers, and government attitudes. Third, the authors study whether there are differences in how the context of the pandemic relates to vaccination willingness for entrepreneurs and employees.

The authors conduct regression analyses using three large datasets. The authors study vaccination status (February 2022) in a 27-country Eurobarometer sample, vaccination intention (December 2020) in a Dutch sample from the LISS panel and vaccination status (July 2021) in a sample from the Understanding America Study (UAS).

All datasets confirm that entrepreneurs have lower vaccination intention and coverage than employees. Even when controlling for the variables described in the LISS and UAS datasets, this negative difference remains. The study results also indicate that demographics, especially vaccination attitudes, are much more important than contextual influences in the decision to get vaccinated against COVID-19.

The authors are the first to dive further into the vaccination differences between entrepreneurs and employees. They advise further research into the drivers of this gap, specifically relating to the role of personality and social normative influences.

Multinational enterprises (MNEs) establish a wide range of alliances to access the critical resources that they may need at any one time. Although inter-organizational relationships (IORs) constitute the channels through which social capital flows, MNEs should consider which mechanisms or characteristics of the relations facilitate their actual mobilization.

A definition of alliance types yielded the parameters for an ordinary least squares regression of a sample from top global-reach MNEs from the airline industry.

The results showed that certain kind of alliances favored the actual mobilization of social capital.

Managers of MNEs must select the type of IOR taking into account the objective they pursue and the type of activity they will include.

Analyzing the factors that influence the degree of mobilization of social capital and how MNEs actually use the resources of the partners require the establishment of a theoretical framework and the development of empirical evidence.

las Empresas Multinacionales (MNEs) establecen una amplia gama de alianzas para acceder a los recursos críticos externos que puedan necesitar en cualquier memento. Las MNEs deben considerar qué mecanismos o características de las relaciones facilitan su movilización real.

una definición de los tipos de alianza produjo los parámetros para una regresión de mínimos cuadrados ordinarios de una muestra de las principales MNEs de alcance global de la industria de las aerolíneas.

Los resultados mostraron que ciertos tipos de alianzas favorecieron la movilización real del capital social.

Analizar los factores que influyen en el grado de movilización del capital social y cómo las MNEs utilizan en la práctica los recursos de sus socios, requiere del establecimiento de un marco teórico y el desarrollo de evidencia empírica.

This study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.

The research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.

EPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km² of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.

This research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

This paper aims to discuss the classification of targets based on their radar cross-section (RCS). The wavelength, the dimensions of the targets and the distance from the antenna are in the order of 1 mm, 1 m and 10 m, respectively.

The near-field RCS is considered, and the physical optics approximation is used for its numerical calculation. To model real scenarios, the authors assume that the incident angle is a random variable within a narrow interval, and repeated observations of the RCS are made for its random realizations. Then, the histogram of the RCS is calculated from the samples. The authors use a nearest neighbor rule to classify conducting plates with different shapes based on their RCS histogram.

This setup is considered as a simple model of traffic road sign classification by millimeter-wavelength radar. The performance and limitations of the algorithm are demonstrated through a set of representative numerical examples.

The proposed method extends the existing tools by using near-field RCS histograms as target features to achieve a classification algorithm.

The study assess the selection ability and market timing skills of mutual fund and unit trust managers in Ghana.

The study uses an improved survivorship bias-free dataset of yearly after-fee returns of all mutual funds and unit trusts operating in Ghana from January 2011 to December 2019, cumulating in nine years of quantitative fund data. The authors assess Mutual funds and Unit trusts that ever existed, “alive” or “dead,” over the sample period in the study. The authors construct factor loadings to enable the application of multifactor models in the analysis. The authors apply the unconditional versions of the Jensen alpha, Fama-French three-factor, and Carhart four-factor models to determine the selection ability and market timing skills of 32 mutual funds and 17 unit trusts. The authors deploy HAC-consistent robust standard errors to the OLS estimations to subdue the effect of heterogeneity and autocorrelation.

The results indicate that, on average, mutual funds and unit trust managers possess market timing skills but no selection ability. When the results are decomposed into fund types, fixed-income and balanced mutual fund managers possess selection ability and market timing skills.

To the authors' best knowledge, this study is the earliest to examine the selection ability and market timing skills of both mutual fund and unit trust managers in Sub-Saharan Africa (SSA). It is also the earliest to construct factor loadings for the Ghana stock market.

Due to the increasing incidence of dementia in Ireland, there is a need to prepare occupational therapy students for future careers in dementia-focused health care. The purpose of this study are to measure the knowledge and attitudes of Irish undergraduate occupational therapy students towards older people with dementia and examine related variables including year of study, personal and placement dementia experiences and future career choice.

Data was gathered using a cross-sectional online survey, incorporating the Alzheimer’s Disease Knowledge Scale and the Dementia Attitudes Scale, which was analysed using descriptive and inferential statistics.

Seventy-five responses were gathered and analysed, indicating more advanced occupational therapy students demonstrate higher levels of dementia knowledge, but dementia attitudes remain similar across different year groups. Students with clinical dementia experiences displayed comparable levels of dementia knowledge and attitudes to those without. However, students with familial dementia experiences displayed significantly more positive attitudes. The likelihood of selecting a future career with older adults with dementia significantly related to students’ positive dementia attitudes but not dementia knowledge.

To the best of the authors’ knowledge, this is the first Irish study that focuses on undergraduate occupational therapy students’ dementia knowledge and attitudes. The results could be used to inform and develop Irish undergraduate occupational therapy programmes.

This study aims to examine the impact of market competition, and capital regulation on the cost of financial intermediation of banks of the Bangladesh banking industry.

This study has used a balanced panel dataset comprised of 340 firm-year observations for 34 commercial banks in the Bangladesh banking industry from 2011 to 2020. The Prais Winsten panel estimator has been used to assess the impact of market competition and capital regulation on the cost of financial intermediation of banks.

Based on the regression results, this study has documented that greater market competition results in a lower cost of financial intermediation for banks. Similarly, an increase in the regulatory capital of banks increases the cost of financial intermediation of banks. The main findings of this study are found robust by using alternative proxies for the cost of financial intermediation, market competition and capital regulation. The regression results also suggest that private commercial banks tend to have a higher cost of financial intermediation than state-owned commercial banks.

The regulatory reforms should aim to foster sustainable and optimal market competition for the Bangladesh banking industry to regulate the market power of banks to reduce the cost of financial intermediation. The regulatory authority of Bangladesh should find the optimal policy measures for implementing the capital regulation in the banking industry which would reduce the cost of financial intermediation margin of banks.

Unlike previous studies which have used structural market competition measures, this study has used non-structural market competition measures to assess the relationship between market competition and cost of financial intermediation in the Bangladesh banking industry.

Fingerprints and blood samples are important for the identification of individuals and criminals. The present study aims to identify the predominant fingerprint patterns and the association between the fingerprint patterns and ABO–Rh blood groups in Omani population.

A cross-sectional study was conducted on 200 Omani individuals aged 18 years (104 males and 96 females). The imprints of all right and left-hand fingers were taken, and the types of the fingerprints were determined using a standard protocol. The blood group of all the subjects was recorded. Chi-square test was performed to identify the association between the fingerprint patterns and the ABO and Rh blood groups.

The loop fingerprint pattern was the most common in Omani subjects (49.4%), followed by whorl (44.9%) and arch (5.7%) pattern. A significant association (p < 0.001) was found between gender and fingerprint pattern. The loop was the highest occurring pattern in the females (54.6%), while the whorl was more in males (50.0%). The whorl pattern was the most common in the AB+ and O− groups. The loop was a predominant pattern in the A+, A−, B+, B− and O+ groups. The Chi-square test also revealed a significant correlation between different fingerprint patterns and blood groups of the subjects (p < 0.001).

The present study is an outcome of undergraduate student's research project thesis for the Doctor of Medicine (MD) program. The results of the present study may help in creating a data bank for biometrics, which can be useful for diagnosing associated diseases and also help in identification of individuals.