bluuebunny/crossref_metadata_2025 huggingface dataset:
Role of Serum Soluble Interleukin-2 Receptor Level in the Diagnosis of Sarcoidosis: A Systematic Review and Meta-Analysis
Authors: Gupta Samiksha , Parmar Miloni , Padappayil Rana Prathap , Bansal Agam , Daouk Salim
Published: July 2022

Introduction

Serum Soluble Interleukin-2 Receptor (sIL-2R) levels are used clinically as a disease activity marker for systemic sarcoidosis. Studies have investigated the diagnostic role of serum soluble interleukin-2 receptor (sIL-2R) level for sarcoidosis relative to biopsy. We performed a systematic review and meta-analysis of studies evaluating the diagnostic utility of sIL-2R.

Methods

We carried out an electronic search in Medline, Embase, Google Scholar, and Cochrane databases using keyword and Medical Subject Heading (MeSH) terms: sarcoidosis and sIL-2R. Studies evaluating the sIL-2R levels as a diagnostic tool in clinically diagnosed or biopsy-proven sarcoidosis patients compared to control groups with non-sarcoidosis patients were included. Forest plots were constructed using a random effect model depicting pooled sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy.

Results

We selected ten studies comprising 1477 patients, with 592 in the sarcoidosis group and 885 in the non-sarcoidosis group. Pooled sensitivity and specificity of sIL-2R levels were 0.88 (95% CI: 0.75-0.95) and 0.87 (95% CI 0.73-0.94) respectively. Pooled negative predictive value and positive predictive value were 0.91 (95% CI 0.77-0.97) and 0.85 (95% CI 0.59-0.96) respectively with diagnostic accuracy of 0.86 (95% CI 0.71-0.93).

Conclusion

In addition to its utility as a marker of sarcoidosis disease activity, sIL-2R has high diagnostic accuracy. Despite the limitations of the heterogenous sarcoidosis population and different sIL-2R cutoffs, our results suggest that sIL-2R is an important biomarker that can be used to confirm sarcoidosis diagnosis in unconfirmed or unclear cases.
A taxonomic synopsis of unifoliolate continental AfricanVepris(Rutaceae) with three new threatened forest tree species from Kenya and Tanzania
Authors: Cheek Martin , Luke W.R. Quentin
Published: July 2022

Descriptions and illustrations are presented for three new species to science,Vepris udzungwaCheek, V. lukeiCheek (both Udzungwa Mts, Tanzania) andV. robertsoniaeCheek & Q. Luke (SE Kenyan kaya forests) in the context of a synoptic taxonomic revision, with an identification key to all the known unifoliolate taxa ofVeprisin continental Africa. The remaining species are given skeletal taxonomic treatments (lacking descriptions). One widespread species in montane eastern Africa is renamed asVepris simplexCheek because its previous name,Vepris simplicifolia(Engl.)Mziray is predated byVepris simplicifoliaEndl. (basionym ofSarcomelicope simplicifolia(Endl.)T.G. Hartley, a widespread species of Australia, Lord Howe and Norfolk Islands, and of New Caledonia).

Conservation assessments are presented for all species, or provisional conservation assessments are presented. Of the 13 taxa, 11 are considered threatened, of which six are VU, two EN and three CR, of which two are possibly extinct globally in the Uluguru Mts of Tanzania although not yet Red Listed on iucnredlist.org.
Discovering Patterns in Bipartite Networks
Authors: Costa Luciano da Fontoura
Published: July 2022

Bipartite networks constitute an especially important type of network that can be widely applied to model and study several intricate systems in many scientific areas, being also theoretically related to several network models and concepts. However, given a bipartite network the identification of specific recurrent patterns of interest often represents a substantial challenge. In the present work, we apply the coincidence methodology for translating data into networks as a means for automatically identifying repeating interconnection patterns in given bipartite networks. The important issue of normalization of the links strength is also addressed. Though the method is illustrated with respect to foodweb networks, its application is general and encompasses many other subjects and areas.
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Authors: Zhao Bo-Wei , Su Xiaorui , Hu Peng-Wei , Ma Yu-Peng , Zhou Xi , Hu Lun
Published: July 2022

The effectiveness of computational drug repositioning techniques has been further improved due to the development of artificial intelligence technology. However, most of the existing approaches fall short of taking into account the non-Euclidean nature of biomedical data. To overcome this problem, we propose a geometric deep learning (GDL) framework, namely DDAGDL, to predict drug-disease associations (DDAs) on heterogeneous information networks (HINs). DDAGDL can take advantage of complicated biological information to learn the feature representations of drugs and diseases by ingeniously projecting drugs and diseases including geometric prior knowledge of network structure in a non-Euclidean domain onto a latent feature space. Experimental results show that DDAGDL is able to identify high-quality candidates for Alzheimer’s disease (AD) and Breast neoplasms (BN) that have already been reported by previously published studies, and some of them are not even identified by comparing models.
Snake Herders: Novel anti-predator behavior by black-tailed prairie dogs in response to prairie rattlesnakes
Authors: Verdolin J.L. , Bledsoe Ellen K.
Published: July 2022

We describe a case of a unique antipredator behavior sequence in response to rattlesnakes observed in a population of black-tailed prairie dogs in Fort Collins, Colorado. An analysis of 5 independent opportunistic video recordings of 6 identifiable individuals revealed individuals across multiple social groups within the population engaged in novel behavioral responses to prairie rattlesnakes. Using Markov-chain analysis we found that prairie dogs engaged in non-random behavioral transitions and that specific pairs of behaviors were contributing to this pattern. We also observed that prairie dogs in this population engaged in novel responses to prairie rattlesnakes, including ‘escorting’ behavior, where a prairie dog would walk alongside the snake.
Dietary vitamin D intake and sun exposure are not associated with type 1 diabetic schoolchildren and adolescents: a first report in Algerian Sahara
Authors: Brikhou Slimane , Nouari Wafa , Bouazza Sofiane , Mezouar Chahrazed El , Benzian Zakaria , Talha Kheira , Aribi Mourad
Published: July 2022

Background

A great number of children and adolescents worldwide suffer from physiological Vitamin D (VD) deficiency, which has been associated to the sun exposure and, consequently, to the risk of the development of various autoimmune diseases, including type 1 diabetes (T1D). However, the association of the disease with VD intake and sun exposure have yet to be explored.

Materials and methods

We conducted a food frequency questionnaire and 24-hour recall food survey, using “Ciqual table 2016” in 335 type 1 diabetic and age- and gender-matched healthy Algerian school children and teenager pupils from sunny Saharan and relatively less sunny Northern regions, aged between 5 and 19 years old.

Results

Both dietary VD intake and VD levels were similar in T1D patients when comparing between North and South regions (for the two comparisons, p > 0.05). Neither sun exposure, nor VD intake was associated with the disease (respectively, relative risk [RR] = 1.050, p = 0.680; RR = 1.082, p = 1.000. For Cochran and Mantel-Haenszel analysis; RR = 0.841, p = 0.862). VD intake showed a significant difference between diabetics and non-diabetics in sunny region (p = 0.022). Additionally, significant differences were highlighted between normal and T1D schoolboys (p = 0.038), and when comparing the two groups according to the dry areas (p = 0.016). Moreover, in contrast with the levels of circulating VD, which is decreased in T1D patients than in healthy controls, those of VD intake was significantly higher (p < 0.05), especially in male patients and in those with balanced diet, poor protein or carbohydrate consumption, a particular food intolerance, and a regular meal (p < 0.05), as well as in patients with a moderate or low consumption of cooked meals or steamed food (p < 0.01). Conversely, VD intake was markedly lower in type 1 diabetics than in controls regarding dry sunny region, including Adrar area, as well as in low fatty foods and eggs consumption (p < 0.05 for all comparisons). Nevertheless, relative risk of sun exposure and dietary vitamin D intake according to the WHO standard showed no significant association with T1D (common Mantel-Haenszel estimation, RR = 0.841, 95% CI 0.118-5.973, p > 0.05).

Conclusions

T1D seems to be not associated with VD intake and sun exposure in the Algerian Sahara region. Therefore, the consumption of VD in T1D patients from the Algerian Sahara would suspect that its association with the disease would be related to its synthesis alteration.
Mental disorders and adherence to antiretroviral treatment in health facilities in Mozambique
Authors: Mandlate Flavio M. , Greene M. Claire , Pereira Luis F. , Gouveia Maria Lidia , Mari Jair Jesus , Cournos Francine , Duarte Cristiane S. , Oquendo Maria A. , Feijó Mello Marcelo , Wainberg Milton L.
Published: July 2022

Introduction

Less adherence to antiretroviral treatment (ART) has been found among people suffering from HIV (PWH) with comorbid mental disorders like depression and alcohol use in Mozambique, a Sub-Saharan African country. However, less is explored with regards to other mental disorders.

Methods

This study assessed the association of multiple mental disorders and adherence to ART based on the data from primary/tertiary health care facilities in Maputo and Nampula, Mozambique. We administered a sociodemographic questionnaire, Mini International Neuropsychiatric Interview (MINI) Plus 4.0.0 adapted for use in Mozambique to assess mental conditions, and a 3-item self-report to measure ART adherence.

Results

Out of 1469 participants, 409 were HIV positive (self-report), with an average age of 36.7 years (SD=9.8), and 30.4% were male. The most common mental disorders were major depressive disorder (27.34%) followed by psychosis (22.03%), suicidal ideation/behavior (15.44%), and alcohol-use disorder (8.35%). Higher levels of non-adherence to ART [(Mean Difference=1.19, 95% CI: 1.04, 1.37)] and the likelihood of missing at least one dose in the last 30 days (OR=3.06, 95% CI: 2.00, 4.67) were found in participants with any mental disorder compared to those without a mental disorder. The highest levels of non-adherence were observed among those with drug use disorders and panic disorder.

Conclusions

In Mozambique, PWH with any co-occurring mental conditions had a lower probability of ART adherence. Integrating comprehensive mental health assessment and treatment and ART adherence interventions tailored to PWH with co-occurring mental disorders is necessary to attain optimal ART adherence and reach the UNAIDS ART target.
A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq
Authors: Ye Wenbin , Lian Qiwei , Ye Congting , Wu Xiaohui
Published: July 2022

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA-seq data, and single-cell RNA-seq (scRNA-seq) data. Particularly, we examined several representative tools using RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques and provided our perspective on how computational methodologies might evolve in the future for non-3’ UTR, tissue-specific, cross-species, and single-cell pA prediction.
A transformer model for predicting cognitive impairment from sleep
Authors: Song Tzu-An , Malekzadeh Masoud , Saxena Richa , Purcell Shaun M. , Dutta Joyita
Published: July 2022

Sleep disturbances are known to be aggravated with normal aging. Additionally, sleep disruptions have a potentially bidirectional causal relationship with dementia due to neurodegenerative diseases like Alzheimer’s disease. Predictive techniques that can automatically detect cognitive impairment from an individual’s sleep data have broad clinical and biological significance. Here, we present a deep learning approach based on a transformer architecture to predict cognitive status from sleep electroencephalography (EEG) data. This work uses data fromN= 1, 502 subjects from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Our transformer model achieves 70.22% accuracy at the binary classification task for distinguishing cognitively normal and impaired subjects based on their sleep EEG. Our method outperforms traditional feature handcrafting, which has an overall accuracy of 57.61% for the same task. We use a sparse regression model to understand and interpret the information captured by each learned feature from our transformer model. To our knowledge, this is the first effort to use deep learning to predict cognitive impairment from sleep metrics.
Proximal dendritic localization of NALCN channels underlies tonic and burst firing in nigral dopaminergic neurons
Authors: Hahn Suyun , Um Ki Bum , Kim Hyun Jin , Park Myoung Kyu
Published: July 2022

In multipolar nigral dopamine (DA) neurons, the highly excitable proximal dendritic compartments (PDCs) and two Na+-permeable leak channels, TRPC3 and NALCN, play a key role in pacemaking. However, the causal link between them is unknown. Here we report that the proximal dendritic localization of NALCN underlies pacemaking and burst firing in DA neurons.

Our morphological analysis of nigral DA neurons reveals that TRPC3 is ubiquitously expressed in the whole somatodendritic compartment, but NALCN is localized within the PDCs. Blocking either TRPC3 or NALCN channels abolished pacemaking. However, only blocking NALCN, not TRPC3, degraded burst discharges. Furthermore, local glutamate uncaging readily induced burst discharges within the PDCs, compared with other parts of the neuron, and NALCN channel inhibition dissipated burst generation, indicating the importance of NALCN to the high excitability of PDCs. Therefore, we conclude that PDCs serve as a common base for tonic and burst firing in nigral DA neurons.
Structural basis for antibody-mediated neutralization of Lymphocytic choriomeningitis virus
Authors: Moon-Walker Alex , Zhang Zeli , Zyla Dawid S. , Buck Tierra K. , Li Haoyang , Avalos Ruben Diaz , Schendel Sharon L. , Hastie Kathryn M. , Crotty Shane , Saphire Erica Ollmann
Published: July 2022

The mammarenavirus Lymphocytic choriomeningitis virus (LCMV) is a globally distributed zoonotic pathogen that can be lethal in immunocompromised patients and cause severe birth defects if acquired during pregnancy. Despite the fundamental importance of LCMV for studying immunobiology, the structure of the trimeric surface glycoprotein, essential for entry, vaccine design and antibody neutralization, remains unknown. In this study, we present the cryoEM structure of the LCMV surface glycoprotein (GP) in its trimeric prefusion assembly both alone and in complex with a rationally engineered monoclonal neutralizing antibody termed 18.5C-M28 (M28). Additionally, we show that passive administration of M28 protects mice from LCMV clone 13 (LCMVcl13) challenge when administered as either a prophylactic or therapeutic. Our study illuminates not only the overall structural organization of LCMV GP and the mechanism for its inhibition by M28, but also presents a promising therapeutic candidate to prevent severe or fatal disease in individuals who are at risk of infection by a virus that poses a threat worldwide.

Highlights

Rationally-engineered antibody M28 neutralizes lymphocytic choriomeningitis virus in vitro.

First high-resolution cryoEM structure of the pre-fusion trimeric lymphocytic choriomeningitis virus glycoprotein alone and in complex with M28.

M28 neutralizes by bridging adjacent glycoprotein protomers and locking it in the pre-fusion state.

Prophylactic and therapeutic administration of M28 protects mice from chronic lymphocytic choriomeningitis virus infection.
Acoustic Correlates of the Syllabic Rhythm of Speech: Modulation Spectrum or Local Features of the Temporal Envelope
Authors: Zhang Yuran , Zou Jiajie , Ding Nai
Published: July 2022

The speech envelope is considered as a major acoustic correlate of the syllable rhythm since the peak frequency in the speech modulation spectrum matches the mean syllable rate. Nevertheless, it has not been quantified whether the peak modulation frequency can track the syllable rate of individual utterances and how much variance of the speech envelope can be explained by the syllable rhythm. Here, we address these problems by analyzing large speech corpora (>1000 hours of recording of multiple languages) using advanced sequence-to-sequence modeling. It is found that, only when averaged over minutes of speech recordings, the peak modulation frequency of speech reliably correlates with the syllable rate of a speaker. In contrast, the phase-locking between speech envelope and syllable onsets is robustly observed within a few seconds of recordings. Based on speaker-independent linear and nonlinear models, the timing of syllable onsets explains about 13% and 46% variance of the speech envelope, respectively. These results demonstrate that local temporal features in the speech envelope precisely encodes the syllable onsets but the modulation spectrum is not always dominated by the syllable rhythm.
Rethinking peer-to-peer communication: how different mediums and product types influence consumers’ language
Authors: Forouhandeh Behnam , Clarke Rodney J. , Reynolds Nina Louise
Published: July 2022

Purpose

The purpose of this paper is to demonstrate the utility of systemic functional linguistics (SFL) as an underlying model to examine the similarities/differences between spoken and written peer-to-peer (P2P) communication.

Design/methodology/approach

An embedded mixed methods experimental design with linguistically standardized experimental stimuli was used to expose the basic linguistic differences between P2P communications that can be attributed to communication medium (spoken/written) and product type (hedonic/utilitarian).

Findings

The findings show, empirically, that consumer’s spoken language is not linguistically equivalent to that of written language. This confirms that the capability of language to convey semantic meaning in spoken communication differs from written communication. This study extends the characteristics that differentiate hedonic from utilitarian products to include lexical density (i.e. hedonic) vs lexical sparsity (i.e. utilitarian).

Research limitations/implications

The findings of this study are not wholly relevant to other forms of consumer communication (e.g. viral marketing). This research used a few SFL resources.

Practical implications

This research shows that marketers should ideally apply a semantic approach to the analysis of communications, given that communication meaning can vary across channels. Marketers may also want to focus on specific feedback channels (e.g. review site vs telephone) depending on the depth of product’s details that need to be captured. This study also offers metrics that advertisers could use to classify media and to characterize consumer segments.

Originality/value

This research shows the relevance of SFL for understanding P2P communications and has potential applications to other marketing communications.
Developing a framework for building maintenance: a case study of Malaysia's low-cost housing via soft system methodology
Authors: Ebekozien Andrew , Samsurijan Mohamad Shaharudin , Aigbavboa Clinton , Awo-Osagie Andrew I.
Published: July 2022

Purpose

The cost of residential building maintenance can harmfully affect low-income earners' expenditure if not checked. A customised maintenance concept via a framework will ensure efficient and proper building maintenance operations. The outcome may keep the life cycle cost down. Studies concerning the low-cost housing (LCH) maintenance concept through a framework are scarce in Malaysia. Thus, the study aims to investigate the state of LCH and develop a framework to improve LCH maintenance practices in Malaysia.

Design/methodology/approach

The study adopted a soft system methodology (SSM) to comprehend Malaysia's LCH building maintenance practices. The SSM allowed an alternative approach to improve LCH maintenance practices via a developed framework. Virtual interviews were conducted with experts, and findings were presented. It was in line with the SSM seven steps.

Findings

The findings show that apart from the poor state of LCH maintenance, there is the absence of a framework to improve maintenance practices, especially in LCH across Malaysia's cities. The findings developed a framework that would reposition the joint management body and management corporation in collaboration with the proposed maintenance agency for better service delivery via substantive, technical and administrative aspects.

Research limitations/implications

This study's data collection is restricted to Pulau Pinang, Kuala Lumpur and Johor through a qualitative research design approach. Future research is needed to consider more extensive coverage and validate the developed framework from this study via a quantitative research design.

Practical implications

Apart from the conceptual model that was developed, the suggested framework can be employed by Malaysia's maintenance practitioners and policymakers as a guideline to improve LCH building maintenance practices across the cities.

Originality/value

This study examined Malaysia's LCH maintenance practices via SSM to identify the state of the houses, identify the information required and propose a suitable framework to improve Malaysia's LCH maintenance practices.
Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information
Authors: Zhao Yaping , Kong Xiangtianrui , Xu Xiaoyun , Xu Endong
Published: July 2022

Purpose

Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

Design/methodology/approach

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

Findings

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

Originality/value

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.
Innovating beyond firm boundaries: resource deployment control in open source software development
Authors: Schaarschmidt Mario
Published: July 2022

Purpose

In times of open and distributed innovation, many innovation activities that are important for firms' products and services take place beyond the boundaries of the firm and thus beyond firms' direct control. A prime example for this phenomenon is open source software (OSS) development, where multiple actors contribute to a public good, which is also integrated into company-owned software products. Despite the importance of aligning community work on the public good with own in-house development efforts, firms have limited options to directly control the OSS project or the project's outcome. This research reflects on resource deployment control, a control mode in which firms assign own developers to work for an OSS project to influence the OSS project, and tests hypotheses on individual developer levels.

Design/methodology/approach

This research tests the effect of perceived resource deployment control on opinion leadership by analyzing employed Linux kernel developers.

Findings

The findings show that developers who perceive being assigned to an OSS project to enact control also exhibit opinion leadership. This research also investigates boundary conditions such as the OSS business model a firm operates and the reputation developers assign to the developers' employer.

Originality/value

This research is the first that is devoted to resource deployment control, and the research closes with a discussion of implications for control theory and the management of innovation beyond firm boundaries.
The impact of the coronavirus pandemic on the market for childcare
Authors: Thomas Diana Weinert
Published: July 2022

Purpose

This paper assesses the short-term consequences of the COVID-19 pandemic on the market for childcare and speculates about potential long-term consequences of pandemic-related policy intervention.

Design/methodology/approach

The paper uses basic statistics and data to describe changes in the market for childcare.

Findings

Policy responses to the pandemic likely aggravated pre-existing trends in the market for childcare, drove up the cost of production at a time when demand was collapsed because of the pandemic, and ultimately resulted in systematic closures of childcare centers. These closures will be difficult to reverse due to the high cost of entry into the industry and overall low profitability.

Research limitations/implications

The study is just a preliminary investigation in its current form that points to future areas of research.

Originality/value

This paper summarizes the results of existing studies and draws some basic conclusions about the effects of policy intervention.
Fast fashion: business models and strategies for adapting to the COVID-19 crisis
Authors: Li Ling
Published: July 2022

Purpose

This study aims to develop and test a methodical approach to assessing the effectiveness of business models in fast fashion.

Design/methodology/approach

The approach allows one to identify the key features of companies' adaptation strategies during the COVID-19 pandemic. The paper analyzes the economic profitability of the fast-fashion industry and establishes business opportunity trends. The author identifies a set of general characteristics of business related to supply and demand before and during the pandemic crisis. The study relies on descriptive statistics and statistical processing methods, including regression analysis, arithmetic mean and the indicator of market economy dynamics.

Findings

The study found that the fast-fashion business model allows companies to adapt to the new market norms because of its dual strategy: demand-driven transparency and supply-driven sustainability, within the logic of consumption. The results indicate that focus on sustainability and transparent stakeholder engagement helps companies to remain relevant in the fashion industry.

Originality/value

The results of the study can be used by general managers of companies, marketers and administrative managers to select strategies and make decisions under the current and future fashion industry demands in markets at different developmental stages.
Audit fees, audit seasonality and audit quality in Nigeria: a mediation analysis
Authors: Ayoola Tajudeen John
Published: July 2022

Purpose

This study aims to examine the mediating role of audit seasonality on the association between audit fees and audit quality in Nigerian deposit money banks.

Design/methodology/approach

The sample comprises 14 banks with annual financial statements between 2008 and 2020. The modified Baron and Kenny’s (1986) causal mediation model by Iacobucci et al. (2007) through the use of bootstrapped partial least square structural equation modelling and Sobel’s (1986) z-test is adopted to achieve this study’s objective.

Findings

The results of the causal mediation analysis show evidence of a fully mediating role of c between audit fees and audit quality in the Nigerian banking industry.

Research limitations/implications

This study extends the body of knowledge by demonstrating how audit fees influence audit quality through audit seasonality as a mediator in line with the job demands-and resources and conservation of resources theories. Regulatory authorities should be wary of policies that will further increase the workload of already burdened personnel of audit firms as the uniform fiscal year-end of 31 December introduced in the Nigerian banking system has unintended consequences on audit fees and audit quality.

Originality/value

To the best of the author’s knowledge, this is one of the first studies to provide evidence on the indirect association between audit fees and audit quality.
Covid 19 vaccine order allocation: an optimization model with substitution
Authors: Sarigol Ilkan , Ozdemir Rifat Gurcan , Bayraktar Erkan
Published: July 2022

Purpose

This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.

Design/methodology/approach

The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.

Findings

Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.

Originality/value

This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.
Revisiting the terrorism–tourism nexus in Nigeria: the moderating role of military spending
Authors: Raifu Isiaka Akande , Afolabi Joshua Adeyemi , Oguntimehin Jr Olusegun Joseph
Published: July 2022

Purpose

Tourism development is critical for economic transformation, particularly in emerging economies. However, the growing spate of terrorism dissuades international tourists, reduces tourism receipts and ultimately hampers the tourism sector's performance. Thus, the government intervenes by altering its military spending to curtail terrorism. Against this backdrop, this study examines the moderating role of military spending in the terrorism–tourism nexus in Nigeria.

Design/methodology/approach

The study employs the dynamic ordinary least squares (DOLS) to investigate the moderating role of military spending in the terrorism–tourism nexus in Nigeria. The authors employ the data that cover the period 1995Q1–2019Q4.

Findings

The results reveal that terrorism has a catastrophic effect on tourism arrivals in Nigeria while military spending has a positive impact on tourism arrivals. The results further show the moderating role of military spending in the terrorism–tourism nexus is positive and statistically significant. However, the findings are subject to the measures of military spending, terrorism and tourism.

Practical implications

The practical implication of the findings is the need for deliberate and strategic budgeting for the Ministry of Defence to combat terrorism, which should not only focus on the procurement of arms and ammunition but also cover the welfare of the military personnel. Nigeria also needs to formulate and implement necessary tourism policies aimed at countering terrorism in a bid to create and maintain a positive image on the global tourist map.

Originality/value

Many studies, particularly in developing countries like Nigeria, had examined the effect of terrorism on tourism but none has examined the moderating role of military spending in the terrorism–tourism nexus. Hence, this study examines the moderating role of military spending in the relationship between terrorism and tourism in Nigeria, a terrorism-prone country with several tourist sites.
What literature has to say about commitment to change: philosophy of commitment to change
Authors: Kayani Muhammad Bilal , Ali Maryam , Javed Hassan
Published: July 2022

Purpose

This paper aims to conclude and provide a review of the current literature on a commitment to change (C2C). Detailed research is provided on the operationalization and conceptualization of C2C.

Design/methodology/approach

The review is conducted based on 55 research papers published in different journals at different times in the past on C2C.

Findings

A complete framework from previous to latest literature has been depicted in the current paper on a C2C with their citation in the one study. Gaps that had not been addressed by previous authors are also highlighted to facilitate future researchers in regards to C2C. Especially in the context of quantitative studies gaps are highlighted in previous literature of C2C.

Practical implications

Future researchers can study the highlighted gaps with regards to C2C. Further it provides researchers a brief summary of 55 researches on C2C.
“Yes, I know you”: the role of source familiarity in the relationship between service adaptive behavior and customer satisfaction
Authors: Shen Yung-Cheng , Lin Heng-Yu , Chou Cindy Yunhsin , Wu Po Han , Yang Wei-Hao
Published: July 2022

Purpose

This study investigates the role of source familiarity in moderating the effect of service adaptive behavior (SAB) on customer satisfaction. Applying the accessibility–diagnosticity framework and situated cognition theory as the theoretical basis, this research hypothesizes that when customers are familiar with the source that provides the service (i.e. brand familiarity for Study 1 and personal familiarity for Study 2), customer satisfaction responses to SAB would be more moderate than when customers are not familiar with the source. Two studies were conducted to test the hypotheses.

Design/methodology/approach

Two experiments manipulating SAB and the brand name familiarity (Study 1) and personal familiarity with the service staff (Study 2) as the source familiarity were conducted. Customer satisfaction as a function of source familiarity was measured to test the hypothesis that source familiarity moderates the relationship between SAB and customer satisfaction.

Findings

Compared to unfamiliar sources, familiar sources generated a more moderate response in customer satisfaction as a function of SAB. High familiarity with the brand and service staff induced top-down, memory-based processing that overrides external stimuli as the basis of satisfaction judgment; bottom-up, stimulus-based processing relying on SAB for judgment kicked in only when the source familiarity is low.

Practical implications

From a practical point of view, this study indicates the importance of SAB, especially for brands with low awareness, and alludes to the comparative importance of relationship building in service delivery processes.

Originality/value

This study contributes to the literature by validating the role of contextual factors in influencing the impact of SAB on customer satisfaction.
Workload balance-based dynamic two-sided matching decision-making approach for cloud manufacturing tasks and services under uncertain preferences
Authors: Zhang Xuejiao , Yang Yu , Wang Jing
Published: July 2022

Purpose

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

Design/methodology/approach

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Findings

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

Originality/value

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.
A two level ensemble classification approach to forecast bitcoin prices
Authors: Kundra Harish , Sharma Sudhir , Nancy P. , Kalyani Dasari
Published: July 2022

Purpose

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

Design/methodology/approach

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

Findings

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

Originality/value

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.
Social capital and corporate entrepreneurship: the role of absorptive capacity in emerging markets
Authors: Fayad Yasmin , El Ebrashi Raghda
Published: July 2022

Purpose

This paper studies the role of inter-firm supply chain social capital and intra-firm social capital in enabling Corporate Entrepreneurship (CE), and also investigates the moderating and mediating effect of absorptive capacity.

Design/methodology/approach

A correlational descriptive survey research is employed for 200 firms; adopting a 13-item Likert scale obtained from Wang and Li (2016) for measuring inter-firm social capital, and an eight item Likert scale for measuring absorptive capacity. This is in addition to a nine item Likert scale obtained from De Clercqet al.(2013) to measure the intra-firm social capital, and a nine item semantic differential scale developed by Covin and Slevin (1989) for measuring the level of corporate entrepreneurship. Statistical analysis packages SPSS V.24 and AMOS V.24 were used.

Findings

Results provide evidence that structural supply chain social capital has an effect on corporate entrepreneurship; mediated by potential absorptive capacity. Additionally, the effect of relational supply chain social capital on corporate entrepreneurship is fully mediated by potential absorptive capacity. Furthermore, the effect of cognitive supply chain social capital on corporate entrepreneurship is fully mediated by potential absorptive capacity. On the other hand, results show that both intra-firm social capital and realized absorptive capacity moderate the relationship between potential absorptive capacity and corporate entrepreneurship.

Research limitations/implications

The convenience sampling technique increases the probability of selection bias. In addition, the research focused on two aspects of intra-firm social capital, namely relational and cognitive dimensions, and overlooked the structural dimension of social capital.

Practical implications

Providing managers with insights about the critical role of developing social capital among supply chain partners to facilitate the transfer and exchange of crucial knowledge necessary for product development and innovation. This is in addition to the need to capitalize on intra-collaborations and cross-functional routines to facilitate CE.

Originality/value

This study provides a required extension to the previous literature, which has not empirically modeled the role of potential absorptive capacity as means by which supply chain social capital dimensions enable CE. Also, the research identifies contingency factors that enable the effect of potential absorptive capacity on CE; namely intra-firm social capital and realized absorptive capacity.
Women's spaces in the vernacular Qatari courtyard house: how privacy and gendered spatial segregation shape architectural identity
Authors: AL-Mohannadi Asmaa , Furlan Raffaello , Grosvald Michael
Published: July 2022

Purpose

This research study aims to explore sociocultural factors such as privacy and gender segregation as they relate to the spatial perception of women's spaces within the vernacular Qatari household, and the activities, habits and practiced culture of its inhabitants.

Design/methodology/approach

This analysis utilizes the Space Syntax methodology to examine a sample of vernacular courtyard houses with respect to space connectivity, conducting an analytical exploration of the role of women based on the system of activities.

Findings

The findings shed light on the spatial complexity and sensitivity of the Qatari household as it reflects the prevailing sociocultural imperatives in Qatari urbanities, including the country's capital, Doha.

Research limitations/implications

This research study thus reinforces the importance of traditional residential architecture in conserving Qatar's distinctive architectural identity, rooted in its sociocultural attributes.

Social implications

Sociocultural features such as privacy and gender segregation are among the most prominent factors influencing the spatial form of vernacular courtyard houses in the Arabian Gulf country of Qatar.

Originality/value

It is argued that within a typical vernacular Qatari household, women's spaces are the least connected (most segregated) areas, due mainly to the influence of privacy norms and gender segregation on the spatial configuration of the house.