Alternative Models Research Articles

RECONSTRUCTION OF EMPOWERMENT-BASED MARKETING PERFORMANCE RECOVERY FOR MICRO AND SMALL BUSINESSES IN INDONESIA

The Covid-19 pandemic has made most economic sectors, especially micro and small enterprises, stagnant and even bankrupt. This situation is what makes micro and small business enterprises change their sales strategy through digitalization schemes. The Covid-19 pandemic has indirectly prompted new changes in Indonesia's business style. Social media and marketplaces can become a new concept to make it easier for micro and small business actors to get more comprehensive marketing access. This study aims to initiate an ideal digital marketing adoption model for micro and small enterprise to restore economic conditions to their original state. The research method is an exploratory qualitative approach using primary data obtained from observations and interviews. The sampling technique used purposive sampling with the criteria of the sources being micro and small business actors assisted by the Department of Cooperatives and SMEs of Semarang, Surakarta, Grobogan, Pemalang, Purworejo. The flow of research analysis is data collection, data reduction, data presentation, and concluding. The results of this study indicate two alternative solution models, including training and startup development and training and scaleup development. The urgency of this research allows the government to carry out single submission online program management to minimize errors and monitor program progress. This solution model is expected to be a role model based on ta'awun (mutual relations) for other provincial governments in empowering micro and small business actors to become more optimal based on appropriate technology.

Why Mature Galaxies Seem to Have Filled the Universe Shortly After the Big Bang — A New Cosmological Model, that Predicted the JWST Observations

Recent observations from the first dataset, provided by NASA's James Webb Space Telescope (JWST) of six massive galaxies, at a time in the early universe, seem to defy conventional cosmological models, as they appear to be as mature and developed as our own local group. Such unexpected discoveries JUSTIFY A RADICALLY NOVEL MODEL OF COSMOLOGY. TO QUOTE JOEL LEJA, ASSISTANT PROFESSOR OF ASTRONOMY AND ASTROPHYSICS AT PENN STATE ''IT TURNS OUT WE FOUND SOMETHING SO UNEXPECTED IT ACTUALLY CREATES PROBLEMS FOR SCIENCE. IT CALLS THE WHOLE PICTURE OF EARLY GALAXY FORMATION INTO QUESTION''. This article provides an alternative mathematical model of cosmological redshifting (\(z\)), which actually predicted such mature galaxies in a 2022 preprint, prior to these recent observations. As well, this model also predicts discrepancies between theoretical and observed galaxy rotation curves with apparent increased energy density. The Azimuthal Projection Model of the Universe is conceptualized as an \(\mathbb{R}^5\) spacetime, with a four spatial dimensional hypersphere azimuthally projected onto a three-dimensional spatial sphere. This simple parsimonious model requires only a few assumptions, excluding dark energy to satisfy the Cosmological Constant \(\Lambda\), and is shown to match the Universal expansion rate, as established from supernova cosmology survey points. This novel model conceives the universe as a higher dimensional dynamic with spacetime as a projection, rather than as an arrow from the absolute beginning of the Big Bang. Redshifting is alternatively proposed as azimuthal angular projections of wavelengths \(\lambda\). Accelerated Universal Expansion is alternatively proposed as azimuthal projections of meridians, asymptotical to a horizon, and Lambert's cosine law of luminous intensity. A radical implication of this model is that azimuthal angular projections are positional dependent, and thus it's conceivable that apparent distances between galaxies vary with the location of the observer (see figure 4). Supportive mathematical evidence is described from the Hubble Tension; Discrepancies between visible spectra redshifting of cepheid variables (the most recent calculation is \(Ho=74.03 \pm 1.42km/sec/Mpc\)), and from temperature fluctuations in the Cosmic Microwave Background (CMB) (which are calculated to be \(Ho=68.7 \pm 1.3km/sec/Mpc\)), which resolves the discrepancy by recalibrating redshift data from supernova Cosmology survey points.

Bereaved Respondent Perceptions of Quality of Care by Inpatient Palliative Care Utilization in the Last Month of Life.

Under traditional Medicare, accountability measures are specific to each healthcare setting. With the growth of alternative payment models such as Medicare Advantage, the focus of accountability measures can be on the longitudinal episode of care. Using the last month of life as the episode of care, examine bereaved family member perceptions of the quality of care by site of death and inpatient palliative/hospice care. Retrospective cohort study using the National Health Aging Trends Study waves 3-11. US decedents age 65 and older with family member or close friend survey response. Overall rating of the quality of care, perceptions of symptom management, being treated with respect, emotional/spiritual support, communication, and receipt of care that the decedent did not want. Among 2796 interviews (weighted N = 12.6 million), 25.7% died at home with hospice, 10.9% at home without hospice, 10.0% in the ICU, 6.4% at a palliative care unit (PCU), 6.4% at a hospice IPU, 9.1% at hospital without inpatient palliative care, 13.2% at a nursing home without hospice, 9.8% in a nursing home with hospice, 4.1% at a hospice residence, and 4.4% at other locations without hospice. Dying at home with hospice received the highest rating of quality of care (60.2% stated excellent care) while the adjusted marginal differences in sites of death with inpatient palliative care services were rated lower: hospice residence 25.6% points lower (95% CI (-13.7%, -37.5%)) and a freestanding IPU was 16.9% points lower (95% CI (- 4.9%, -29.0%)). Examining the episode of care as the last month of life, hospice at home is associated with higher rating of the quality of care while inpatient palliative care services in hospital, hospice residence, or hospice IPU settings are rated lower.

Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi-model fusion stacking ensemble learning are superior to the single-model regression methods. Therefore, the stacking ensemble learning method that combines AdaBoost, RF, and XGBoost regression models is effective and feasible for assisting in the detection of berberine content in Rhizoma Coptidis.

Forecasting VaRs via hybrid EVT with normal and non-normal filters: A comparative analysis from the Chinese stock market

This paper studies the performance of hybrid methods combining normal and non-normal GARCH-type filters with extreme value theory (EVT) in predicting Value-at-Risk (VaR) for four major stock indices in the Chinese stock market. Based on the out-of-sample VaR forecasts results over the 24 models considered, we find that the hybrid EVT approaches illustrate substantial advantage over the corresponding GARCH-type models with parametric distributions, especially in the lower tail and extremal VaR evaluations (1% and 0.5% VaRs). Overall, the hybrid EVT methods with heavy-tailed filters (std-EVT, sstd-EVT) perform the best, followed by the hybrid EVT approaches with light-tailed filters (norm-EVT, snorm-EVT), and then the GARCH-type models with skewed student-t distribution. The conditional normal, skewed normal and student-t models are rejected and excluded from the Model Confidence Set (MCS) in predicting extremal VaRs for most cases, particularly for the lower tail. Additionally, we find that the results for alternative models in predicting VaRs vary sharply among alternative innovation specifications, but present trivial differences across the volatility models. Furthermore, our results based on the MCS tests and comparative backtests also indicate that the scoring function effectively discriminates predictive performance across methods for the lower tail, in contrast to showing less disparity for the upper tail. This emphasizes the importance of model selection in VaR forecasting performance for the lower tail. The above findings are robust to alternative threshold selections and the rolling-step chosen to update parameters. Similar analyses concerning the out-of-sample performance of the 24 models in predicting the Expected Shortfall (ES) show consistent results overall.

Analisis Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA

<p>Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R<sup>2</sup> or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R<sup>2</sup> is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.</p><p><strong>Keywords</strong><strong>: </strong>3 time series; kernel regression; GCV; MDKA stock price.</p>

Integrating place-based and virtual models of palliative care: A case report.

Palliative care management in rural and remote areas generally relies on local resources including a primary healthcare professional and a local health facility. Either in the absence of a primary healthcare professional, or in more complex cases, a patient is frequently transferred to a larger rural- or metropolitan-based health facility, away from community and local supports. With increasing challenges in locum availability, healthcare professionals are challenged in looking out for their own wellness needs, often having to choose between taking time-off or staying on duty and available to provide ongoing care for their patients. In this case report, we describe an alternative hybrid model of care which integrates place-based and virtual management, utilising digital health technologies. This approach supported a rural healthcare professional to take leave, by transferring the care and management of a palliative care patient to the WA Country Health Inpatient Telehealth Service, allowing the patient to remain in their local community and die in their place of choice. Access to palliative care is of great importance to quality care and its impacts cannot be overstated for the person with a terminal illness and their family, it is also important to take into consideration the role of the health professional and their well-being.

Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model

Given close ties between energies and economic growth and evidence in the literature that fundamental information helps improve the pricing efficiency of energy products, in this study we examine volatility prediction for the U.S. energy sector considering the impact of economic variables. In particular, we develop a hybrid model that combines the GARCH-MIDAS model and LSTM neural network. This particular specification is motivated by the need to simultaneously take a large number of economic predictors into account and allow a flexible volatility component structure with potential nonlinear relation among economic determinants. Based on the sample period from January 1991 to September 2022, our empirical results show that the hybrid model generates statistically more precise volatility forecasts out of sample than a number of alternative models, and this is robust during the energy market turmoil brought by the onset of the COVID-19 pandemic and the Russian–Ukrainian clash. Finally, volatility forecasts from the hybrid model allow mean–variance utility investors to achieve higher economic value.

Is it the end of the technology acceptance model in the era of generative artificial intelligence?

Purpose The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research. Design/methodology/approach This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research. Findings TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures. Research limitations/implications To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry. Originality/value This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.

Stream-disk shocks as the origins of peak light in tidal disruption events.

Tidal disruption events (TDEs) occur when stars are ripped apart1,2 by massive black holes and result in highly luminous, multi-wavelength flares3-5. Optical-ultraviolet observations5-7 of TDEs contradict simple models of TDE emission2,8, but the debate between alternative models (for example, shock power9,10 or reprocessed accretion power11-16) remains unsettled, as the dynamic range of the problem has so far prevented ab initio hydrodynamical simulations17. Consequently, past simulations have resorted to unrealistic parameter choices10,12,18-21, artificial mass injection schemes22,23 or very short run-times24. Here we present a three-dimensional radiation-hydrodynamic simulation of a TDE flare from disruption to peak emission, with typical astrophysical parameters. At early times, shocks near pericentre power the light curve and a previously unknown source of X-ray emission, but circularization and outflows are inefficient. Near peak light, stream-disk shocks efficiently circularize returning debris, power stronger outflows and reproduce observed peak optical-ultraviolet luminosities25,26. Peak emission in this simulation is shock-powered, but upper limits on accretion power become competitive near peak light as circularization runs away. This simulation shows how deterministic predictions of TDE light curves and spectra can be calculated using moving-mesh hydrodynamics algorithms.

Standardized Pre-clinical Surgical Animal Model Protocol to Investigate the Cellular and Molecular Mechanisms of Ischemic Flap Healing

BackgroundSome of the most complex surgical interventions to treat trauma and cancer include the use of locoregional pedicled and free autologous tissue transfer flaps. While the techniques used for these reconstructive surgery procedures have improved over time, flap complications and even failure remain a significant clinical challenge. Animal models are useful in studying the pathophysiology of ischemic flaps, but when repeatability is a primary focus of a study, conventional in-vivo designs, where one randomized subset of animals serves as a treatment group while a second subset serves as a control, are at a disadvantage instigated by greater subject-to-subject variability. Our goal was to provide a step-by-step methodological protocol for creating an alternative standardized, more economical, and transferable pre-clinical animal research model of excisional full-thickness wound healing following a simulated autologous tissue transfer which includes the primary ischemia, reperfusion, and secondary ischemia events with the latter mimicking flap salvage procedure.ResultsUnlike in the most frequently used classical unilateral McFarlane’s caudally based dorsal random pattern skin flap model, in the herein described bilateral epigastric fasciocutaneous advancement flap (BEFAF) model, one flap heals under normal and a contralateral flap—under perturbed conditions or both flaps heal under conditions that vary by one within-subjects factor. We discuss the advantages and limitations of the proposed experimental approach and, as a part of model validation, provide the examples of its use in laboratory rat (Rattus norvegicus) axial pattern flap healing studies. ConclusionsThis technically challenging but feasible reconstructive surgery model eliminates inter-subject variability, while concomitantly minimizing the number of animals needed to achieve adequate statistical power. BEFAFs may be used to investigate the spatiotemporal cellular and molecular responses to complex tissue injury, interventions simulating clinically relevant flap complications (e.g., vascular thrombosis) as well as prophylactic, therapeutic or surgical treatment (e.g., flap delay) strategies in the presence or absence of confounding risk factors (e.g., substance abuse, irradiation, diabetes) or favorable wound-healing promoting activities (e.g., exercise). Detailed visual instructions in BEFAF protocol may serve as an aid for teaching medical or academic researchers basic vascular microsurgery techniques that focus on precision, tremor management and magnification.Graphical

The Care Needs of Patients With Cognitive Impairment in Late-Stage Parkinson's Disease.

Cognitive impairment is common in Parkinson's disease (PD), but care needs and resource use for those with significant cognitive impairment are not well established. 675 participants with PD from the international Care of Late-Stage Parkinsonism (CLaSP) study were grouped into those without (n = 333, 49%) and with cognitive impairment (MMSE < 24/30 or diagnosis of dementia or Mild Cognitive Impairment; n = 342, 51%) and their clinical features, care needs and healthcare utilisation compared. The relationship between cognition and healthcare consultations was investigated through logistic regression. Cognitive impairment was associated with more motor and non-motor symptoms, less antiparkinsonian but higher rates of dementia and antipsychotic medication, worse subjective health status and greater caregiver burden. A considerable proportion did not have a pre-established cognitive diagnosis. Care needs were high across the whole sample but higher in the cognitive impairment group. Home care and care home use was higher in the cognitive impairment group. However, use of healthcare consultations was similar between the groups and significantly fewer participants with cognitive impairment had had recent PD Nurse consultations. Worse cognitive impairment was associated with lower frequency of recent PD nurse and multidisciplinary therapy consultation (physiotherapy, massage, occupational therapy, speech training and general nursing). Those with cognitive impairment have more severe PD, higher care needs and greater social care utilisation than those with normal cognition, yet use of health care services is similar or less. Cognitive impairment appears to be a barrier to PD nurse and multidisciplinary therapy consultations. This challenges current models of care: alternative models of care may be required to serve this population. Parkinson's disease is a long-term progressive health condition. Over time, many people with Parkinson's develop problems with thinking and memory, called cognitive impairment. This can negatively impact the daily lives of the person with Parkinson's and their caregiver. It is also thought to be a barrier to accessing healthcare. How people with Parkinson's who have cognitive impairment use healthcare and detail of their care needs is not well known.We analysed data from a large sample of people with advanced Parkinson's from six European countries to investigate their symptoms, care needs and healthcare use. We compared those with cognitive impairment to (342 people) to those without cognitive impairment (333 people).We found that those with cognitive impairment had more severe Parkinson's across a range of symptoms compared to those without cognitive impairment. They also had more care needs, reported their health status to be worse, and their caregivers experienced greater strain from caring. Whilst use of other healthcare services was similar between the two groups, those with cognitive impairment were less likely to have recently seen a Parkinson's nurse than those without cognitive impairment. Further analysis showed an association between cognitive impairment and not having seen a Parkinson's nurse or therapist recently, taking psychiatric symptoms, functional disability and care home residence into account. Therapists included were physiotherapy, massage, occupational therapy, speech training and general nursing. These findings highlight unmet need. We suggest that healthcare should be more targeted to help this group of people, given their higher care needs.

Linkage vector autoregressive model

AbstractTo accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical data, where unknown parameters of the function can be estimated from the data. We propose a simulation technique to check the stationarity condition of the LAR model and suggest a Bayesian Markov chain Monte Carlo method for estimation. Empirical results for the quarterly growth rates of gross domestic product (GDP) in 45 countries indicate that (i) the autoregressive (AR) coefficient for the aggregated growth rate is hard to distinguish from zero; (ii) a panel AR model with individual effects has a positive autocorrelation; and (iii) the alternative LAR models are preferred to the panel AR model. Depending on the specification of the linkage variables, the sign and size of the linkage effect can differ. The logistic linkage function has a flexible structure to accommodate the size of the linkage of individual GDP growth rates, and it strengthens the dynamics.

Grey Duane model for reliability growth prediction under small sample uncertain failure data

Traditional reliability growth models require failure data to adhere to a specific distribution, which greatly limits the applicability in data-scarce scenarios. Drawing from the characteristic in modeling small sample and poor information of grey system theory, this study introduces a novel grey Duane reliability growth prediction model, tailored for analyzing uncertainty in limited failure data. The characteristics and adaptability of the proposed grey Duane model (GDM) are analyzed and compared with two traditional reliability growth prediction models. Utilizing a first-order accumulation generation operation, a differential function is established to estimate the unknown parameters in GDM. The proposed GDM enables synchronized predictions of failure time, failure number, and instantaneous mean time between failures. To validate the applicability of GDM in reliability growth management, an industrial case in particular electronic equipment during an aircraft’s flight-testing process is applied, whose results demonstrate its superior predictive accuracy compared to alternative models.

Exchange rate and housing affordability in OECD countries

Purpose This study aims to construct alternative models to establish the dynamic relationship between exchange rates and housing affordability by estimating both the short- and long-run relationship between exchange rates and housing affordability for 18 OECD countries from 1975Q1 to 2022Q4. After that, this study demonstrates how this nexus behaves during high and low inflation regimes and turbulent times. Design/methodology/approach This study uses the panel autoregressive distributed lag technique to examine the nexus between housing affordability to capture the distinct characteristics of the sample countries and estimate various short- and long-run dynamics in the relationship between housing affordability and exchange rate. Findings Exchange rate appreciation improves housing affordability in the short run, whereas this connection tends to dissipate in the long run. Moreover, inflation can worsen housing affordability during turbulent times, such as the global financial crisis, in both the short and long run. Ignoring these changes in the relationship between exchange rates and housing affordability during turbulent times can lead to incorrect conclusions. Originality/value To the best of the authors’ knowledge, this study is the first to examine the association between exchange rates and housing affordability by demonstrating how these variables behave in high and low inflation regimes and turbulent times.

Conformational variants of the ternary complex of C5a, C5aR1, and G-protein

The complement component fragment 5a (C5a) binds and activates two complement receptors like C5aR1 and C5aR2, which play a significant role in orchestrating the proinflammatory function of C5a in tissues through the recruitment of heterotrimeric G-proteins and β-arrestins. Dysregulation of the complement induces excessive production of C5a, which triggers aberrant activation of the C5a-C5aR1-G-protein and C5a-C5aR2-β-arrestin signalling axes in tissues, contributing to the pathology of numerous immune-inflammatory diseases. Thus, understanding the interaction of C5a with C5aR1 and C5aR2, as well as the interaction of G-protein and β-arrestins, respectively, with C5a-C5aR1 and C5a-C5aR2, holds tremendous therapeutic value. In the absence of structural data, we have previously elaborated the binary complexes of C5a-C5aR1 and C5a-C5aR2, as well as the ternary complex of C5a-C5aR2-β-arrestin1, in highly refined model structures. While our ternary model complex of C5a-C5aR1-G-protein was in progress, two cryo-electron microscopy-based ternary structural complexes of C5aR1 were made available by others. However, it is observed that the interaction of the crucial NT-peptide of C5aR1 with C5a, including the portion of the G⍺i-subunit that harbors the switch-I region, is not fully resolved in both complexes. The current study addresses the issues and provides two highly refined alternative model ternary complexes of C5a-C5aR1-G-protein. The study highlights the conformational heterogeneity in C5aR1 by comparing the two conformational variants of the model ternary complex in the context of C5a-C5aR2-β-arrestin1 for further devising methods and molecules targeting both surface and intracellular C5aR1/C5aR2 for effectively mitigating the proinflammatory role of C5a in various disease settings. Communicated by Ramaswamy H. Sarma

The role of the IPCC in assessing actionable evidence for climate policymaking

Climate policymakers across the world seek inputs from the research community to determine appropriate policies to reduce greenhouse gas emissions. However, the reports of the Intergovernmental Panel on Climate Change (IPCC), which perform the largest available analytical exercise in this area, offer scarce analytics on climate policy design. Here, we explore how, despite its ‘neutral, policy-relevant but not policy-prescriptive’ principle, the IPCC’s analytical scenario process in Working Group III on Mitigation has adopted an implicitly prescriptive policy position in favour of carbon pricing. Drawing on the example of alternative climate-economic modelling using the E3ME-FTT framework, we explore a pathway for the IPCC process that could cater for diverse ranges of more realistic granular policies. We conclude that, to become truly policy-relevant, the IPCC’s climate mitigation work is in urgent need of reform to provide more effective support for policy design.

Bivariate Time Series Analysis of Nigeria Gross Domestics Product and Communication Sector

The need to establish a relation between Gross Domestic Product and Communication sector was the major focus of this research. This research work investigated the contribution of Communication sector to the Gross Domestic product of Nigeria Economy. With the aid of ACF and PACF, ARIMA (1 1 1) was suggested for both variables. Alternative multivariate time series models used for the analysis were ARIMAV, MARDL and MARDL-MA models. The research has established interaction and interdependence between the two macroeconomic variables, and has also revealed that each of the variable has contributed significantly to each other at first time lag. The error variances of the bivariate time series model were derived for GDP and Communication sector. When comparing the three models for the two economic variables, ARIMAV model for Gross Domestic Product has the least error variance of 0.2183 making it the best model, while MARDL model for communication sector produced the least error variance of 0.0723, thereby indicating that MARDL model outperformed ARIMAV and MARDL-MA models for communication sector. Hence, this research has brought to focus the fact that performance of a model over another is predicated upon the nature of the economic data. That means there is no fixed multivariate time series model for a given macroeconomic data due to the dynamic nature of the time series.

Distinct RBC alloantibody responses in type 1 interferon-dependent and -independent lupus mouse models.

During transfusion of red blood cells (RBCs), recipients are exposed to both ABO and non-ABO 'minor' antigens. RBC donor units and recipient RBCs are not routinely matched for non-ABO antigens. Thus, recipients are exposed to many RBC alloantigens that can lead to RBC alloantibody production and subsequent clinically significant hemolysis. RBC alloantibodies also significantly limit the provision of compatible RBC units for recipients. Prior studies indicate that the frequency of RBC alloimmunization is increased during inflammatory responses and in patients with autoimmune diseases. Still, mechanisms contributing to alloimmune responses in patients with autoimmunity are not well understood. More than half of adult patients with systemic lupus erythematosus (SLE) produce type 1 interferons (IFNα/β) and express IFNα/β stimulated genes (ISGs). Previously, we reported that IFNα/β promote RBC alloimmune responses in the pristane mouse model, which develops a lupus-like phenotype that is dependent on IFNα/β signaling. However, it is unclear whether IFNα/β or the lupus-like phenotype induces alloimmunization in lupus models. Therefore, we tested the hypothesis that IFNα/β promotes RBC alloimmune responses in lupus by examining alloimmune responses in IFNα/β-independent (MRL-lpr) and IFNα/β-dependent (pristane) lupus models. Whereas pristane treatment significantly induced interferon-stimulated genes (ISGs), MRL-lpr mice produced significantly lower levels that were comparable to levels in untreated WT mice. Transfusion of murine RBCs that express the KEL antigen led to anti-KEL IgG production by pristane-treated WT mice. However, MRL-lpr mice produced minimal levels of anti-KEL IgG. Treatment of MRL-lpr mice with recombinant IFNα significantly enhanced alloimmunization. Collectively, results indicate that a lupus-like phenotype in pre-clinical models is not sufficient to induce RBC alloantibody production, and IFNα/β gene signatures may be responsible for RBC alloimmune responses in lupus mouse models. If these findings are extended to alternate pre-clinical models and clinical studies, patients with SLE who express an IFNα/β gene signature may have an increased risk of developing RBC alloantibodies and may benefit from more personalized transfusion protocols.

Leading collaborative change amid a crisis: a framework to liberate a team’s leadership skills and promote sales performance

PurposeThis study aims to introduce an alternative model, “volatility, uncertainty, complexity and ambiguity (VUCA), Virtue and Vice” (3V’s), to unleash leadership skills, promote organisational collaborative change and impact sales performance during an unprecedented crisis.Design/methodology/approachThe methodology outlines action research based on the 3V’s model and its application in an international business-to-business sales organisation during Covid-19. It explores alternative paths informed by play-at-work and Plato’s philosophy applied to work-based-learning. Each action/iteration adds to the model, which becomes more likely appropriate for various situations.FindingsThe 3V’s boosted change implementation and improved sales performance. The 3V’s conceptualised an invitation to immerse oneself in the constant “river of change” (VUCA) and a means of understanding the role of leadership in navigating this change by embracing simple rules: searching for justice (Virtue) and overcoming the barrier of public opinion (Vice).Research limitations/implicationsThe 3V’s model is grounded in leadership literature and a sole application, providing real international data relevant to organisations and leaders. This has yet to be evaluated further.Practical implications3V’s can enhance the understanding of a leading collaborative change and re-frame team dynamics in post-pandemic times for the broader public.Social implicationsThe approach advocated is a practice of “swimming alongside the team”, which should enable empowerment and collaboration rather than a top-down direction. Focussing on leaders who are moral people, this approach becomes a differentiator in a digital world.Originality/valueThis study examines Plato’s philosophy, play-at-work and other leadership theories in a model which prepares organisations to respond to crisis by providing the ability to reflect on human aspects and straightforward, transferable skills.