Understanding VR/AR in marketing & sales for B2B: an explorative study

Abstract. The research explored the impact of immersive reality technologies, particularly VR and AR, in marketing and sales for the B2B sector. Study interests were fuelled by both an industrial hype and vehement investments on these technologies, especially in the last five years. However, the potential of these technologies is still unexplored and widely misunderstood by businesses as the innovations are slowly taking off. Additionally, existing literature showed a need to clearly define various simulated realities in the continuum, including VR and AR, as well as a general misunderstanding of the potential of immersive reality technologies, and a shortage of studies covering holistically different VR/AR applications in marketing, especially for the B2B sector. Therefore, this research aims to bridge the gap between managerial and academic’ understanding by providing a holistic framework explaining the impact of immersive reality technologies in B2B marketing and sales and provide a clear distinction between VR and AR in the Virtuality-reality continuum. This research also aims to assist marketers and managers in embracing these technologies to better meet the needs of future generations. The study adopted a qualitative exploratory approach allowing researchers to gain an in-depth understanding of the topic from an industrial perspective. The study used an abductive thematic analysis approach to analyse empirical results and ten semi-structured interviews with eleven VR/AR providers for primary data collection. Results were structured based on four main themes, namely: VR and AR definitions, customer technology adoption factors, VR/AR impact and applications on B2B marketing, and last, VR/AR impact on sales performance outcomes. This study contributes to the existing literature by proposing a tentative definition for each terminology “VR” and “AR” separately that merges academic perspectives and industry insights. Overall, empirical study suggests that immersive reality technologies can affect both marketing activities and sales performance outcomes for the B2B sector. However, successfully embracing these technologies calls for collaboration to overcome financial, technical and social barriers while also enhancing aspects like the user experience to step out of the still immature VR/AR market. VR and AR together have an impact on marketing for B2B by enhancing customization, non-verbal communication, learning and experiential marketing while also creating memorable experiences that stick in the minds of the consumer. Concerning the customer’s purchasing journey, this study extends existing literature to cover all customer purchasing stages, including the pre-purchase, purchase and post-purchase. Results emphasize the pre-purchase phase as the most impacted by immersive reality technologies, followed by post-purchase and purchase stages, respectively. Finally, this study suggests that the use of VR/AR as sales support tools can yield positive efficiency returns through higher task performance and a reduction in sales-related costs, and positive effectiveness returns through greater customer and social engagement, stronger collaborative business relationships and the enhancement of proactive (sales planning) and reactive (adaptive selling) behaviours in the sales process

CYTOMEGALOVIRUS IgG AND IgM ANTIBODIES AMONG SUDANESE PATIENTS WITH ACUTE MYELOID LEUKEMIA: RELATION TO HEMATOLOGICAL PROGNOSTIC MARKERS

Background: Viral oncogenesis has remained an area of interest in cancer biology. Viruses have been great tutors of cancer biology, helping researchers to uncouple many signaling pathways and identifying critical therapeutic targets. Aim: The aim of this study was to assess the incidence of cytomegalovirus (CMV) infection and its impact on hematological prognostic markers of Acute Myeloid Leukemia (AML) among Sudanese populations. Method: The seroprevlance of CMV infection in AML patients was assessed in 100AML and 100 age and gender-matched controls. The associations of total white cell count and absolute blast count with the seroprevlance were examined. Results: The prevalence of CMV infection was 81% in patients and 17% in control subjects. Total white cell count and blast count were higher in AML CMV positive patients than AML CMV negative patients. Conclusion: Our findings indicate a high incidence of CMV infections in AML and its worse association with hematological markers could emphasize the role of CMV in the progression of AML. KEYWORDS: Acute myeloid leukemia; Cytomegalovirus

CYTOMEGALOVIRUS IgG AND IgM ANTIBODIES AMONG SUDANESE PATIENTS WITH ACUTE MYELOID LEUKEMIA: RELATION TO HEMATOLOGICAL PROGNOSTIC MARKERS

Background: Viral oncogenesis has remained an area of interest in cancer biology. Viruses have been great tutors of cancer biology, helping researchers to uncouple many signaling pathways and identifying critical therapeutic targets. Aim: The aim of this study was to assess the incidence of cytomegalovirus (CMV) infection and its impact on hematological prognostic markers of Acute Myeloid Leukemia (AML) among Sudanese populations. Method: The seroprevlance of CMV infection in AML patients was assessed in 100AML and 100 age and gender-matched controls. The associations of total white cell count and absolute blast count with the seroprevlance were examined. Results: The prevalence of CMV infection was 81% in patients and 17% in control subjects. Total white cell count and blast count were higher in AML CMV positive patients than AML CMV negative patients. Conclusion: Our findings indicate a high incidence of CMV infections in AML and its worse association with hematological markers could emphasize the role of CMV in the progression of AML. KEYWORDS: Acute myeloid leukemia; Cytomegalovirus

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Peer reviewe

PREVALENCE OF PARASITIC CONTAMINATION OF LEAFY GREEN VEGETABLES IN MISURATA, LIBYA

This study was undertaken to determine the prevalence of parasitic contamination in leafy green (lettuce and rocket) vegetables, samples of which were taken up from different regions of Misurata, Libya. A total of 112 raw vegetable samples randomly selected from farms and markets were subsequently examined by a concentration method and then assayed by light microscopy. It was found that 56.3% of the green vegetables were contaminated with different intestinal parasites, the parasites included cysts of Giardia spp., Enatmeaba histolytic a, Entameaba coli , Coccidia spp. oocysts, Balantidium coli and eggs of Hymenolepis nana., Ascaris lumbricoides., Toxocara spp., Strongyloides spp., Trichius trichura and Trichostronylus spp. The highest rate of contamination was detected in rocket (64.3%) while contamination was lower in green lettuce (48.2%). Toxocara spp. eggs were the highest prevalent parasite detected in green vegetables (27%) with the highest score density found in the rocket. Toxocara was followed by Entameaba coli cysts (24%), Coccidia spp. Oocysts (22%), Enatmeaba histolytica cysts (19%), Giardia spp. cysts (10%), and Hymenolepis nana eggs (8%). There were lesser rates of contamination from the parasites Strongyloides spp., Trichius trichura and Trichostronylus spp. There was no significant difference between single and mixed contamination of rocket and lettuce P>0.05. However, there was a statistical difference between protozoa and helminths contamination of rocket and lettuce (P≤0.01). We conclude these findings may have important implications for global food safety and confirm that green vegetables are a point of transmission of intestinal parasites to humans and so are a threat to public health in Misurata, Libya

Co-Expression Effect of SLC7A5/SLC3A2 to Predict Response to Endocrine Therapy in Oestrogen-Receptor-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor positive (ER+) and are subject to endocrine therapy however, an unpredictable subgroup of patients will develop resistance to endocrine therapy. SLC7A5/SLC3A2 complex is a major route for the transport of large neutral essential amino acids through the plasma membrane. Alterations in the expression and function of those amino acid transporters lead to metabolic reprogramming, which contributing to the tumorigenesis and drug resistance. This study aims to assess the effects and roles of SLC7A5/SLC3A2 co-expression in predicting response to endocrine therapy in patients with ER+ breast cancer. The biological and clinical impact of SLC7A5/SLC3A2 co-expression was assessed in large annotated cohorts of ER+/HER2- breast cancer with long-term follow-up at the mRNA and protein levels. In vitro experiments were conducted to investigate the effect of SLC7A5/SLC3A2 knockdown in the proliferation of cancer cells and to the sensitivity to tamoxifen. We found that proliferation-related genes are highly expressed in subgroup of patients with high SLC7A5/SLC3A2, and knockdown of SLC7A5/SLC3A2 decreased proliferation of ER+ breast cancer cells. In patients treated with endocrine therapy, high SLC7A5/SLC3A2 co-expression was associated with poor patient outcome, and depletion of SLC7A5/SLC3A2 using siRNA increased the sensitivity of breast cancer cells to tamoxifen. On the basis of our findings, SLC7A5/SLC3A2 co-expression has the potential of identifying a subgroup of ER+/HER2- breast cancer patients who fail to benefit from endocrine therapy and could guide the choice of other alternative therapy

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy

Sustainable lightweight foamed concrete using hemp fibre for mechanical properties improvement

Fibres have long been used as an additive in the fabrication of building elements and materials. A combination of natural and synthetic fibres has shown promise in preliminary research and testing, with the added benefit of greatly improved strengths of the composites. Compared to traditional reinforcement bars, natural fibre reinforcement's ratio of fibre required is significantly lower, making it more beneficial in terms of energy and economic values. Recent research has focused on the feasibility of using both natural and synthetic fibres as reinforcement in concrete and other construction materials. Thus, the purpose of this research is to investigate the feasibility of using hemp fibre at various percentages (0%, 0.2%, 0.4%, 0.6%, and 0.8%) as an additive in lightweight foamed concrete to enhance mechanical properties. Three LFC densities namely 500, 900 and 1300 kg/m3 were fabricated and tested. Axial compressive strength, flexural strength, splitting tensile strength, and ultrasonic pulse velocity were the four mechanical parameters that were assessed. The findings demonstrated that adding 0.4-0.6% of HF to LFC produced the best results for ultrasonic pulse velocity, compressive strength, flexural strength, and splitting tensile strength. The HF is essential in assisting to stop the spread of cracks in the plastic state of the cement matrix after the load was applied