Alt-Index: A proposed Index for measuring the social activity of scientific research

Altmetrics is a broad term used to refer to article level metrics, which focus on a more timely measurement of interest in scholarly documents made visible through social media (Priem et al., 2010). As with any new metric, concerns have been raised regarding its use (Prime, 2013; Kwok, 2013), due to the fact that an Altmetric score might potentially be manipulated or gamed, just as it is possible to game citations (Bartneck & Kokkelmans, 2011; Wilhite et al., 2012). However, given that there are so many diverse measures now compiled within Altmetrics, the tampering process is actually not that easy (Piwowar, 2013). Altmetric scores have therefore attracted the attention of the scientific community, who, parallel to traditional forms of scholarly communication, are now relying on social media to disseminate research as part of their daily practices (Piwowar, 2013). Thus far, a few studies have shown weak to medium correlations between bibliometric measures and Altmetric scores (Costas et al., 2015). More recently, a comprehensive study using data from Altmetic.com and Scopus has shown that when compared to journal citation scores, Altmetric scores demonstrate a higher-level of accuracy for identifying highly cited publications (Hassan et al., 2017). With this paper, we would like to propose a new measure, termed the alt-index. It is analogous to the h-index (Hirsch 2005), and it is defined as follows: "a scholar has an alt-index of a, if a of her/his Np papers have at least a social mentions, and the other (Np-a) papers have no more than a mentions each"

Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table

Geotechnical characteristics of effluent contaminated cohesive soils

In developing countries like Pakistan, raw industrial effluents are usually disposed-off directly into open lands or in water bodies resulting in soil contamination. Leachate formation due to rainfalls in openly dumped solid waste also adds to soil contamination. In this study, engineering behavior of soils contaminated by two industrial effluents, one from paper industry (acidic) and another from textile industry (basic), has been investigated. Laboratory testing revealed significant effects of effluent contamination on engineering behavior of tested soils. Liquid limit, plasticity index, optimum moisture content and compression index of tested soils were found to increase with effluent contaminant, indicating a deterioration in the engineering behavior of soils. Whereas maximum dry density, undrained shear strength and coefficient of consolidation of the contaminated soils showed a decreasing trend. The dilapidation in engineering characteristics of soils due to the addition of industrial effluents could pose serious threats to existing and future foundations in terms of loss of bearing capacity and increase in settlement. Keywords: soil contamination, industrial waste, engineering behavior, effluent waste, leachate. First published online: 28 Nov 201

Comparison of Cerebrospinal Fluid Leakage in Endoscopic Endonasal Transsphenoidal Surgery for Pituitary Adenoma with and without Sellar Floor Reconstruction

Objectives:  This study aimed to compare CSF leak in endoscopic endonasal TSS of pituitary adenoma with and without reconstruction of the sellar floor with no intraoperative CSF leakage. Materials and Methods:  It was a randomized controlled trial of 116 patients of both genders diagnosed case of pituitary adenoma who underwent endoscopic endonasal TSS over 1 year. The cases were randomized into 2 groups. In Group A endoscopic endonasal TSS and the sellar floor, reconstruction was done while in Group B only endoscopic endonasal transsphenoidal surgery was done without reconstruction. Results:  The patient’s mean age in group A was 40.7 ± 9.56 years, and in group, B was 41.9 ± 10.5 years. The gender distribution, for group A, males and females were 29 each (50%) and in group B, the males were 36 (62%) and females were 22 (38%). There were 52 (89.7%) cases of macroadenoma and 6 (10.3%) cases of microadenoma in each group. On the 1st postoperative day, CSF leakage was noted in 2 (3.4%) patients of group A, and CSF leakage was observed in 2 (3.4%) patients of group B. Results revealed no difference in CSF leakage between both groups. There were minor nasal complications in both groups. Conclusion:  There is an equal chance of success with endoscopic endonasal transsphenoidal surgery (TSS) of pituitary adenoma with and without reconstruction of the sellar floor, concerning post-operative CSF leak, in patients who have no intraoperative CSF leak which enlarges the pool of options for treatment

IEEE Access special section editorial: Mission critical public-safety communications: architectures, enabling technologies, and future applications

Disaster management organizations such as fire brigades, rescue teams, and emergency medical service providers have a high priority demand to communicate with each other and with the victims by using mission-critical voice and data communications [item 1) in the Appendix]. In recent years, public safety agencies and organizations have started planning to evolve their existing land mobile radio system (LMRS) with long-term evolution (LTE)-based public safety solutions which provides broadband, ubiquitous, and mission-critical voice and data services. LTE provides high bandwidth and low latency services to the customers using internet protocol-based LTE network. Since mission critical communication services have different demands and priorities for dynamically varying situations for disaster-hit areas, the architecture and the communication technologies of the existing LTE networks need to be upgraded with a system that has the capability to respond efficiently and in a timely manner during critical situations

Culture-Based Identification of Causative Organisms in Ascitic Fluids of Patients with Spontaneous Bacterial Peritonitis Secondary to Decompensated Liver Disease and their Sensitivities to Ceftriaxone as an Empiric Therapy

OBJECTIVES To identify the pathogens in the ascitic fluids of patients with spontaneous bacterial peritonitis and then to determine their sensitivity pattern to ceftriaxone. METHODOLOGY The cross-sectional study was conducted at the Medical Unit-A, Department of Medicine, Hayatabad Medical Complex, Peshawar, from November 2021 to April 2022. Before ceftriaxone treatment was started, a minimum of 10 ml of ascitic fluid was introduced into a blood culture vial. Only patients with a positive culture were registered, and their information was gathered using a proforma. For statistical analysis, SPSS version 23 was used. RESULTSA total of 96 patients were enrolled in our study. There were 62 (59.52%) male and 34 (40.48%) female patients. Based on the isolation and identification of bacteria, the most prevalent bacteria isolated was Escherichia coli in 36 (37.5%) patients, followed by Acinetobacter Spp in 13 (13.54%) patients, Streptococcus spp in 14 (14.58%), Enterococcus spp in 11 (11.45%), Staphylococcus aureus in 9 (9.39%), MRSA in 8(8.33%) and K. Pneumonia in  5(5.21%) patients. The overall sensitivity of ceftriaxone to gram-positive bacteria was observed in 12 (42.85%) isolates, whereas the overall sensitivity of ceftriaxone to gram-negative bacteria was observed in 25 (36.76%) isolates. (p=0.091) (Figure 6). CONCLUSION Our study concludes that gram-negative bacteria were more prevalent than gram-positive bacteria in ascitic fluids of patients with spontaneous bacterial peritonitis. The most common isolated pathogen was E.coli. Gram-negative was more resistant to ceftriaxone as compared to gram-positive bacteria

Important genes affecting fibre production in animals: A review

The realignment of the production profile to respond to demanding market signals is one of the most important challenges that an animal breeders face today. Animal fibre being a significant contributor to the agricultural economy needs special attention. This is especially true for sheep and goats where fibre production can account for as much as 20% of the total gross income. It is therefore necessary to gain a better insight into the genes governing wool traits. Gene mapping studies have identified some chromosomal regions influencing fibre quality and production. These may help in the selection of animals producing better quality wool. These are more efficient and accurate than the conventional techniques. This paper critically reviews various genes governing fibre growth in animals and their importance. Fibre quality and production genes may provide novel insights into our understanding of the science of genetics and breeding. The discovery of new fibre-related genes and their functions may also help in future studies related to fibre development and in the development of new and advanced techniques for the improvement of fibre production and quality

Relationship between risk perception and employee investment behavior

nvestment behavior of an investor depends on his/her risk perception and risk attitude. This paper attempts to explore that how the perception of an investor who is also the employee of that organization differs from other investors. Does he/she perceives risk similarly as other common investors or his relationship with the organization as an employee has any impact his/her risk perception, attitude and investment behavior. This research study is conceptual in nature and mainly based on previous literature findings and evidences. Findings of this study suggested that employees risk perception is directly related with investment behavior and there is strong relationship between them. This can help the management to make special offers of shares to employees, this will further strength the bond of employees with the organization

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic