Industry 4.0 or Pharma 4.0?

This chapter examines the convergence of Industry 4.0 and Pharma 4.0 in the context of healthcare supply chains. It investigates the potential applications of these industrial revolutions to enhance the flexibility, benefits, challenges, and opportunities of healthcare supply chains. This chapter highlights the application of state-of-the-art technology to create intelligent, adaptable, and personalized supply chain systems for the healthcare and pharmaceutical sectors. The literature on “Pharma Industry 4.0” is reviewed, with a focus on the opportunities for sustainable value creation and pharmaceutical supply chain research. Healthcare supply chain has some serious issues like counterfeit drugs, non-transparent supply chain, unfear track and trace system of medicines and biomedical instruments. The authors identified the potential solutions for these issues with the help of current innovative technologies and practices

CPU and RAM Energy-based SLA-aware Workload Consolidation Techniques for Clouds

Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as ‘‘pay as you go’’ provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server’s CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server’s capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA)

Frequency of Arrhythmias and Postural Orthostatic Tachycardia Syndrome in Patients With Marfan Syndrome: A Nationwide Inpatient Study.

Background Marfan syndrome (MFS) is an autosomal dominant connective tissue disorder affecting multiple systems, particularly the cardiovascular system. The leading causes of death in MFS are aortopathies and valvular disease. We wanted to identify the frequency of arrhythmia and postural orthostatic tachycardia syndrome, length of hospital stay, health care-associated costs (HAC), and in-hospital mortality in patients with MFS. Methods and Results The National Inpatient Sample database from 2005 to 2014 was queried using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for MFS and arrhythmias. Patients were classified into subgroups: supraventricular tachycardia, ventricular tachycardia (VT), atrial fibrillation, atrial flutter, and without any type of arrhythmia. Data about length of stay, HAC, and in-hospital mortality were also abstracted from National Inpatient Sample database. Adjusted HAC was calculated as multiplying HAC and cost-to-charge ratio; 12 079 MFS hospitalizations were identified; 1893 patients (15.7%) had an arrhythmia; and 4.9% of the patients had postural orthostatic tachycardia syndrome. Median values of length of stay and adjusted HAC in VT group were the highest among the groups (VT: 6 days, $18 975.8; supraventricular tachycardia: 4 days,$11 906.6; atrial flutter: 4 days, $11 274.5; atrial fibrillation: 5 days,$10431.4; without any type of arrhythmia: 4 days, $8336.6; both P=0.0001). VT group had highest in-patient mortality (VT: 5.3%, atrial fibrillation: 4.1%, without any type of arrhythmia: 2.1%, atrial flutter: 1.7%, supraventricular tachycardia: 0%; P<0.0001) even after adjustment for potential confounders (without any type of arrhythmia versus VT; odds ratio [95% CI]: 3.18 [1.62-6.24], P=0.001). Conclusions Arrhythmias and postural orthostatic tachycardia syndrome in MFS were high and associated with increased length of stay, HAC, and in-hospital mortality especially in patients with VT

Taking on a Community Solutions Process (Co-Solve) to the Pain and Opioid Epidemic: A Multi-disciplinary and Multi-institute Pain Panel and Community Response in Sacramento, California

America’s healthcare providers and patients are challenged by an overwhelming high prevalence of chronic pain and opioid misuse. Approximately 23.4 million adults suffer from daily pain and in 2014, nearly 61% of Americans who died from drug overdoses used an opioid analgesic. Unrecognized addiction, untreated psychiatric comorbidity, and lack of training/education for providers and patients are factors associated with chronic pain and opioid misuse. Communication strategies and structures are required to enhance collaboration between multidisciplinary providers and institutions. On September 28, 2017, an open panel discussion with pain specialists from three major academic and medical institutes in Sacramento, California initiated an integrative community solutions process to optimize pain education best practices and to protect public health. The attendees represented a wide range of healthcare disciplines. This commentary describes ideas derived from dialogue between community attendees and panelists, which considers both healthcare provider characteristics and patients’ cultural backgrounds. Providers of most disciplines underscored the need to share information and institute cross-disciplinary training on pain and behavioral health treatments. In conclusion, we outline an integrative community-based framework, namely the Community Solutions Process (Co-Solve), to help other communities to implement and derive their own action-oriented solutions unique to their population

Recent advances and perspectives on starch nanocomposites for packaging applications

Starch nanocomposites are popular and abundant materials in packaging sectors. The aim of this work is to review some of the most popular starch nanocomposite systems that have been used nowadays. Due to a wide range of applicable reinforcements, nanocomposite systems are investigated based on nanofiller type such as nanoclays, polysaccharides and carbonaceous nanofillers. Furthermore, the structures of starch and material preparation methods for their nanocomposites are also mentioned in this review. It is clearly presented that mechanical, thermal and barrier properties of plasticised starch can be improved with well-dispersed nanofillers in starch nanocomposites

ESTIMATION OF TOTAL FACTOR PRODUCTIVITY GROWTH IN AGRICULTURE SECTOR IN PUNJAB, PAKISTAN: 1970-2005

The genus Ziziphus (Jujube) with more than 100 species of deciduous or evergreen trees and shrubs distributed in the tropical and subtropical regions of the world offers sufficient plants genetic resources suitable for arid and semi arid climates to enhance food security. Some of the species, like Z. mauritiana occur in nearly every continent and is thought to possess great genetic diversity. The traditional selection and cultivation of Ziziphus varieties in China and India resulted in better known and more widely researched varieties than those in other regions. Several local and exotic ber varieties are cultivated for fruit production in Pakistan with the least research work regarding different aspects including morphological characterization of the available germplasm resources. In this study, existing gene pool was characterized for physical and morphological diversity to develop a reliable identification key which would lead to characterization, selection and approval of better germplasm for further cultivation. Eleven commercial varieties (Desi, Selection-13, Gola, Selection 11, Karnal Local, Gourh, Karela, Umran-9, Mirpuri, Khati Mithi, and Badam) and two unknown strains (Anonymous-1 and Anonymous-2) of ber were studied for qualitative and quantitative characters. The quantitative studies included leaf area, petiole length and fruit diameter, weight and volume while, qualitative studies comprised of leaf shape, apex, base, margins and characteristics of leaf dorsal and ventral surface. Fruits from the selected strains were also subjected to morphological studies including shape, type of stem-end and cavity, form of styler-end and skin appearance. Results showed great physico-morphological diversity suggesting division of all the 13 cultivars into 4 sections

Optimizing Large-Scale PV Systems with Machine Learning: A Neuro-Fuzzy MPPT Control for PSCs with Uncertainties

The article proposes a new approach to maximum power point tracking (MPPT) for photovoltaic (PV) systems operating under partial shading conditions (PSCs) that improves upon the limitations of traditional methods in identifying the global maximum power (GMP), resulting in reduced system efficiency. The proposed approach uses a two-stage MPPT method that employs machine learning (ML) and terminal sliding mode control (TSMC). In the first stage, a neuro fuzzy network (NFN) is used to improve the accuracy of the reference voltage generation for MPPT, while in the second stage, a TSMC is used to track the MPP voltage using a non-inverting DC—DC buck-boost converter. The proposed method has been validated through numerical simulations and experiments, demonstrating significant enhancements in MPPT performance even under challenging scenarios. A comprehensive comparison study was conducted with two traditional MPPT algorithms, PID and P&O, which demonstrated the superiority of the proposed method in generating higher power and less control time. The proposed method generates the least power loss in both steady and dynamic states and exhibits an 8.2% higher average power and 60% less control time compared to traditional methods, indicating its superior performance. The proposed method was also found to perform well under real-world conditions and load variations, resulting in 56.1% less variability and only 2–3 W standard deviation at the GMPP