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Purpose A literature review and meta-synthesis of qualitative research had enabled us to develop a grounded theory explaining the difficulties breast cancer survivors face with the initial decision to accept long-term endocrine therapy, and the everyday challenges of continuing or deciding to stop treatment early. Our objective was to interview a cohort of women in a UK setting to corroborate and complete the grounded theory with the end users’ primary involvement. Methods A semi-structured interview schedule was written based on the existing grounded theory. Fourteen women with a history of hormone-positive breast cancer were recruited and interviewed. The audio-recorded interviews were transcribed and analysed against the existing grounded theory. Results The findings were compatible with the core theory ‘Hobson’s choice or a horned dilemma’ and its constituent categories previously developed, with additional concepts identified and added to our paradigm models. Importantly, we found that some women who started with a strong sense of commitment to their treatment changed their mind as they experienced the medication side effects over time, impacting on their persistence with long-term endocrine therapy. Conclusion The findings indicate an opportunity for health providers to intervene and influence women’s waning perceptions of the necessity of their treatment, for example upon experiencing the side effects. Interventions could involve the provision of side effect management strategies via accessible resources.

Graphical abstract, Highlights • Khat is a widely used plant-based psychostimulant. • Administration of khat leads to disruption of spatial working memory in mice. • It promotes in mice perseverative behavior rather than alternation behavior. • Khat treated mice also show right side position preference., Around 10 million people chew the fresh leaves and twigs of Catha edulis Forsk (khat), which synthesize cathinone, for its psychostimulatory effect. Several studies have reported that regular khat users show executive and cognitive dysfunction, such as impaired inhibitory control and poor performance on memory tests. In this study, the effect of fresh khat extract (100 and 250 mg/kg) on spatial working memory and short-term memory in mice was assessed using spontaneous and rewarded alternation T-maze tests. In the spontaneous alternation test, mice treated with fresh khat extract decreased their spontaneous alternation level to around chance level, and it remained at this level over the 7 days of khat administration and testing. On testing after a 7-day khat free period, the previously khat treated mice showed alternation level above chance but below their pre-khat alternation level. In the rewarded alternation test, acute treatment with khat caused the mice to alternate well below chance level, and then over the next 3 days of khat treatment, the alternation level increased. After a 2 and 9-day khat-free period, the previously khat treated mice alternated above chance level but below their pre-khat level. In both these tests, the mice did not show any position preference before khat treatment; however, during the khat treatment, the mice showed a right side tendency. The results show that khat treatment causes persistent changes in alternation behavior and promotes perseverative behavior, presumably due to its effect on the neural circuits activity and the neurotransmitters and promotion of position preference.

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.

In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

By the increasing growth of the Internet of Things (IoT) which provides interconnection and communications between electronic devices and corresponding sensors, a large volume of data is exchanged by multi-input multi-output (MIMO) telecommunication systems. In the case of IoT, reducing the data volume by removing the data redundancy results in more green communication by less power consumption for data transmission and less required storage memory. An approach to avoid the pilot data thus having less redundant data is using blind techniques. This research work presents an improvement to Stone’s blind source separation (BSS) precision, robustness, and computation load and its application to blind MIMO IoT interference channel estimation, multi-nodes IoT data detection, separation and identification in a MIMO-OFDM IoT network. Stone’s BSS is based on complexity conjecture indicating the independent sources have higher predictability than the mixtures. The presented improvement to Stone’s BSS is by a probabilistic modification to the short-term predictability merit by acquiring the prediction coefficients proportional to probability weights which follow a super Gaussian distribution assumption for sources. The probabilistic modification to Stone’s BSS (P-Stone) makes it maximally compatible with a pre-specified probability distribution model, and thereof the signal recovery is not only due to predictability maximization, but it is also inherent to increasing non-gaussianity which results in more independent recoveries, and less dependent on serial dependency of sources. Despite the Stone’s BSS, the proposed merit function does not need any long-term predictor; thus, it achieves around fifty times lower complexity load using just short-term predictors. The superiority of P-Stone to Stone BSS, AMUSE, and SOBI as well-known second-order techniques has been statistically evaluated and clarified through the experiments over MIMO-IoT networks of different combinations. As well, the comparative analysis over multimedia mixtures of music, speech, and images demonstrates its efficiency dominance.

Co-administration of multiple intravenous (IV) medicines down the same lumen of an IV catheter is often necessary in the intensive care unit (ICU) while ensuring medicine compatibility. This study explores ICU nurses' views on the everyday practice surrounding co-administration of multiple IV medicines down the same lumen. Qualitative study using focus group interviews. Three focus groups were conducted with 20 ICU nurses across two hospitals in the Thames Valley Critical Care Network, England. Participants' experience of co-administration down the same lumen and means of assessing compatibility were explored. All focus groups were recorded, transcribed verbatim, and analysed using thematic analysis. Functional Resonance Analysis Method was used to provide a visual representation of the co-administration process. Two key themes were identified as essential during the process of co-administration, namely, venous access and resources. Most nurses described insufficient venous access and lack of compatibility data for commonly used medicines (eg, analgesics and antibiotics) as particular challenges. Strategies such as obtaining additional venous access, prioritizing infusions, and swapping line of infusion were used to manage IV administration problems where medicines were incompatible, or of unknown or variable compatibility. Nurses use several workarounds to manage commonly encountered medication compatibility problems that may lead to delays in therapy. Organizations should review and tailor compatibility resources towards commonly administered medicines using an interdisciplinary approach. Developing a clinical decision-making pathway to minimise variability while promoting safe co-administration practice should be prioritised. This study highlights several ways ICU nurses are able to manage challenges associated with co-administration and the need for the development of a more robust and comprehensive compatibility resource that is relevant to everyday practice through collaboration between nurses and pharmacists.

Othman AlOmeir, Nilesh Patel, Parastou Donyai Department of Pharmacy, University of Reading, Reading, Berkshire, UKCorrespondence: Othman AlOmeir; Parastou Donyai, Email aalomear@su.edu.sa; p.donyai@reading.ac.ukObjective: Non-adherence to adjuvant hormone therapy prescribed orally in the treatment of breast cancer is complex as the literature has shown. Many women find it hard to adhere to the hormonal medicines they are prescribed and expected to take for at least 5 years following the initial management of their breast cancer. Arguably, communicating other women’s ‘trials, tribulations, and triumphs’ with medication-taking could help newly-diagnosed patients to better prepare for the journey ahead. Our objective was to visually represent women’s experiences with these medicines using data synthesized in the literature.Methods: Three schematics were drawn for each phase of medication-taking, namely, starting out, adherence, and cessation. The schematics were validated by interviewing a panel of healthcare professionals (n=10) and calculating a Content Validity Index (CVI). The edited drawings were discussed with a separate panel of breast cancer survivors (n=14) whose responses were elicited qualitatively in one-to-one interviews.Results: A total of 76 individual pictograms were drawn across the three schematics. The 13 pictograms that had an item-level CVI< 0.8 were modified according to feedback resulting in three final schematics with an overall CVI of 87%, 87% and 80%, respectively.Conclusion: Synthesised summaries of women’s experiences with oral hormone therapy for breast cancer were visualised via three validated schematics. The schematics could aid patient-professional communication to help anticipate and tackle negative experiences and support decisions to take hormone medication in breast cancer.Keywords: adherence, medication, breast cancer, hormone therapy, qualitative research, pictogram

Background More people are turning to internet pharmacies to purchase their prescription medicines. This kind of purchase is associated with serious risks, including the risk of buying fake medicines, which are widely available on the internet. This underresearched issue has been highlighted by many newspaper articles in the past few years. Newspapers can play an important role in shaping public perceptions of the risks associated with purchasing prescription medicines on the internet. Thus, it is important to understand how the news media present this issue. Objective This study aimed to explore newspaper coverage of the problem of purchasing fake prescription medicines on the internet. Methods Newspaper articles were retrieved from the ProQuest electronic database using search terms related to the topic of buying fake prescription medicines on the internet. The search was limited to articles published between April 2019 and March 2022 to retrieve relevant articles in this fast-developing field. Articles were included if they were published in English and focused on prescription medicines. Thematic analysis was employed to analyze the articles, and the Theory of Planned Behavior framework was used as a conceptual lens to develop the coding of themes. Results A total of 106 articles were included and analyzed using thematic analysis. We identified 4 superordinate themes that represent newspaper coverage of the topic of buying prescription medicines on the internet. These themes are (1) the risks of purchasing medicines on the internet (eg, health risks and product quality concerns, financial risks, lack of accountability, risk of purchasing stolen medicines), (2) benefits that entice consumers to make the purchase (eg, convenience and quick purchase, lower cost, privacy of the purchase), (3) social influencing factors of the purchase (influencers, health care providers), and (4) facilitators of the purchase (eg, medicines shortages, pandemic disease such as COVID-19, social media, search engines, accessibility, low risk perception). Conclusions This theory-based study explored the news media coverage of the problem of fake prescription medicines being purchased on the internet by highlighting the complexity of personal beliefs and the range of external circumstances that could influence people to make these purchases. Further research is needed in this area to identify the factors that lead people to buy prescription medicines on the internet. Identifying these factors could enable the development of interventions to dissuade people from purchasing medicines from unsafe sources on the internet, thus protecting consumers from unsafe or illegal medicines.

Background Many people in the United Kingdom are turning to the internet to obtain prescription-only medicines (POMs). This introduces substantial concerns for patient safety, particularly owing to the risk of buying fake medicines. To help reduce the risks to patient safety, it is important to understand why people buy POMs on the web in the first place. Objective This study aimed to identify why people in the United Kingdom purchase medicines, specifically POMs, from the internet, and their perceptions of risks posed by the availability of fake medicines on the web. Methods Semistructured interviews were conducted with adults from the United Kingdom who had previously purchased medicines on the web. Purposive sampling was adopted using various methods to achieve diversity in participants’ experiences and demographics. The recruitment was continued until data saturation was reached. Thematic analysis was employed, with the theory of planned behavior acting as a framework to develop the coding of themes. Results A total of 20 participants were interviewed. Participants had bought various types of POMs or medicines with the potential to be misused or that required a higher level of medical oversight (eg, antibiotics and controlled medicines). Participants demonstrated awareness of the presence and the risks of fake medicines available on the internet. The factors that influence participants’ decision to buy medicines on the web were grouped into themes, including the advantages (avoiding long waiting times, bypassing gatekeepers, availability of medicines, lower costs, convenient process, and privacy), disadvantages (medicine safety concerns, medicine quality concerns, higher costs, web-based payment risks, lack of accountability, and engaging in an illegal behavior) of purchasing medicines on the web, social influencing factors (interactions with health care providers, other consumers’ reviews and experiences, word of mouth by friends, and influencers’ endorsement), barriers (general barriers and website-specific barriers) and facilitators (facilitators offered by the illegal sellers of medicines, facilitators offered by internet platforms, COVID-19 outbreak as a facilitating condition, and participants’ personality) of the purchase, and factors that lead people to trust the web-based sellers of medicines (website features, product appearance, and past experience). Conclusions In-depth insights into what drives people in the United Kingdom to buy medicines on the web could enable the development of effective and evidence-based public awareness campaigns that warn consumers about the risks of buying fake medicines from the internet. The findings enable researchers to design interventions to minimize the purchasing of POMs on the web. A limitation of this study is that although the interviews were in-depth and data saturation was reached, the findings may not be generalizable, as this was a qualitative study. However, the theory of planned behavior, which informed the analysis, has well-established guidelines for developing a questionnaire for a future quantitative study.

Background: Obesity is a positive risk factor in development of hypertension, diabetes, gallbladder diseases, coronary heart diseases, and few types of cancers. Body mass index (BMI) is an inexpensive and easy screening method for weight category: Underweight, healthy weight, overweight, and obesity. Only a few studies have been conducted until now which focuses on finding any relationship between BMI and blood group of school going children. Aims and Objectives: The aims of this were as follows: (a) To find out BMI in school children of study center and (b) to find out correlation of BMI with blood groups and other variables of participants. Materials and Methods: It was a cross-sectional study conducted among 101 children of school going age group 10–15 years at one of the private schools of Ahmedabad, Gujarat. A questionnaire was prepared which included sections of demographic details, blood group, diet history, and family history. Results: About 50% of participants were from of either age 13 or 14. There was almost same proportion of participants of both sexes. About 70% of participants were found to be underweight, while about 15% were overweight. Overweight category of BMI is most commonly seen in children having blood group “A” while no child of “O” blood group was found to be overweight. This association was statistically significant. Conclusion: Almost 70% of participants were underweight, while about 15% were overweight. Overweight was most commonly seen in children having blood group “A.” No child of “O” blood group was found to have overweight.

This chapter illustrates the role of evolutionary optimization in designing AI end-devices to monitor the efficiency of agriculture vehicles (AgVs) mainly on the field via economic sound-based IoT sensors. Due to the application of AI on end-devices, there is a certain limitation for memory and complexity of the deployed algorithms. In such a condition, a machine learning model with optimal structure is of favorite. Lightweight is an aspect of the model as its model structure is optimized for operation of minimum complexity but an acceptable efficiency. The chapter explains that how this target can be achieved by the deployment of metaheuristic evolutionary optimizers. The AI model with optimum complexity and structure is suitable especially for deployment on smartphones. The optimization assists the designer in achieving not only the lightweight structure but also a maximized efficiency for recognition via built-in economic sensors such as smartphone microphones.

Pemphigus is a life‐threatening disease but timely recognition of oral lesions is critical to prevent serious cutaneous and fatal complications.

Currently, the development of smart systems for recognition and sensing of volatile organic compounds (VOCs) in medical, agricultural, biometric, security and safety, applications is an emerging research area. This review presents an introduction to the field of VOC recognition by analytical methods and sensing by chemical sensors. The role of pattern recognition methods in the analysis of sensor array response is briefly discussed. Besides, the electronic nose (E-Nose) system (a bio-inspired prototype of the natural olfaction system by combining a chemical sensor array and pattern recognition methods) and its significance for VOCs recognition and sensing in different applications is explained. The study concludes current constraint and future prospects of VOC recognition and sensing in real-time applications.

The wind power generation is a rapidly growing grid integrated renewable energy (RE) technology with an installed capacity of 539.291 GW. The capability of the wind energy conversion system (WECS) to remain integrated into the utility network in the case of low voltage events is called low-voltage ride-through (LVRT) capability. This paper offers a comprehensive overview of improvement techniques of the LVRT capability in WECS to increase the wind energy penetration level in the utility grid. Exhibited portrait manifests a broad spectrum of 1) wind turbines, 2) electrical generators used for wind power applications, 3) international grid codes applicable for grid integration of WECS, 4) LVRT fundamentals in WECS, 5) wind turbines LVRT methods by doubly fed induction generator (DFIG), 6) wind turbines LVRT methods by permanent magnet synchronous generators (PMSG), and 7) LVRT methods of wind turbines using squirrel cage induction generator (SCIG). This ready-reckoner paper critically reviews and classifies more than 190 research papers on LVRT issues, practices, and available technologies for grid integration in wind energy systems, and it aims to be a quick reference for the researchers, designers, manufacturers, and engineers working in the same field.

Background Since 2015, pharmacists have been integrating into English general practices and more recently into primary care networks. General practice-based pharmacists provide a range of patient-facing services, such as medication reviews, management of long-term conditions and minor ailments, prescribing duties and answering queries over the telephone. Literature reports patients’ satisfaction with general practice-based pharmacists’ services, however, previous research captured only limited experiences. The aim of the current study was to pursue an extensive exploration of patients’ experiences of pharmacists in general practice. Methods General practice-based pharmacists, working in practices in West London, Surrey and Berkshire, handed invitation packs to patients seen during consultations. Patients that wanted to take part in the study were invited to undertake a qualitative, in-depth, face-to-face, semi-structured interview within the practice with which each patient was registered. Interviews lasted from 15 min to more than 1 h and were audio-recorded. Recruitment continued until data saturation. Audio-recordings were transcribed verbatim and transcripts analysed thematically. Results Twenty participants were interviewed. Four themes were discerned: awareness (“I had been coming to this practice for 24 years and I didn’t know that there was a pharmacist”); accessibility (“People ring for a GP [general practitioner] appointment … it’s Monday and they [receptionist] tells you ‘We can slot you in on Friday’ … with a pharmacist on board, they can [instantly] look at you”); interactions (“I’ve always had a really good interaction with them [pharmacists] and they listen and they take on board what I’m trying to say”); and feedback (“It’s easier [to collect feedback instantly] because I could have forgotten half of what they [pharmacists] have told me in an hour or so’s time”). Conclusions Findings indicate that pharmacists’ integration into general practices could improve accessibility to, and the quality of, care received. The findings will assist policy development to provide general practice-based pharmacists’ services as per patients’ needs.

Smart monitoring of off-road vehicles is cursed by their complex and expensive IoT sensors technologies. High dependence on the cloud/fog computation, availability of the network, and expert knowledge make it handicap in the rural off-network areas. Use of edge devices, such as smartphones, attributed by computation capabilities is the solution that is yet to be developed at commercial level (Fawwaz and Chung 2020) and (Zhengwei et al. , 2021). Additionally, the user's growing demand for economic and user-friendly technology motivates to shift from costly and complex sensors to economic. In this article, we present the hybridized computational intelligence methodology to develop an edge-device-enabled AI technology for health monitoring and diagnosis (HM&D) of the off-road vehicles, taking use of super economic microphones as sensors. Smartphones are benefited by integrated microphones, and thus, the App-based developed technology is generalized for all vehicles from old to new. Enhanced selection and log-scaled mutation genetic algorithms is used to evolve the structure of the artificial neural network toward an optimally lightweight structure. Each evolved lightweight ANN structure is trained by scaled conjugate gradient back-propagation training algorithm to optimize corresponding weights and biases. The comparative results with currently reported genetic algorithms for edge computation prove it a breakthrough technology for edge-device-enabled HM&D of off-road vehicles (Yan et al. , 2020).

Background:\ud Uptake of non-medical prescribing by pharmacists working in primary care has been slow. This is despite benefits such as quicker and more efficient access to medicines for patients, a reduction in doctor workload and enhanced professional satisfaction. This systematic review explores the views, opinions and attitudes of pharmacists and graduates towards non-medical prescribing.\ud \ud Methods:\ud Medline, ScienceDirect, Embase and the University of Reading Summon Service were searched to identify qualitative and mixed methods papers that examined the views, opinions and attitudes of pharmacists and graduates towards non-medical prescribing. Papers published between January 2003 and September 2017 were included. Studies were quality assessed using the CASP checklist and then analysed using thematic synthesis.\ud \ud Results:\ud After 85 full text articles were assessed, a final 14 studies were eligible for inclusion. The included studies assessed pharmacists currently prescribing and other pharmacists and graduates with familiarity of non-medical prescribing. Thematic synthesis identified two themes: (1) practice environment, and (2) pharmacist’s role. Non-medical prescribing was considered a natural extension to the role of a pharmacist despite difficulties in completing the required training. The ability to then prescribe was dependent on funding and access to medical records, time and support staff. Pharmacists experienced professional rivalry with both support and resistance from members of the primary care team. The provision of training was frequently referred to as unsatisfactory. Pharmacists were motivated to prescribe, deriving increased job satisfaction and sense of professionalism, however, they often felt under prepared for the reality of unsupervised practice. Furthermore, pharmacists reported a cautious approach with a fear of making errors frequently discussed. \ud \ud Conclusions:\ud This review has identified themes and subsequent barriers and facilitators to non-medical prescribing. Many of the barriers are more perceived than real and are diminishing. Consideration of these will assist and advance pharmacist prescribing in primary care, leading to positive outcomes for both patient care and the pharmacy profession.

Penetration level of solar photovoltaic (PV) energy in the utility network is steadily increasing. This changes the fault level and causes protection problems. Furthermore, multi-tapped structure of distribution network deployed to integrate solar PV energy to the grid and supplying loads at the same time also raised the protection challenges. Hence, this manuscript is aimed at introducing an algorithm to identify and classify the faults incident on the network of utilities where penetration level of the solar PV energy is high. This fault recognition algorithm is implemented in four steps: (1) calculation of Stockwell transform-based fault index (STFI) (2) calculation of Wigner distribution function-based fault index (WDFI) (3) calculation of combined fault index (CFI) by multiplying STFI and WDFI (4) calculation of index for ground fault (IGF) used to recognize the involvement of ground in a fault event. The STFI has the merits that its performance is least affected by the noise associated with the current signals and it is effective in identification of the waveform distortions. The WDFI employs energy density of the current signals for estimation of the faults and takes care of the current magnitude. Hence, CFI has the merit that it considers the current magnitude as well as waveform distortion for recognition of the faults. The classification of faults is achieved using the number of faulty phases. An index for ground fault (IGF) based on currents of zero sequence is proposed to classify the two phase faults with and without the ground engagement. Investigated faults include phase to ground, two phases fault without involving ground, two phases fault involving ground and three phase fault. Fault recognition algorithm is tested for fault recognition with the presence of noise, various angles of fault incidence, different impedances involved during faulty event, hybrid lines consisting of overhead line (OHL) and underground cable (UGC) sections, and location of faults on all nodes of the test grid. Fault recognition algorithm is also tested to discriminate the transients due to switching operations of feeders, loads and capacitor banks from the faulty transients. Performance of the fault recognition algorithm is compared with the algorithms based on discrete wavelet transform (DWT), Stockwell transform (ST) and hybrid combination of alienation coefficient and Wigner distribution function (WDF). Effectiveness of the fault recognition algorithm is established using a detailed study on the IEEE-13 nodes test feeder modified to incorporate solar PV plant of capacity 1 MW in MATLAB/Simulink. Algorithm is also validated on practical utility grid of Rajasthan State of India.

Background In England, since 2015, there has been a formal drive to integrate pharmacists into general practice as a new healthcare service. Research efforts have offered insights into how general practice-based professionals and patients view the service, however, they took no account of community pharmacy teams’ opinions. There have been anecdotal statements about opposition from community pharmacies to the service, due to fears of losing business. The aim of the current study was to identify the experiences and perceptions of community pharmacy teams regarding pharmacists’ presence in general practice. Methods The National Health Service Choices website was used to identify community pharmacies within a radius of two miles from eight West London general practices. The search resulted in 104 community pharmacies which were all contacted via telephone. Pharmacy staff who verbally expressed their interest to participate were then provided with the study’s documents. Qualitative, face-to-face, semi-structured interviews were conducted inside the pharmacy from which each participant was recruited. Interviews lasted 30 to 45 min and were audio-recorded. Audio-recordings were transcribed verbatim and transcripts analysed thematically. Results Forty-eight community pharmacy staff participated. Four themes were discerned: awareness (“I knew that [pharmacists] have already been implemented [in general practice] but I haven’t really followed it … where does the pharmacist role come?”); interactions (“I’m just so pleased that there’s a pharmacist professional in the general practice … because we speak the same language!”); patient care (“if I was a patient knowing that there is a general practitioner and a pharmacist [in general practice], I would … think ‘nothing can go wrong at the moment’”); and funding challenges (“if general practices take on the extra responsibility of stop smoking or flu vaccination campaigns … financially, this would affect this pharmacy”). Conclusions The current study revealed the perceived impact of general practice-based pharmacists on community pharmacies would be improved communication between pharmacies and practices. Findings will inform policy so that any future framing of pharmacists’ presence in general practice considers the needs of community pharmacies.

This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.

Background: The adverse health consequences of overweight and obesity in India leads to higher prevalenceof diabetes mellitus and cardiovascular diseases. Also the compliance of people for routine form of exercisefor BP control has not been very encouraging due to time, place etc constrains.Aim: Therefore we conducted a nonrandomised clinical study to determine the short-term effects ofisotonic handgrip exercise by using smiley balls on blood pressure in healthy normal weight and overweightadolescents with the objective to find a user friendly exercise which help in reducing blood pressure.Method: A non randomized clinical study was conducted on 100 young normal-weight and pre-obese adults(50 Boys and 50 Girls) in the age group of 18–25 years. Isotonic handgrip exercise was performed at therate 20 contractions/minute (2 sec contraction/1 sec relaxation) at maximal intensity for 10 minutes usingsmiley ball. Pulse rate and blood pressure parameters were tested at baseline and immediately after exercisein post-exercise recovery period.Result: Statistically significant reduction was observed in systolic blood pressure(SBP) and mean arterialpressure (MAP) in both pre-obese boys and girls groups while pulse pressure & mean arterial pressure innormal weight girls after exercise regime.Conclusion: We conclude that the exercise regime under consideration can produce some short-termbeneficial effects with respect to blood pressure in especially pre-obese group of adults.

AimTo investigate effect of V˙ O2max) among sedentary adults.MethodsWe studied 53 sedentary urbanites aged ≥50 years, randomised into: (1) male (MS) and (2) female (FS) undertaking three short-duration exercise (5–10 min) daily, and (3) male (ML) and (4) female (FL) exercising 30–60 min 3–5 days weekly. Resting systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate and V˙ O2max were measured at baseline and 8 weekly for 24 weeks.ResultsAt baseline, 50% MS, 61.5% ML, 53.8% FS and 53.8% FL had SBP ≥120 mm Hg, and 14.3% MS, 53.8% ML, 23.1% FS and 38.5% FL had DBP ≥80 mm Hg. At 24 weeks, where SBP remained ≥120 mm Hg, values decreased from 147±19.2 to 132.3±9.6 mm Hg (50% of MS), from 144±12.3 to 128±7.0 mm Hg (23.1% of ML), from 143.1±9.6 to 128.0±7.0 mm Hg (53.8% of FS) and from 152.3±23.7 to 129±3.7 mm Hg (30.8% of FL). For DBP ≥80 mm Hg, MS and FS percentages maintained, but values decreased from 101±15.6 to 84.5±0.7 mm Hg (MS) and 99.0±3.6 to 87.7±4.9 mm Hg (FS). In ML and FL, percentage with DBP ≥80 mm Hg dropped to 15.4% (86.1±6.5 to 82.5±3.5 mm Hg) and (91.4±5.3 to 83.5±0.71 mm Hg). V˙ O2max increased from 26.1±4.4 to 32.0±6.2 for MS, from 25.8±5.1 to 28.8±5.4 for ML (group differences p=0.02), from 20.2±1.8 to 22.7±2.0 for FS and from 21.2±1.9 to 24.2±2.7 for FL (groups differences p=0.38).ConclusionAccumulated moderate intensity exercise bouts of V˙ O2max improvements compared with current recommendations among sedentary adults.

A very recent methodology of data sampling with a sub-Nyquist rate is compressive sensing (CS). CS theory is established based on the higher sparsity of the data containing information. It uses this fact for recovery of the data sampled with much fewer samples than required by the Nyquist sampling theorem. CS addresses two main questions: (i) how to construct a compressive sensing matrix which satisfies the uniqueness required for later recovery, (ii) how to recover the original much longer signal vector than the current available compressively sensed one. This chapter reviews mainly the related theory and solution for the latter question of the signal/image recovery from the compressively sensed signal vectors. It presents a review of the pre-requirements in compressive sensing for the recovery process and summarizes the related theorems. The recovery of the signals after being compressively sensed with and without noise is studied and the main methodologies used are explained.

This chapter illustrates the characteristics of plant genetics-inspired evolutionary optimization (PGEO). The computation strategy of PGEO is inspired by the theory of Mendelian evolution. Presented PGEO optimizer is a binary-coded algorithm based on mainly three concepts from plant genetics: (i) the “denaturation” of DNA of two different species to produce the hybrid “offspring DNA,” (ii) the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations, (iii) the epimutation, through which organism resists for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary training technique. Based on this framework, four evolutionary operators—(1) flipper, (2) pollination, (3) breeding, and (4) epimutation—are created in the binary domain. The chapter gives characteristics and a detailed tutorial to PGEO theory.

Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.

Agents are intelligent entities that act flexibly and autonomously to make wise decisions based on their intelligence and experience. A multi-agent system (MAS) contains multiple intelligent interconnected collaborating agents for solving a problem beyond a single-agent ability. A smart grid combines advanced intelligence systems, control techniques, and sensing methods into an existing utility power network. For controlling smart grids, various control systems with different architectures have already been developed. MASs based control of power system operations has been shown to overcome the limitations of time required for analysis, relaying and protection, transmission switching, communication protocols, and management of plant control. These systems provide an alternative for the fast and accurate power networks control. This paper comprehensively overviews MASs used for the control of smart grids. This is intended to provide a wide spectrum on the status of smart grids, MAS-based control techniques and their implementation for the control of smart grids. Use of MASs to control various aspects of smart grids including management of energy, marketing energy, pricing, scheduling energy, reliability, the security of network, fault handling capability, communication between agents, SG-electrical vehicles, SG-building energy systems, and soft grids have been critically reviewed. More than a hundred publications on the topic of MAS-based control of smart grids are critically examined, classified, and arranged for fast reference.

After deep venous thrombosis (DVT), many patients have impaired quality of life (QOL). We aimed to assess whether pharmacomechanical catheter-directed thrombolysis (PCDT) improves short-term or long-term QOL in patients with proximal DVT and whether QOL is related to extent of DVT.The Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis (ATTRACT) trial was an assessor-blinded randomized trial that compared PCDT with no PCDT in patients with DVT of the femoral, common femoral, or iliac veins. QOL was assessed at baseline and 1 month, 6 months, 12 months, 18 months, and 24 months using the Venous Insufficiency Epidemiological and Economic Study on Quality of Life/Symptoms (VEINES-QOL/Sym) disease-specific QOL measure and the 36-Item Short Form Health Survey (SF-36) physical component summary (PCS) and mental component summary general QOL measures. Change in QOL scores from baseline to assessment time were compared in the PCDT and no PCDT treatment groups overall and in the iliofemoral DVT and femoral-popliteal DVT subgroups.Of 692 ATTRACT patients, 691 were analyzed (mean age, 53 years; 62% male; 57% iliofemoral DVT). VEINES-QOL change scores were greater (ie, better) in PCDT vs no PCDT from baseline to 1 month (difference, 5.7; P = .0006) and from baseline to 6 months (5.1; P = .0029) but not for other intervals. SF-36 PCS change scores were greater in PCDT vs no PCDT from baseline to 1 month (difference, 2.4; P = .01) but not for other intervals. Among iliofemoral DVT patients, VEINES-QOL change scores from baseline to all assessments were greater in the PCDT vs no PCDT group; this was statistically significant in the intention-to-treat analysis at 1 month (difference, 10.0; P .0001) and 6 months (8.8; P .0001) and in the per-protocol analysis at 18 months (difference, 5.8; P = .0086) and 24 months (difference, 6.6; P = .0067). SF-36 PCS change scores were greater in PCDT vs no PCDT from baseline to 1 month (difference, 3.2; P = .0010) but not for other intervals. In contrast, in femoral-popliteal DVT patients, change scores from baseline to all assessments were similar in the PCDT and no PCDT groups.Among patients with proximal DVT, PCDT leads to greater improvement in disease-specific QOL than no PCDT at 1 month and 6 months but not later. In patients with iliofemoral DVT, PCDT led to greater improvement in disease-specific QOL during 24 months.

As a great computational intelligence technique, artificial neural networks (ANNs) have intensively attracted the interest of researchers of artificial intelligence. Due to the easy implementation of ANN, vast types of structures and associated rules, their successful application can be seen in real-life and industrial problems. From a wide variety of ANN such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, and spiking ANN, we describe a multi-layer perceptron ANN with the focus on designing AI-based condition monitoring system. This chapter presents the neuroevolution-based monitoring system to detect the oil filter condition in agricultural (Ag) machines using meta-heuristic METO algorithm. Evolutionary learning algorithm finds the optimal weights of ANN along with the behavior of each neuron.

Purpose Numerous studies have examined non-adherence to adjuvant endocrine therapy in women recovering from breast cancer, but none provides a comprehensive theory to explain the challenges of long-term medication taking and resilience needed to continue. The aim of this study was to source, appraise, and synthesize data from existing qualitative studies to develop an in-depth explanatory model of non-adherence and discontinuation of hormonal medication among breast cancer survivors. Methods A comprehensive search of databases and the literature identified 24 eligible qualitative studies published 2010–2019. Quotations (n = 801) listed within these papers and the original author interpretations were synthesized using NVivo, and grounded theory methodology. Results At the beginning, knowledge about adjuvant endocrine therapy, trust in doctors, and worries and expectations, mean agreeing to medication is the only viable option, akin to a Hobson’s choice. Thereafter, women’s ability to deal with medication side-effects, knowledge and support received affect their decision to continue, akin to a horned dilemma where giving up the medication risks cancer recurrence and continuing means reduced contentment. Women stopping medication altogether question treatment necessity, search for normalcy and prioritize quality of life. Conclusion Shared experiences and understandings were uncovered by examining commonalities in existing publications. The core category explained the difficulties women face with the initial decision to accept long-term endocrine therapy and then the everyday challenges of continuing or deciding to stop treatment early. An educational tool to inform survivors and health professionals about these challenges could potentially improve women’s experience on treatment and in turn their adherence.

Compressive sensing is a recent data sampling technique with a variety of advantages over the classical Shannon–Nyquist based technique. The main theoretical approach to compressive sensing is based on the informative value of data according to sparsity where the higher sparsity indicates the higher information content. Therefore, while data samples are linearly mixed and sensed by a much smaller number of sensors and result in compressively sensed data of much less volume, the sparsity maximization is a strong approach to retrieving the original higher volume data from the compressed one. The sparsity analysis is the main approach to the idea of compressive sensing, and an efficient measure of sparsity has a key role in this regard. Although k-sparsity is the sparsity measure in use by compressive sensing techniques, it being well established in the theoretical analysis of compressive sensing, there are a variety of sparsity measures. This chapter reviews the sparsity measures from the k-sparsity already in use to be compared with other more complicated sparsity measures.

Compressive sensing is a recent highly applicative approach. It enables efficient data sampling at a much lower rate than the requirements indicated by the Nyquist theorem. Compressive sensing possesses several advantages, such as the much smaller need for sensory devices, much less memory storage, higher data transmission rate, many times less power consumption. Due to all these advantages, compressive sensing has been used in a wide range of applications. An application field of compressive sensing in health care is in neuro-signal acquisition. This chapter reviews the different aspects of neuro-signal compressive sensing in the literature. It provides an intuitive explanation of the formulas of implementing compressive sensing in neuro-signal-based applications with descriptive figures and block diagrams.

A very recent meta-heuristic optimizer is by inspiration from plant biology where the Mendel law of heredity is implemented through multi-species in two generations. Plant biology-inspired optimizer named as Mendelian Evolutionary Optimization Algorithm (METO), which has several advantages outperforming the state-of-the-art optimizers. It is highly capable of finding the best solution for multimodal problems with global optimal solution and computationally fast. METO not only performs well over the problems with around thirty variables but also performs well on the very high-dimensional problems such as hundred variables. Besides the literature introducing the characteristics of the METO, this chapter investigates the way METO explores the search space of the problem by exchanging the gene’s information between the multi-species. Each plan in a species represents by double strands DNA. Here, we will observe how METO covers the search space and avoid being stuck in a local minimum and moves toward the global solution. In this chapter, we investigate the behavior of the operators of METO such as flipper, pollination, self- and cross-breeding, and epimutation.

A simple, rapid, precise, and accurate isocratic reversed-phase stability indicating HPLC method was developed and validated for simultaneous determination of the nebivolol and valsartan in the tablet dosage form. The effective chromatographic separation was achieved by a YMC pack pro octadecyl silane (150×4.6 mm, 3 µm) column using a mobile phase composed of methanol: acetonitrile: 0.05 M potassium dihydrogen phosphate buffer (Ph=3.0 with 10% ortho phosphoric acid after addition of 0.2% triethylamine) (30:30:40, v/v/v) at a flow rate of 1 mL/min and UV detection at 282 nm. Drugs were subjected to the acid, base, oxidation, heat, and photolysis to apply the stress. Linearity ranges were 5–30 µg/mL (r2= 0.9989) for nebivolol and 80-480 µg/mL (r2=0.9991) for valsartan. Limit of detection was 0.38 µg/mL and 1.08 µg/mL for nebivolol and valsartan, respectively. The limit of quantification for the nebivolol and valsartan was 1.15 µg/mL and 3.27 µg/mL, respectively.

Malignant Pleural Mesothelioma (MPM) is a rare pleural malignancy, with a vague presentation complicated by a decades-long latency period between environmental exposure and clinical manifestations. Spontaneous hydro-pneumothorax is a rare presentation of MPM, most often requiring invasive tissue biopsy to confirm the etiologic diagnosis. We present the case of 79-year-old male smoker with no documented history of asbestos exposure, who was found to have MPM after presenting with dyspnea and subsequently found to have recurrent hydro-pneumothorax. On Literature review of the limited documented cases of MPM with hydro-pneumothorax, we found an exclusively male population with a significant smoking history, a marked right sided pathology predominance, and a generally poor prognosis. While this corresponds with the examined case, and suggests that the presence of hydro-pneumothorax implies a high-grade tumor and significant tissue invasion, and therefore poor prognosis similar to that of stage 4 disease, it differs from more generalized case reviews of MPM, most importantly in their anatomical descriptions, prognostic indicators, and epidemiologic tendencies. Keywords: Malignant pleural mesothelioma, Hydrothorax, Pneumothorax, Hydro-pneumothorax

Background and Objectives Osteoarthritis (OA) of the knee affects the aging population and has an associated influence on the health care system. Rigorous studies evaluating radiofrequency ablation for OA-related knee pain are lacking. This study compared long-term clinical safety and effectiveness of cooled radiofrequency ablation (CRFA) with intra-articular steroid (IAS) injection in managing OA-related knee pain. Methods This is a prospective, multicenter, randomized trial with 151 subjects with chronic (≥6 months) knee pain that was unresponsive to conservative modalities. Knee pain (Numeric Rating Scale [NRS]), Oxford Knee Score, overall treatment effect (Global Perceived Effect), analgesic drug use, and adverse events were compared between CRFA and IAS cohorts at 1, 3, and 6 months after intervention. Results There were no differences in demographics between study groups. At 6 months, the CRFA group had more favorable outcomes in NRS: pain reduction 50% or greater: 74.1% versus 16.2%, P < 0.0001 (25.9% and 83.8% of these study cohorts, respectively, were nonresponders). Mean NRS score reduction was 4.9 ± 2.4 versus 1.3 ± 2.2, P < 0.0001; mean Oxford Knee Score was 35.7 ± 8.8 vs 22.4 ± 8.5, P < 0.0001; mean improved Global Perceived Effect was 91.4% vs 23.9%, P < 0.0001; and mean change in nonopioid medication use was CRFA > IAS (P = 0.02). There were no procedure-related serious adverse events. Conclusions This study demonstrates that CRFA is an effective long-term therapeutic option for managing pain and improving physical function and quality of life for patients with painful knee OA when compared with IAS injection. Clinical Trial Registration: ClinicalTrials.gov (NCT02343003).

In this paper, a real-life application of bi-level evolutionary optimization is proposed to optimize the electricity industry infrastructure. It offers a coordinated generation and transmission expansion planning (CGTEP) from the perspective of an independent system operator (ISO). The main objective of the proposed study is to show the effect of optimizing the generators concerning capacity and location both to reduce the transmission investment and increasing the reliability of the network. The proposed framework of bi-level optimization contributes to utilize global evolutionary optimization method GA in its hybrid form in level-I to select the location of lines and energy generators. The respective capacities of the corresponding selected lines and generators are optimized in the level-II by RW. In conflicting objectives of minimizing the investment for capacity addition in the network and maximizing the reliability, a Pareto-optimal solution is achieved by using the theory of marginal value (TMV). To satisfy TMV, the total cost is minimized, which comprises the cost of investment in building new transmission and generation capacities, cost of not-served expected energy, cost of unutilized expected generation, and cost of unserved energy due to the constrained network. Proposed methodology on IEEE 24-bus power system is presented encountering the combination of N-1 and probable N-2 contingency security criteria. The comparison results show that bi-level GA-RW optimization minimizes the investment with increasing power system reliability.

In the area of medical imaging, automation of medical image classification or recognition is an active field of research. Over the past decade, due to the popularity and usability of artificial neural networks, it is becoming the norm to achieve this automation by deep neural networks. In toxicology based research, zebrafish has become a key model organism in phenotypical imaging and drug discovery. Detection of complex patterns and phenotypes in zebrafish embryo during preclinical or clinical trials is a standard part of the drug discovery cycle. Currently this classification task of phenotypes is mostly experts based. In this work, we propose a fine-tuned convolution neural network (CNN) based model for automated classification of different phenotypical changes observed due to the toxic substance in the zebrafish embryo. We demonstrated the ability of CNN model as well as a fine-tuned CNN based model to classify different deformation in an embryo with high accuracy. Such automated medical imaging model can be used extensively by experts in the area of toxicology and drug discovery.