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This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

This paper aims to examine the extent to which performance indicators (PIs) reported by New Zealand (NZ) higher education institutions (HEIs) correspond with accounting standards and guidance and the effects issuance of principles-based authoritative guidance and early adoption of Public Benefit Entity Financial Reporting Standard 48 (PBE FRS 48) have on the PIs disclosed.

Using a content analysis index derived from accounting standards and guidance, we conduct a longitudinal assessment of the 2016 and 2019 statements of service performance published by 22 NZ HEIs.

The PIs reported extend beyond the service performance elements proposed by standard-setters. Despite few indicators on intermediate and broader outcomes, the measures disclosed by HEIs are reflective of their role in the NZ economy and the national Tertiary Education Strategy. The results show that principles-based authoritative guidance and early adoption of PBE FRS 48 influence the focus and type of measures disclosed, while there is no evidence of improvements in the reporting of impacts, outcomes and information useful for performance evaluation.

This paper provides timely insights for standard-setters and regulators on the influence principles-based accounting standards and guidance have on non-financial reporting practices.

This study contributes to the scant literature on HEIs’ service performance reporting. It presents a model for conceptualising HEIs’ PIs that can be used as a basis for future research on non-financial reporting. It also reflects on the tension between accountability and “accountingisation”, suggesting that, although the PIs reported support formal accountability, they do not communicate whether HEIs’ activities and outputs meet their social purpose.

The COVID-19 pandemic has created a significant disruption in the educational systems worldwide. Some institutions opted for emergency remote education due to the need to cancel in-person activities. The aims of this paper were to evaluate the use of asynchronous methodology in health sciences education, determine whether asynchronous methodology was sacrificing overall student satisfaction, and investigate whether satisfaction improved as the program develops.

Initially, there was phase 1 that corresponded to four weeks of activities. Each professor produced a video lesson, and after each video lesson, a weekly educational activity was made available. Next, phase 2 was implemented using the same methodology, however lasting six weeks. Three questionnaires were developed, and a Likert scale was administered to verify the students’ level of satisfaction. Data were analyzed using frequency distributions, mean values, standard deviation and confidence interval. The normality of the sum data (total of the questionnaires) was tested using the Kolmogorov–Smirnov test.

Although the students pointed out that the asynchronous methodology facilitated access to the content and considered this methodology satisfactory, they expressed a reduced level of satisfaction regarding emergency remote education in general when data from the first weeks were compared to those of the previous weeks. It is clear that students became increasingly discouraged and tired over time, which motivated the institution to shift into a combination of synchronous and asynchronous methodology to improve student learning.

Teaching in the field of health care encompasses difficult competencies that sometimes are impossible to be learned remotely, so there is a need to examine and evaluate properly the remote education in this area. With careful planning, educational institutions can evaluate their experiences during the pandemic, allowing those involved to highlight strengths and identify weaknesses to better prepare for future needs to improve remote education.