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Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Because of the global increase of climate change effects, floods are becoming more frequent and severer, especially in urban areas of coastal cities and islands where floodplains have turned into buildings because of rapid urbanisation, leading to a higher risk of damages. Urban heritage areas should be highly considered in the time of evacuation because of the vulnerability of streets and buildings and limitations on taking counteractions. Given these limitations, this study aims to propose a network of potential evacuation routes based on spatial configuration analysis of the heritage areas.

Penang Island's heritage site, namely, George Town, located on the northwest coast of Malaysia, is chosen as the case study. By using an approach of spatial configuration analysis using space syntax techniques in addition to considering the potential starting points for evacuation and flood risk map of the area, this study analysed the area's street network values for evacuation function during flood crisis time.

Potential evacuation routes were identified for flood disasters in the George Town heritage area. Furthermore, the proposed evacuation routes were evaluated in terms of time for evacuation by metric step-depth analysis of space syntax.

A few studies have focused on practical guidelines for evacuation routes based on spatial configuration analysis, an important yet neglected approach in this regard, especially concerning urban island areas. This study can contribute to providing strategies to reduce vulnerability and casualties in urban heritage areas.

The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

This article focuses on the assessment of entrepreneurship competence by selected vocational teachers in Italy. The exploratory research question addresses the extent to which entrepreneurship assessments are competence based, and the research seeks to identify fully fledged assessment programmes with both a formative and summative component, and the use of assessment rubrics. It also explores the extent to which entrepreneurship competence is referred to in school documentation and later assessed, and the tools and strategies used for such assessment.

This case study is part of a larger European research project promoted by Cedefop; in Italy it focused on six selected vocational IVET and CVET programmes and apprenticeship schemes. It used a wide range of instruments to ensure triangulation and multiple perspectives: analysed policy documents and undertook online interviews with experts and policy makers. At VET providers' premises it deployed: analysis of school documents; observations of learning environments; interviews and focus groups with (in schools) teachers, directors and vice directors, learners and alumni (in companies) instructors, company tutors and employers, apprentices and alumni.

Assessment tasks were rarely embedded within fully fledged assessment programmes involving both formative and summative tasks, and assessment rubric for grading. Most of the time, entrepreneurship programmes lacked self-assessment, peer assessment and structured feedback and did not involve learners in the assessment process. Some instructors coached the students, but undertook no clear formative assessment. These findings suggest institutions have a testing culture with regard to assessment, at the level of both policy and practice. In most cases, entrepreneurship competence was not directly assessed, and learning outcomes were only loosely related to entrepreneurship.

One limitation concerned the selection of the VET providers: these were chosen not on a casual basis, but because they ran programmes that were relevant to the development of entrepreneurship competence.

At the policy level, there is a need for new guidelines on competence development and assessment in VET, guidelines that are more aligned with educational research on competence development. To ensure the development of entrepreneurship competence, educators need in-service training and a community of practice.

So far, the literature has concentrated on entrepreneurship education at the tertiary level. Little is known about how VET instructors assess entrepreneurship competence. This study updates the picture of policy and practice in Italy, illustrating how entrepreneurship competence is developed in selected IVET and CVET programmes and apprenticeships.

Gender-diverse people experience unique cultural and interpersonal stigma in mainstream society and sometimes within their own communities; they face allegations of inauthenticity based on their nonconformity to either cisnormative or transnormative gender regimes. Based on 21 in-depth life history interviews, we unveil the intricate interactional process of negotiating identity and authenticity in the biographical work of gender-diverse individuals. In this study, gender-diverse people engaged in a “gender audit” with their gender-diverse interviewer. Gender audits yield verbal performances of gender with oneself and others. Ambiguity was “accounted for” or “embraced and created” in their biographical work to organize their life stories and undermine binary essentialism – a discourse that was “discursively constraining.” Gender audits took place in participants' day-to-day lives, either through self-audits, questioning from others, or both. In the final analysis, we assert that we all engage in gender auditing. Gender audits are intersubjective sites of domination, subordination, resistance, and social change. Gender diversity, then, can be viewed as a product of gender in flux.

The first-aid courses organized by the Youth Red Cross Carinthia (Austria) had a quality problem, necessitating a professionalization in teaching and time structure. This research aimed to enhance the quality and effectiveness of these courses by implementing modified lesson studies with non-professional trainers. The paper presents the realization process, empirical research and results obtained by applying the first-aid curriculum.

Around 22 lesson study first-aid courses (14 classes with 2 cycles, 8 with 3 cycles) were conducted and evaluated in different Austrian school types. An observation sheet was created to evaluate attention and competencies. Interviews were conducted with both teachers and students to validate the results.

The research findings demonstrate that lesson studies can significantly enhance the quality and effectiveness of first-aid courses. Inexperienced and experienced first-aid teachers significantly improved their teaching skills. Newly educated first-aid teachers showed substantial improvement, leading to the introduction of an induction period and coaching opportunity within the Youth Red Cross Carinthia.

This is the first lesson study conducted in a non-academic context. It highlights the adaptation process of Carinthian first-aid courses. It illustrates how lesson studies impact lesson clarity, instructional variety, student engagement in the learning process, student outcome, student feedback and teaching effectiveness in a non-academic context. It contributes to the literature on the application of lesson study in first-aid education and provides insight into the benefits of this approach in enhancing the quality of first-aid training.

The empirical studies of the options market efficiency have reported contradictory results, which sometimes confuse practitioners and academicians. The aim of this study was to clarify several aspects of options market efficiency by exploring the answers to two main questions: Under what conditions is the options market more efficient? Are the discrepancies in the estimated efficiency due to the reality of efficiency or mismeasurement?

Using a meta-analysis approach, 54 studies have been analyzed, which included 1,315 tests. The sum of the observations for all of the tests is 3.7 m observation sets. The effect size (type r) has been used to compare the different statistics in different studies. The cumulative effect size and its diversification have been calculated by the random effects model and Q statistic, respectively.

The most interesting finding of the study was that the options market, in all circumstances, is significantly inefficient. Another important finding was that the heterogeneity of options market efficiency is due to the complexity of pricing relations, test time, violation index and price type. To overcome this heterogeneity and accuracy, future studies should test the no-arbitrage options pricing relations at different times and by different price types, using complex and simple pricing relations and either mean violation or violation ratio efficiency measures.

Public disagreement about the options market efficiency in past studies means that this variable is heterogeneous in different conditions. As a significant contribution, this study develops the literature by proposing the causes of options market efficiency heterogeneity.

As Australians experience more fierce and frequent natural disasters, there are urgent calls for businesses to meaningfully respond to climate change. Australian financial and professional services employees occupy an ambiguous space as climate mitigation measures have different economic implications for their clients. The purpose of this paper is to investigate how Australian professionals experience climate change and respond to the issue within their workplace.

This mixed methods study applies a systems thinking framework to investigate: how do professionals’ experiences of the issue of climate change and the workplace influence their cognitions, emotions and behaviour? And in particular, what psychosocial antecedents precede voicing climate concern?

Firstly, a survey of professionals (N = 206) found social norms, perceived behavioural control and biospheric values, but not attitudes, significantly predicted prohibitive green voice. Middle managers were significantly likely to voice climate concern, whereas senior managers were significantly likely to express climate scepticism. Ten professionals were then interviewed to gain a contextualised understanding of these trends. Interpretive phenomenological analysis identified five interrelated themes: (1) active identity management, (2) understanding climate change is escalating, (3) workplace shapes climate change response, (4) frustration and alienation and (5) belief that corporations prioritise profit.

Findings are discussed in relation to how employees may both embody and adapt their organisations. These results have implications for understandings of workplace meaningfulness and organisational risk governance.