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In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The purpose of this transformative service research (TSR) is to apply, innovate on and extend the understanding of service-dominant logic (SDL) perspectives, sustainable service ecosystem design ideas, transformative value and meeting sustainable development goals (SDGs). This study explores these through volunteers’ lived experiences and their perceived health and well-being outcomes in the context of botanic gardens as health-care service settings.

A total of 3 UK botanic gardens and 84 volunteers between 22 and 87 years of age participated in this qualitative study. Volunteering stories were collected through emails, telephone exchanges, online and in-person interviews, free-flowing discussion and field observations. These were coded and analysed by using computer-assisted qualitative data analysis software, NVivo 14 Plus and Leximancer. Thematic analysis facilitated the mapping of well-being outcomes highlighting transformative value against existing health and well-being indices.

Insights extend knowledge into SDL, TSR and transformative value experienced by volunteers across three UK botanic garden service ecosystems. Environmental, organisational and personal factors, and physical, mental and social health outcomes are presented to emphasise transformative value experienced, especially in retiree volunteers. Theoretical contribution is in the form of empirical evidence to support and extend insights about transformative value and more so, significant epistemological change and meeting SDGs in botanic gardens. Results add to contemporary TSR on health-care-related well-being outcomes and ideas regarding sustainable service ecosystem design.

It is recommended that service research be extended across other botanic gardens, as well as other novel underexplored contexts for comparative studies of transformative value. Continued development and consideration of service designs as ongoing efforts to redefine and reimagine services marketing innovation for botanic gardens are recommended. Botanic gardens are complex service ecosystems worthy of rigorous service research to capture and measure the impact and outcome of ongoing work of the sector in advancing SDGs and having a transformative effect on individual and societal health and well-being.

This study highlights opportunities for greater area-based, coordinated, collaborative, multi-stakeholder services marketing partnerships for strategic sustainable service ecosystem design for the botanic gardens and health-care sectors. These sectors can make better use of service research and marketing to further innovate and co-develop health and well-being strategies, campaigns and opportunities to develop services to transform and influence positive health and well-being outcomes for people. Results reveal greater opportunities for collaborative partnership and services marketing’s role and practice for the ongoing vitality and viability of botanic gardens. Joint efforts would enable innovation on sustainable service ecosystem design, advancing SDGs and improving life on planet Earth.

Transformative value linked to newfound life experiences and meaning to life after retiring with a range of factors, and health and well-being outcomes were prominent. Social connections to the wider community were present, revealing links to a range of people who may not have traditionally had contact with botanic garden heritage and their strategic efforts. Therefore, it is services marketing opportunities for botanic gardens that hold one key to greater transformative value, sustainability and greater influence and impact on individual and societal health and well-being.

To the best of the author’s knowledge, this is the first TSR on botanic gardens as health-care service settings, resulting in a conceptual framework on transformative value and well-being outcomes in meeting SDGs. It extends insights on SDL, sustainable service ecosystem design and roles of marketing for the common good. Botanic gardens are unique research institutes, highly acclaimed for research, conservation, education and displays of special botanical collections, as well as providing health care, among other impactful SDG opportunities. This can be made more explicit through ecosystemic thinking, service research and integrated services marketing of botanic garden’ roles and contributions worldwide.

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

In their study of reciprocity in investigative interviews, Matsumoto and Hwang (2018) found that offering interviewees water prior to the interview enhanced observer-rated rapport and positively affected information provision. This paper aims to examine whether tailoring the item towards an interviewee’s needs would further enhance information provision. This paper hypothesised that interviewees given a relevant item prior to the interview would disclose more information than interviewees given an irrelevant item or no item.

Participants (n = 85) ate pretzels to induce thirst, engaged in a cheating task with a confederate and were interviewed about their actions after receiving either no item, an irrelevant item to their induced thirst (pen and paper) or a relevant item (water).

This paper found that receiving a relevant item had a significant impact on information provision, with participants who received water providing the most details, and significantly more than participants that received no item.

The findings have implications for obtaining information during investigative interviews and demonstrate a need for research on the nuances of social reciprocity in investigative interviewing.

The findings have implications for obtaining information during investigative interviews and demonstrate a need for research on the nuances of social reciprocity in investigative interviewing.

To the best of the authors’ knowledge, this study is the first to experimentally test the effect of different item types upon information provision in investigative interviews.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

Drawing on signalling and impression management theories, this study aims to examine a bidirectional association between managerial tone in earnings conference calls and financial performance.

The sample includes non-financial firms listed in the FTSE 350 index during the period 2010–2015. Managerial tone was measured using positive and negative keywords based on the Loughran-McDonald Sentiment Word Lists, while return on assets was used as a proxy for firms’ financial performance.

The findings indicate that current financial performance positively affects the managerial tone in earnings conference calls. Likewise, the results also show that there is a positive relationship between managerial tone in earnings conference calls and firms’ future financial performance.

The results have important implications for top management to use more virtual communication media (i.e. earnings conference calls) to continue managing their relationships with financial stakeholders and helping them better understand financial performance, especially in countries where holding such calls is not yet part of firms’ policy.

To the best of the authors’ knowledge, this is one of the first studies that explore the relationship between managerial tone in earnings conference calls and financial performance. Overall, this study contributes to managerial tone literature and holds significant theoretical and practical implications.

In this paper, the authors prove that the Douglas space of second kind with a generalised form of special (α, β)-metric F, is conformally invariant.

For, the authors have used the notion of conformal transformation and Douglas space.

The authors found some results to show that the Douglas space of second kind with certain (α, β)-metrics such as Randers metric, first approximate Matsumoto metric along with some special (α, β)-metrics, is invariant under a conformal change.

The authors introduced Douglas space of second kind and established conditions under which it can be transformed to a Douglas space of second kind.