Search results

This paper aims to investigate whether official development assistance (ODA) inflows to developing countries (lower-middle and low income) can cause the symptoms of Dutch disease or not.

This study applies the methodology of dynamic panel data estimation with a one-step system generalized methods of moment (GMM) for the sample of 59 developing countries from 2001 to 2019.

The results indicate that ODA (as a percentage of gross domestic product (GDP)) rises by 1%, the real effective exchange rate (REER) appreciates by 0.252%. This finding reveals that these selected developing countries have faced the symptoms of Dutch disease. The countries with the higher ODA ratio have a higher effect of the Dutch disease, and the managed floating exchange rate regime is the lowest impacted, when compared to the fixed and flexible exchange rate.

The selected countries are recommended to use ODA inflows right and efficiently. These ODA inflows should be invested in productive sectors or support for production rather than in consumption. The managed float exchange rate regime is applied to reduce the symptom of Dutch disease for the selected countries. The good cooperation of monetary and fiscal policies is important to absorb the huge ODA inflow and sterilize the adverse effects of the disease.

The paper contributes to the literature and empirical of the Dutch disease. An adverse effect of the huge ODA inflow to the developing countries appreciated of the real exchange rate and caused the symptom of the dutch disease.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0777

Postulating that individuals exposed to the threat of contagious diseases respond oversensitively toward other people, the current research aims to investigate its impact on consumers’ preferences for human images—human presence—in product packaging.

Five independent online and offline experiments were conducted. Studies 1, 2a, and 2b employed a three-group (threat: contagious vs. control vs. noncontagious) between-subjects design to investigate the main effect and its underlying mechanism. To further examine the moderation effects, Study 3 used a 2 (threat: contagious vs. control) × 2 (product feature: basic vs. antibacterial) between-subjects design, and Study 4 employed a 2 (threat: contagious vs. control) × 3 (human type: non–human vs. human–adult vs. human–baby) between-subjects design.

Studies 1, 2a, and 2b demonstrate that consumers facing the threat of contagious diseases tend to avoid social interaction, leading to a lower preference for products featuring human presence (vs. non-human presence). Studies 3 and 4 contribute to our hypothesized process by providing boundary conditions. Specifically, when the product incorporates an antibacterial function (Study 3) and the packaging depicts a baby (Study 4), the existing effect can be attenuated.

Despite the prevalence of experiencing epidemics and pandemics, little work has examined how threatened consumers respond to product packaging. The present research addresses this gap by exploring consumers' preferences for products featuring human presence on their packaging. Furthermore, this research contributes to the practical understanding of consumer choices by identifying product features and human types as moderating factors.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.

Functional foods have been marketed as promoting health and reducing the risk of disease. While the market of functional foods is increasing across the globe, little is known about how actual and subjective health status are related to functional food choices and existing research evidence is inconsistent. Therefore, the purpose of this paper is to systematically explore the relationship between functional food choices and perception related dimensions vs medical dimensions.

The study used data collected from a large-scale mail survey in Japan (N = 8,368) and a representative Internet survey in Lithuania (N = 900). It used structural equation modeling (SEM) to test the proposed conceptual model.

The general results indicated that functional foods could be used to maintain one’s subjective health status – the frequency of using functional food products was positively related to consumers’ subjective health status (p = 0.04). However, if consumers were experiencing health-related issues (self-reported disease symptoms or current medical treatment), there was no systematic relationship between such experience and the usage of functional food products.

To the best of the authors’ knowledge, this study is among the first to systematically analyze the relationship between subjective health status, self-reported disease symptoms, current medical treatment and the frequency of using different functional food product groups. The findings indicated that it is important to simultaneously consider different underlying factors, such as specific to functional food targeted disease symptoms and specific food product groups, which contributed to a more thorough understanding of functional food consumption.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

This study conducted a review following the guidelines of Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Searches were conducted in databases PubMed, Scopus, Web of Science, Cochrane Library, cumulative index to nursing and allied health literature, PsycINFO, China national knowledge infrastructure Wanfang, VIP Database for Chinese Technical Periodicals and SinoMed from inception to February 12, 2023.

A total of 14 studies (describing 14 serious games) for improving the mental health of adolescents with chronic diseases were included. Of all the included games, 12 were not described as adopting any theoretical framework or model. The main diseases applicable to serious games are cancer, type 1 diabetes and autism spectrum disorder. For interventional studies, more than half of the study types were feasibility or pilot trials. Furthermore, the dosage of serious games also differs in each experiment. For the game elements, most game elements were in the category “reward and punishment features” (n = 50) and last was “social features” (n = 4).

Adolescence is a critical period in a person’s physical and mental development throughout life. Diagnosed with chronic diseases during this period will cause great trauma to the adolescents and their families. Serious game interventions have been developed and applied to promote the psychological health field of healthy adolescents. To the best of the authors’ knowledge, this study is the first to scope review the serious game of promoting mental health in the population of adolescents with chronically ill. At the same time, the current study also extracted and qualitatively analyzed the elements of the serious game.

Millets are underused crops that have the potential to withstand harsh environmental conditions. Recent research has proved immense nutritional benefits associated with millets which have increased their utilization to some extent but yet their sole potential is left to be exploited. Different millet varieties have exceptional nutritional and nutraceutical properties which can ameliorate even the deadly conditions of cancers. They have significant protein composition ranging from 10% to 12% which possess effective bioactive potential. Protein hydrolysates containing bioactive peptides have been evaluated for their therapeutic effects against a variety of diseases. This review aims to discuss the bioactive potential of different millet protein hydrolysates to encourage research for development of effective natural therapeutics.

The present article elaborates on effective studies on the therapeutic effects of millet protein hydrolysates.

Several effective millet peptides have been reported for their therapeutic effect against different diseases and their antioxidant, anti-inflammatory, anticancer, antimicrobial and antidiabetic effects have been investigated.

This review focuses on millet bioactive peptides and their significance in treating variety of diseases. Thus, will further encourage research to explore the novel therapeutic effects of millet proteins hydrolysates which can eventually result in the development of natural and safe therapeutics.