John B. Cole, Christine F. Baes, Sophie A.E. Eaglen
et al.
ABSTRACT: When related animals are mated to one another, genetic defects may become apparent if recessive mutations are inherited from both sides of the pedigree. The widespread availability of high-density DNA genotypes for millions of animals has made it possible to identify and track known defects as well as to identify and track previously unknown defects that cause early embryonic losses. Although the number of known defects has increased over time, the availability of carrier information has been used to dramatically reduce the frequency of many disorders. The economic impact of known genetic defects in the US dairy cattle population has decreased by ∼2/3 since 2016, due largely to the avoidance of carrier-to-carrier matings. Effective population management requires robust systems for reporting new defects, identification of causal mechanisms, and development of commercially available tests. The United States and Canada depend on informal cooperation among many groups, including farmers, purebred cattle associations, genetics companies, and researchers, to identify emerging and causal defects. The structure of a collaborative system including all key sectors of the dairy cattle industry to support long-term population management is described. This review provides a comprehensive overview of the landscape surrounding genetic defects in dairy cattle. Topics covered include current defects of relevance to commercial dairy producers, trends in carrier frequencies over time, how best to manage these defects, strategies for detecting emerging diseases, and marketing and trade considerations.
Abstract This paper uses actuarial models to analyze the impact of tax authority's full responsibility collection of social security fee on the scope for reducing the pension insurance contribution rate. The study finds that: (1) The tax authority's full responsibility for collecting social security fees can increase the pension insurance collection rate by 20.3%-25.2%; (2) If the balance of fund income and expenditure and pension treatment remain unchanged, the contribution rate can be reduced by 1.34–2.22% in 2021–2030; (3) If the state-owned shares are transferred to enrich the social security fund, the contribution rate can be reduced by 3.58–4.22% in 2021–2030, and the payment burden of enterprises can be significantly reduced. In short, if the tax authority is fully responsible for collecting social security fees, it can provide conditions for lowering the contribution rate of pension insurance, but it needs to cooperate with other policies, such as the transfer of state-owned shares to enrich the social security fund. Chinese government should gradually clarify the tax authority's responsibility for collection to reduce the contribution rate of pension insurance and stimulate the market vitality.
The Białowieża Forest, a unique lowland forest complex in Central Europe, has undergone significant ecological changes in recent years, primarily due to a massive outbreak of the spruce bark beetle (Ips typographus L.). This outbreak, which peaked in 2016, caused extensive Norway spruce (Picea abies (L.) H. Karst) mortality, leading to major changes in forest structure and functioning. Ground-based forest inventory methods remain a fundamental and accurate source of ecological information. However, they are timeconsuming and relatively expensive, particularly when applied across large or inaccessible areas such as protected areas or wetlands. For this reason, alternative approaches are increasingly being explored – especially those leveraging remote sensing techniques, which allow for faster, spatially consistent and repeatable forest condition assessments. Satellite imagery and airborne laser scanning (ALS) data have proven to be powerful tools for monitoring forest condition at different spatial scales. This study investigates the feasibility of integrating high-resolution multispectral satellite imagery (Pléiades, 2 m spatial resolution) with ALS data (11 points/m²) to detect and map individual dead spruce trees in the Polish part of the Białowieża Forest. The methodological framework (Fig. 2) included pre-processing steps such as cloud masking, shadow elimination, canopy gap removal and segmentation of individual tree crowns from ALS data. These procedures aimed to enhance the classification accuracy and spatial alignment of the datasets. A supervised maximum likelihood classification was applied to the satellite imagery, distinguishing three vegetation classes: dead trees, conifers and broadleaves. The classification resulted in a high accuracy for dead trees (producer’s accuracy: 96.8%, user’s accuracy: 94.6%; Tab. 4, Fig. 3). A multistep integration method was used to detect individual dead trees by correlating spectrally classified pixels with tree crown segments derived from ALS data. Each crown was assessed for spatial proximity and spectral coverage of the classified dead pixels. By applying optimized thresholds for proximity and coverage, the algorithm identified individual dead spruce trees with a detection accuracy of 71.1% – true positives and a false positive rate of 22.0% (Tab. 9), validated against manually interpreted reference data. The final detection map indicated over 381,000 individual dead spruce trees in the forest area (Tab. 8, Fig. 5). The integration of ALS and satellite data proved to be effective, especially in forest compartments with lower structural and species complexity. However, limitations were observed in areas such as the Białowieża National Park, where diverse canopy structures, species heterogeneity and occlusion effects significantly reduced detection rates. In such areas, the satellite pixels often represent mixed spectral signals, which reduces the reliability of the classification for individual tree identification. The study emphasizes the utility of combining complementary remote sensing datasets to enable cost-effective, large-scale monitoring of forest health. It also highlights the temporal constraints of integrating data collected in different years, as forest conditions may change due to logging or vegetation regrowth. Although data mismatches can lead to errors, these can be mitigated by excluding known logged areas. This research contributes to the development of practical approaches for operational forest monitoring and supports sustainable forest management under increasing ecological pressures. The proposed method, when adapted to specific forest conditions, can improve forest inventory systems. Future work should explore improvements in tree crown segmentation, the use of multi-temporal satellite data and the integration of additional spectral indices or Deep Learning-based classification algorithms.
Kassem Danach, Abdullah Hussein Khalaf, Abbas Rammal
et al.
Effective data fragmentation is essential in enhancing the performance of distributed database management systems (DDBMS) by strategically dividing extensive databases into smaller fragments distributed across multiple nodes. This study emphasizes horizontal fragmentation and introduces an advanced machine learning algorithm, Red Fox Optimization-based Support Vector Machine (RFO-SVM), designed for optimizing the data fragmentation process. The input database undergoes meticulous pre-processing to address missing data concerns, followed by analysis through RFO-SVM. This algorithm efficiently classifies features and target labels based on class labels. The RFO algorithm optimizes critical SVM parameters, including the kernel, kernel parameter, and boundary parameter, leveraging the accuracy metric. The resulting classified data serves as fragments for the fragmentation process. To ensure precision in fragmentation, a Genetic Algorithm (GA) allocates these fragments to diverse nodes within the DDBMS, optimizing the total allocation cost as the fitness function. The proposed model, implemented in Python, significantly contributes to the efficient fragmentation and allocation of databases in distributed systems, thereby enhancing overall performance and scalability.
Pasty Asamoah, Daniel Zokpe, Richard Boateng
et al.
The increasing reliance on Generative Artificial Intelligence (GenAI) among students and knowledge workers poses significant risks and raises integrity concerns, prompting some institutions to impose bans on its use. With scant research on a guided framework, strategies, and checklists for the utilization of GenAI, we propose a framework based on the expertise acquisition model, information retrieval theory, and deontological ethics theory, emphasizing the need for domain knowledge, query capabilities, and ethical acumen (DEQ). Using a ‘thing’ ethnography methodology with ChatGPT, we validated our framework, highlighting the importance of these competencies to mitigate risks, including the generation of factually incorrect and plagiarized content. Our findings suggest that while GenAI can drive innovation, its content should be used as a guiding tool to enhance critical thinking and reasoning, ultimately helping knowledge workers and students avoid plagiarism and maintain academic integrity. We discuss this novel framework and provide avenues for extending this study.
In recent years, additive manufacturing has emerged as a revolutionary technology, opening up new design possibilities. In this article, we examine the redesign and optimization of a wrist splint topology for additive manufacturing techniques. By taking advantage of the capabilities of this advanced manufacturing method. This study proposes to improve the functionality, ergonomics and overall performance of the wrist splint. The design process includes customized measurements based on CT scan or 3d scan, 3D modeling, and simulation to ensure optimal fit and structural integrity of the device. After finite element analysis, we evaluate the mechanical properties of the topologically optimized design. The results confirm that the proposed approach offers significant improvements in raw material consumption while maintaining the strength and ventilation required for patient comfort. Compared with the solid splint, the weight of the optimized wrist and hand splint is reduced by 46%.
Systems engineering, Management information systems
The software development and management (SDM) practice helps organizations to ensure that their software products are developed methodically and delivered in accordance with the stakeholders’ requirements. The main purpose of the SDM is to map out the management tasks and sequences them rationally. Software must be developed in a systematic manner to ensure that it is developed on time, within budget and with all functional and non-functional capabilities. SDM practice plays a significant role in ensuring that the software is developed in a systematic manner, traditionally using the models such as waterfall model, agile model, and V-model. There are many modern software development models such as DevSecOps that extends the capability of existing models and makes it more cloud friendly. Web-based development and delivery of software applications as well as Low-Code/No-Code (LCNC) are becoming increasingly popular and valuable due to availability of tools and technology for SDM. Selecting the right model for developing software ensures that all the disciplines of that model are implemented and monitored during the SDM process. Hence, SDM practices strategically manage how software is being developed, tested, and deployed and creates the business value for the stakeholders.
Advances in information technology have dramatically enhanced mobile phones. Power capacity is one of the most significant limitations of a mobile phone. As a result, efficient energy management in such devices is critical everywhere. The goal of this research is to find a way to charge electronic devices wirelessly using radio frequency (RF) electromagnetic (EM) waves (Rectenna using energy detection-based spectrum sensing). Mechanical deformations cause frequency detuning, which lowers the effectiveness of antennas and rectennas that would otherwise allow wireless communication and RF energy harvesting in the far field. A rectenna based on a stretchable multiband antenna is designed as a self-powered system to perform reliably and integrate RF power received across its multiband despite mechanical deformations. Depending on what the battery needs, the proposed multiband antenna will work at 900 MHz, 1800 MHz, 2100 MHz, and 2.45 GHz as both an RF transducer and an RF energy harvester. Depending on the received RF power density (high), the receiving RF wave will be utilized for both communication and RF energy harvesting (RF-EH) when the battery's current voltage is less than 20% (referred to as “low voltage”). Otherwise, the received RF wave will be used only for RF-EH. The installed multiband rectifiers function perfectly in terms of efficiency and bandwidth. This proposed technique would reduce the charging crisis by 60–90% depending on the location of the mobile phone or receiver of ambient EM signals. This paper could help researchers in the field of RF energy-based wireless charging systems.
Organizasyonun en önemli kaynağı olması sebebiyle bilginin en iyi şekilde yönetilmesi ve bunun için teknolojik araçlardan yararlanılması günümüzde önemli bir meseledir. Bu çalışma, kamu kurumlarında bilgi yönetiminin mevcut durumunu ve yapılması gerekenleri tespit edebilmek için Türkiye’deki sekiz Bakanlığın Bilgi İşlem Daire Başkanları (BİDB) ile görüşmeler yapılarak hazırlanmıştır. Olgu bilim desenine uygun olarak düzenlenen ve verilerin betimsel içerik analizine tabi tutulduğu çalışma, bakanlıklardaki bilgi yönetimi unsurları ve teknoloji ilişkisi üzerine odaklanmıştır. Bilgi yönetimi unsurları; insan, organizasyon, bilgi varlığı/içerik, süreç ve teknoloji olmak üzere beş ana başlık altında gruplandırılmıştır. Çalışma kapsamındaki nitel araştırma sonuçlarına göre, Türkiye’deki kamu kurumlarında genel olarak; bilgi yönetiminin muğlak bir konu olduğu, bilgi yönetimine ilişkin stratejik plan olmadığı, kurumlarda bir bilgi yönetimi (BY) organizasyonunun bulunmadığı, bilgi yönetimi rehberine ihtiyaç olduğu, üst yönetimin enformasyon teknolojilerine yönelik desteğinin tam olduğu, teknolojik araçların belli oranda mevcut olduğu ancak bilginin yönetilmesi odağında yapılandırılmadığı ve bütünleşik bir sistem yapısında olmadığı ortaya çıkmış, bu bağlamda öneriler sunulmuştur.
The implementation of programmes to diversify the production of enterprises of defence industry complex is a process with many risks, to minimise which guarantees for the sale of civilian products are required. In this regard, the urgency of the task of mastering competencies that allow to successfully implement this process in offline and online environments increases. A significant part of such competencies is related to marketing in general and marketers using special technologies to form a product portfolio, assess its market capacity taking into account competition, develop strategies and tactics for introduction to new markets, including pricing, distribution, sales and promotion.On the basis of traditional marketing competencies, ways of adapting the functionality of marketers for the development and implementation of projects for the introduction of new products of enterprises of defence industry complex to new (civilian) markets are proposed, directions for solving marketing problems of creating and scaling startups, market priorities for the formation of special ecosystems for the diversification of the production process have been presented. The functionality of marketing positions has been proposed: marketing (web) analyst, product manager, distribution and sales specialist, marketing communications specialist, who should be employed in new marketing structures. At the first stage, it is proposed to enter new markets (for enterprises of defence industry complex) within the framework of digital structures – from electronic trading platforms and/or marketplaces. This approach will provide cost savings and seems more adequate for successful competition with existing players actively working in the network. At the next stages, it is advisable to create unified ecosystems grouped according to the product principle, in which, in addition to goods, related services from existing organisations engaged in training, design, operation, supply of raw materials, materials and semi-finished products will be offered.
The object of this article is to present a concise study of the state of scientific literature regarding the relationship between the Environmental, Social and Corporate Governance (ESG) factors and the financial performance at a company-based level, taking into account the sustainable paradigm. According to scientific literature, we expect that the more socially responsible companies are interested in the well-being of the stakeholders (customers, employees, members of the supply chain, and the community) as much as in that of their shareholders.
Management. Industrial management, Management information systems
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173786.
In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spoken fluency as the main parameters of scoring. In order to improve the accuracy of keyword scoring, this paper uses synonym discrimination method to identify the synonyms in the examinees' answer keywords. At the sentence level, attention LSTM model is used to analyze examinees' translation of sentence general idea. Finally, spoken fluency is scored based on tempo/rate and speech distribution. The final translation quality score is obtained by combining the weighted scores of the three parameters. The experimental results show that the proposed method is in good agreement with the result of manual grading, and achieves the expected design goal compared with other methods.
COVID-19 is a disease caused by coronavirus 2 (SARS-CoV-2). This virus belongs to the group of viruses that infect the respiratory system. Furthermore, the rapid rate of spread has made several countries implement a policy of implementing a lockdown to prevent the spread of this virus. In Indonesia, the government implemented the policy of "Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM)" to suppress the spread of this virus. Based on data from the Task Force for the Acceleration of Handling COVID-19 of the Republic of Indonesia, the number of confirmed positive cases as of August 6, 2021 is 3,568,331 people with a death toll of 102,375. The existence of the COVID-19 vaccine is currently under threat, this is due to the emergence of new variants of the COVID-19 virus. The RT-PCR method as the main standard used throughout the world in detecting this virus has a fairly high specificity, which is around 95 percent, which is a manual process that can only be done by health workers. In addition, this test takes a long time and the number of testing facilities is limited. The presence of X-ray scanning machines in hospitals can be used to increase the availability of COVID-19 testing facilities. The thoracic x-ray image generated by the scanner can be used to detect the virus easily, quickly and precisely. In this study, a website-based system was designed to detect the COVID-19 virus in thoracic x-ray images using a convolutional neural network algorithm. The results obtained show that this system is able to classify chest x-ray images into three classes, namely COVID-19, Viral Pneumonia, and normal. The accuracy value obtained is 89.6% and the F1 value is 87.9%.
Christina I.V. Gumpo, Tinashe Chuchu, Eugine T. Maziriri
et al.
Background: Online social networking websites have revolutionised communication in the postmodern era of today. Specifically, Instagram has witnessed a phenomenal growth, and the site is particularly popular with young consumers in South Africa. Instagram is seen by this cohort as a source of information to determine their tourism destinations.
Objectives: This research examined how perceived usefulness of Instagram, perceived ease of use of Instagram and perceived credibility of Instagram influence attitude towards the use of Instagram, intent on using Instagram and actual use of Instagram to identify tourist destinations amongst young consumers.
Method: A survey questionnaire was administered to 349 young student consumers who were based in the Johannesburg Metropolitan area of the Gauteng province, South Africa. A structural equation modelling procedure was utilised in testing the proposed relationships.
Results: The results indicate that all the hypotheses suggested have been positive and significant. It is worth noting that there was the strongest connection between attitude towards Instagram for identifying travel destinations and intention to use Instagram for identifying travel destinations.
Conclusion: Tourism organisation marketers may be able to devote time in researching the practicality of using Instagram to increase awareness of tourist locations.
Management information systems, Information theory