bluuebunny/crossref_metadata_2025 huggingface dataset:
How can elements of raga music influence a signature sound?
Authors: Harsha Jerome Senaviratne
Published: September 2024

This master's thesis is on incorporating elements of traditional music, specifically Raga Bhairav and Bhairavi, into contemporary music production. The thesis investigates how these elements can enhance the signature sound of modern compositions and impact the composer's artistic expression. The research questions explore the advantages and challenges of integrating traditional music elements into modern compositions and advocating cultural exchange and understanding. The thesis will use artistic development work, which combines artistic endeavors and scholarly research, as its method. The aim is to ensure the highest quality of artistic practice, meeting the same quality standards and academic achievements as other scientific activities. This study will provide insight into the potential impact of raga scales on modern compositions and contribute to the ongoing discourse on incorporating traditional music into contemporary music production.
Songwriting and AI
Authors: Celine Lyng
Published: September 2024

In this thesis, I explore and research songwriters' experience of using artificial intelligence as a songwriting tool. To do so, three participants have explored the use of two different artificial intelligence tools in their songwriting session, in collaboration with me. One of the tools is used to inspire lyrical writing and one is to create a starting point for chord progressions. To gather data, I have had three collaborative songwriting sessions, where the artists and I use the tools whilst writing a song for their artist project. As well as the sessions, there was held six qualitative interviews, two per artist. The first one was for documenting their prior experience and expectations. The second interview was held after they had received a produced demo. This was used to ask about their experiences writing songs with artificial intelligence. We also talked about the song related to ownership. The thesis questions whether artificial intelligence will make one more productive, too productive, or even if the tools can be perceived as distracting.

In addition to using Artificial Intelligence in three different songwriting sessions, I have researched the ethical dilemmas with using AI, such as copyright infringements, and discussing options for opting out of having one’s work be a part of the training of an AI.

My personal journey highly inspires the thesis through using artificial intelligence for my musical project. By using the same process as the three participants are taking part in, my debut EP, Sugarcoated, was created.

This thesis is approved by SIKT (816719).
Advancing crack segmentation detection: Introducing AAMC-Net algorithm for image crack analysis
Authors: Wang Xiaofang , Liu Chenfang , Hou Junliang , Zhou Liang
Published: September 2024

This research paper presents an innovative solution to address the challenges of poor detail detection effectiveness and prolonged training time in image segmentation. The proposed approach leverages the Adaptive Attention Multiscale Convolution Network (AAMC-Net), incorporating a multi-scale dilated convolution VGG L network for feature extraction and a deconvolution method for image segmentation. Extensive experiments demonstrate the superior performance of the proposed algorithm concerning intersection over Union (IOU), accuracy, precision, recall, F1, average training efficiency, and segmentation efficiency when compared to several traditional algorithms. On average, the proposed algorithm achieves remarkable improvements of 3.9%, 3.1%, 1.7%, 4.9%, 17.9%, 14.8% ,and 20.2% in these metrics. Moreover, the enhanced algorithm exhibits notable advantages in detail processing and real-time image segmentation detection.
Semantic feature-based test selection for deep neural networks: A frequency domain perspective
Authors: Jiang Zhouxian , Li Honghui , Tian Xuetao , Wang Rui
Published: September 2024

While deep neural networks (DNNs) have great potential for applications in security and safety-critical domains, their limited robustness to adversarial samples and out-of-distribution (OOD) samples raise significant concerns. In the software engineering community, significant efforts have been devoted to devising testing techniques that verify the robustness of DNNs. This paper investigates semantic feature-based test selection for DNNs from a frequency domain perspective and propose a novel method called SaFeTS. Specifically, we leverage saliency detection techniques, such as Fourier Phase Transform to extract semantic features from test cases. These features are then clustered to select diverse test cases to evaluate the robustness of DNNs and model retraining. Experiments on CIFAR-10 and SVHN datasets demonstrate that SaFeTS exposes more varied model errors compared to baseline methods. Further, retraining with SaFeTS-selected samples significantly improves adversarial and out-of-distribution robustness over state-of-the-art test selection methods.
AI large models bring great opportunities to reusable design of cad software
Authors: Sun Yunlei , Yan Bingyi , Shao Zhaotong
Published: September 2024

With the rapid development of artificial intelligence (AI), large models have achieved significant breakthroughs in general-purpose domains. However, their application in computer-aided design (CAD) software is still in its early stages. Reusable design is crucial for improving efficiency and innovation in CAD systems. This paper reviews progress in rule-based reasoning (RBR) and case-based reasoning (CBR), two prevailing techniques for reusable design. RBR represents expert knowledge as rules but lacks self-learning capabilities. CBR draws on prior cases to solve new problems but relies heavily on surface empirical knowledge. Recent advances in large AI models provide new opportunities to enhance reusable design, thanks to superior language and reasoning abilities. However, adapting large models to effectively leverage CAD-specific design knowledge presents open challenges. To advance progress in this area, this paper analyzes the potential impacts of large models on improving knowledge acquisition, case retrieval, rule representation, and reasoning explain ability for hybrid CBR-RBR systems, and proposes a reusable design framework combing large language models, knowledge graphs, and databases to realize more intelligent and interpretable reuse. This review synthesizes key developments in RBR, CBR, and large AI models, highlighting promising directions for advancing reusable design in CAD software. The integration of reasoning techniques with large models, opening promising new directions for computer-aided engineering enhanced by artificial intelligence, as well as lays the foundation for more efficient, innovative, and sustainable engineering design.
A hybrid GA-powell algorithm for geometric constraint solving
Authors: Yunlei Sun , Yucong Li
Published: September 2024

Geometric constraint solvers are crucial for computer-aided design (CAD), and their algorithms are the focus of research. Current geometric constraint solvers based on traditional numerical methods lack support for multi-solution problems, so we propose a hybrid algorithm that combines the genetic algorithm, which is good at global convergence, and Powell?s method, which is good at local refinement, to address the limitations of traditional numerical methods in geometric constraint solving algorithms (sensitivity to initial values, susceptibility to falling into local optimums, and being only able to obtain a single solution) and the challenges of intelligent optimization algorithms (complex parameter tuning, slow convergence and low accuracy). Our method has a large accuracy improvement over the comparison method in basically all test cases, and its efficiency can also meet the needs of real geometric constraint solving scenarios. This research provides new insights into the design of geometric constraint solving algorithms, offers a fresh perspective on improving the performance and generality of solvers, and contributes to technological advances in the CAD field.
Maritime trajectory mining: An automatic zones of interests discovery and annotation framework⋆
Authors: Ghannou Omar , Thuillier Etienne , Boucelma Omar
Published: September 2024

As global traffic continues to grow, the identification of areas of particular significance, known as Zones of Interest (ZOI), becomes crucial for optimizing transportation systems and analyzing mobility patterns. In the maritime domain, effective ZOIs discovery is essential for enhancing route planning, improving safety measures, and managing resources efficiently. Within the context of trajectory mining, these ZOIs provide valuable insights into movement behaviors and operational efficiencies. In this paper, we present a framework for discovering and annotating ZOIs within maritime trajectories. The proposed approach involves processing raw positional data to initially identify candidate ZOIs, which are subsequently refined using contextual information. By leveraging real georeferenced vessels trajectories, collected from thousands of commercial ships, this framework proposes a structure of elements that will be implemented as part of the TNTM French project. While this research contributes to maritime field by providing a method for ZOIs discovery and annotation, it can be generalized to various application domains that may leverage of mobility data analytics.
GCN-LSTM: multi-label educational emotion prediction based on graph Convolutional network and long and short term memory network fusion label correlation in online social networks
Authors: Liu Zhiguang , Li Fengshuai , Hao Guoyin , He Xiaoqing , Zhang Yuanheng
Published: September 2024

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects.
A multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for Esnglish text classification
Authors: Wen Tianying
Published: September 2024

The traditional methods of English text classification have two disadvantages. One is that they cannot fully represent the semantic information of the text. The other is that they cannot fully extract and integrate the global and local information of the text. Therefore, we propose a multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for English text classification. In this method, the character-level vector and word-level vector representations of English text are calculated using a pre-training model to obtain a more comprehensive text feature vector representation. Then the multi-head attention mechanism is used to capture the dependencies in the text sequence to improve the semantic understanding of the text. Through feature fusion, the channel features are optimized and the spatial features and time series features are combined to improve the classification performance of the hybrid model. In the stage of network training, the weighted linear combination of maximum Shannon entropy and minimum cross entropy is used as the return degree evaluation function of the bee colony algorithm, and the scale factor is introduced to adjust the solution search strategy of leading bees and following bees, and the improved artificial bee colony algorithm is combined with the classification network to realize the automatic optimization and adjustment of network parameters. Experiments are carried out on public data set. Compared with traditional convolutional neural networks, the classification accuracy of the new model increases by 2% on average, and the accuracy of data set increases by 2.4% at the highest.
Stacked denoised auto-encoding network-based kernel principal component analysis for cyber physical systems intrusion detection in business management
Authors: Song Zhihao
Published: September 2024

At present, the network data under the environment of industrial information physical system is larger and more complex. Traditionally, feature extraction by machine learning is cumbersome and computation-intensive, which is not conducive to anomaly detection of industrial network data. To solve the above problems, this paper proposes a stacked denoised auto-encoding network based on kernel principal component analysis for industrial cyber physical systems intrusion detection. Firstly, a novel kernel principal component analysis method is used to reduce the data feature dimension and obtain a new low-dimension feature data set. Then, a multi-stacked denoised auto-encoding network model is used to classify and identify the data after dimensionality reduction by voting. Experimental results show that the proposed method has better classification performance and detection efficiency by comparing the state-of-the-art intrusion detection methods.
A novel image enhancement method using retinex-based illumination map weighted guided filtering
Authors: Chen Su , Li Dahai
Published: September 2024

Halo artifact, edge detail loss and noise amplification are the main problems in low illumination image enhancement, an image enhancement algorithm combining Retinex and illumination map weighted guided filtering is proposed. The traditional defogging physical models only enhance the images based on dark channels prior, resulting in different depths of field in local areas, and it can lead to some problems such as image overexposure and halo artifacts. To solve this problem, the method of combining light and dark channels is adopted to calculate the atmospheric light value and transmittance. For the problem that edge information is easily lost, the illumination gradient domain weighted guided filtering is utilized to improve the thinning transmittance. Experimental results with the proposed method have obvious improvement in denoising, halo elimination, brightness adjustment and edge preservation in the low-illumination image under different conditions.
Multi-object real-time tracking for intelligent breeding of animal
Authors: Wang Fei , Xia Bin , Pan Liwu
Published: September 2024

Animal intelligent breeding utilizes advanced technology and intelligent systems to monitor, analyze, and optimize animal growth environments and management, which can enhance breeding efficiency and animal health levels. In this paper, we propose a new multi-object real-time tracking within deep framework for intelligent breeding of animal (MRT-IB), which consists of semantic feature extraction module, center point prediction module, and object and trajectory calibration module. MRT-IB reduces the difficulty of modeling animal trajectories by performing animal detection on consecutive frames, resulting in higher robustness in real farming scenarios compared to traditional multi-object tracking schemes that directly model animal motion trajectories.
Passive Investment and Share Repurchases
Authors: Bratten Brian , Huang Meng , Payne Jeff L.
Published: September 2024

SYNOPSIS

We investigate the association between passive investment and share repurchases, both of which are at historically high levels and the subject of increased scrutiny. We first document that firms with more passive ownership exhibit higher levels of repurchase activity. Next, and more importantly, we find that firms with passive ownership conduct more “suspect” repurchases that result in reported earnings that meet or beat analysts’ earnings forecasts, and this effect is more pronounced for firms with performance-based equity compensation incentive plans. We also find that repurchase activity more negatively affects firms’ future employment, capital expenditures, and performance when the firm has more passive owners. We conclude that passive investment is associated with an increase in the quantity and a reduction in the quality of firms’ repurchase activities. Our study provides evidence that passive institutional investment may allow opportunistic management behavior rather than serving the monitoring role traditionally associated with institutional ownership.

Data availability: Data are available from the public sources cited in the text.

JEL Classifications: M41; G32; G35.
Efficacy of planned moderate to severe torque changes in mandibular central incisors with an initial series of Invisalign aligners: a retrospective cohort study
Authors: Rajan Nancy , Weir Tony , Meade Maurice J.
Published: September 2024

Objectives

To determine whether the achieved mandibular central incisor lingual root torque (LRT) changes matched the planned changes after treatment with an initial series of Invisalign aligners when ≥10° of torque change was planned.

Materials and Methods

A sample of adult patients who underwent treatment with the Invisalign appliance between 2013 and 2021 were evaluated. The pretreatment, planned, and achieved posttreatment digital models were measured using Geomagic Control X metrology software. The effect of age, number of prescribed aligners, sex, the presence of power ridges (PRs), and differing weekly wear protocols (WPs) in relation to the achieved LRT changes were determined.

Results

Seventy mandibular central incisors from 35 patients satisfied inclusion criteria. The accuracy of the achieved lower incisor LRT compared with that planned was 58.2%. Underexpression of planned torque changes was observed in most incisors (N = 66; 94.3%), with a clinically significant shortfall (≥5°) observed in 68.6% (N = 42) teeth. Patient age, sex, the WP, or the presence of PRs did not influence the differences between planned and achieved outcomes (P > .05). The prescribed number of aligners was influential in the difference between the planned and achieved torque outcomes (P < .01).

Conclusions

Underexpression of mandibular central incisor root torque was observed in most incisors in patients when ≥10° change in LRT was planned. Lower incisor LRT was not significantly affected by the presence of PRs or differences in WPs.
A TRAJETÓRIA DO MST: CONQUISTAS, DESAFIOS E A INTEGRAÇÃO AOS MERCADOS INSTITUCIONAIS
Authors: Souza Andre Luiz de , Lazzaretti Miguel Ângelo
Published: September 2024

O artigo examina a trajetória do Movimento dos Trabalhadores Rurais Sem Terra (MST) no Brasil, desde suas origens até os desafios atuais. Destaca as conquistas do movimento, as mudanças estratégicas e seu compromisso contínuo com a justiça social e a reforma agrária. Analisa a transição do MST de estruturas coletivistas de negociação para a integração em mercados institucionais, evidenciando o fortalecimento da produção, o aumento da renda e a promoção da autonomia das famílias assentadas. Esses fatores são cruciais na luta pelos direitos à terra e no empoderamento dos assentamentos da reforma agrária. O MST possui uma longa história de luta pela transformação social e pela democratização do acesso à terra. Após o assentamento, a luta persiste por melhores condições de produção e geração de renda. As políticas públicas emancipatórias das duas primeiras décadas do século XXI foram essenciais para fortalecer as cadeias produtivas e abrir caminhos para a formação de mercados da reforma agrária. Conclui-se que os mercados são essenciais para a reprodução das famílias assentadas e que sua auto-organização promove mercados alternativos que tendem a desenvolver a autonomia e o controle das cadeias produtivas. As políticas públicas emancipatórias são importantes para o fortalecimento da produção e a criação de novos mercados.
O que é a doença de Muller-Weiss?
Authors:   Revista de Medicina Desportiva informa, Ribeiro Basil
Published: September 2024

“Rafael Nadal é considerado um dos melhores tenistas de sempre, tendo ganho 22 torneios do Grand Slam até maio de 2024. Poucos saberão que é portador de uma doença crónica no pé desde 2005, desde há quase 20 anos! Foi nesse ano, no Masters de Madrid, que se ficou a saber depois de ele perder o jogo contra o canadiano Denis Shapovalov. Durante o terceiro set ele mal se mexia no court devido às dores. A doença de Muller-Weiss é uma doença idiopática rara que afeta o osso navicular, um osso muito importante para a manutenção do arco do pé. Existe o colapso progressivo do osso, com fragmentação, com consequente dor e deformidade. Esta destruição óssea leva a artrite talonavicular assimétrica com a cabeça do talus a deslocar-se lateral e plantarmente, o que orienta a articulação subtalar para varo...”
Prevenção – Resumo / summary of: The association between daily step count and all-cause and cardiovascular mortality: a meta-analysis.
Authors:   Revista de Medicina Desportiva informa, Ribeiro Basil
Published: September 2024

Summary of the paper: Abstract - Aims: There is good evidence showing that inactivity and walking minimal steps/day increase the risk of cardiovascular (CV) disease and general ill-health. The optimal number of steps and their role in health is, however, still unclear. Therefore, in this meta-analysis, we aimed to evaluate the relationship between step count and all-cause mortality and CV mortality. Methods and results: … Conclusion: This meta-analysis demonstrates a significant inverse association between daily step count and all-cause mortality and CV mortality with more the better over the cut-off point of 3867 steps/day for all-cause mortality and only 2337 steps for CV mortality.
Anastomotic Integrity in Colorectal Surgery for Colorectal Cancer: The Role of Protective Ileostomy and the Corner Effect
Authors: Meneses do Rêgo Amália Cinthia , Araújo-Filho Irami
Published: September 2024

Anastomotic leakage is one of the most significant complications following low colorectal surgery for rectal cancer, with profound implications on morbidity, mortality, and long-term oncological outcomes. Protective ileostomy has been widely adopted to mitigate these risks by diverting fecal flow away from the anastomotic site, allowing it to heal without exposure to bowel contents. However, recent discourse challenges the routine use of protective ileostomies, suggesting a more selective approach based on patient-specific risk factors, surgical techniques, and intraoperative findings. This review examines the multifactorial nature of anastomotic failure, focusing on the impact of patient comorbidities, neoadjuvant therapies, surgical expertise, and technological advances, such as fluorescence angiography with indocyanine green (ICG), to assess tissue perfusion. The concept of the “corner effect” in low colorectal anastomoses, where tension and ischemia at stapled junctions predispose to leakage, is discussed. The review also explores the implications of long-term ileostomy use, including its association with Low Anterior Resection Syndrome (LARS) and delayed recovery. Further, we address gaps in the literature regarding the timing of ileostomy reversal, the influence of neoadjuvant therapy on anastomotic healing, and the potential role of bioabsorbable materials in reinforcing anastomoses. The review calls for a more personalized approach to protective ileostomy use, integrating patient characteristics, surgical expertise, and technological advancements to optimize outcomes in colorectal cancer surgery.
Smart Cashier with Self Service System at Supermarket
Authors: Sugiarta Zakhimi Anan , Muskhir Mukhlidi , Luthfi Afdal
Published: September 2024

The increasing population growth in Indonesia also affects the need for a comfortable and safe shopping place. The increasing number of consumers at the same time causes lines, especially in the payment section. The purpose of this research is to try to develop a cashier with a self-service method to help speed up the payment process and reduce lines. This research uses a GM66 scanner to scan barcodes on product items to be purchased. While the 3200-DB TCS Sensor is used to detect the nominal money paid. Information on the price of goods that have been scanned is displayed on the LCD screen. After payment is complete, the shopping receipt will be printed. In the results of this study, a tool was obtained that can be used to automatically sum up the price of groceries. Additionally, this tool can also facilitate transactions for payment of groceries. However, in this study, the new payment transaction process can only use $50 bills, and there is no system for returning change.
Inovasi Peralatan bayi: Ayunan Bayi Otomatis Berbasis Arduino Mega dengan Fitur Monitoring Tangisan Bayi melalui Aplikasi Blynk
Authors: Gumelar Salsabila Tri , Kristianti Veronica Ernita , Utami Priska Restu
Published: September 2024

Pemanfaatan perkembangan teknologi dapat dilihat pada peralatan rumah tangga yang mulai canggih dengan menerapkan sistem otomasi. Banyak kendala yang terjadi pada ibu  ketika menidurkan  bayinya, oleh karena itu untuk membantu meringankan tugas ibu maka perlu adanya alat otomatis yang dapat menidurkan bayi. Salah satunya adalah alat pengayun otomatis yang dapat bergerak sendiri tanpa tarikan atau dorongan dari manusia. Prototipe ini bekerja jika terdeteksi air maka sensor raindrop akan memberi informasi ke aplikasi monitoring bahwa bayi sedang mengompol, sensor suara jika mendeteksi suara tangisan bayi akan mengirimkan sinyal ke aplikasi monitoring bahwa bayi sedang menangis Kemudian motor stepper akan menggerakkan keranjang bayi setiap terdeteksinya suara tangisan bayi dan DFPlayer akan memutar musik untuk menenangkan bayi sementara, sebelum ibu menghampiri bayi nya. Hasil pengujian sensor suara bekerja sesuai dengan perancangan. Sensor raindrop dapat mendeteksi air dengan rentang rata-rata dari 1 ml – 50 ml dan sensor mic dapat mendeteksi suara tangisan bayi yang memiliki rata – rata 72 – 90 dB dan mengaktifkan ayunan serta musik selama 30 detik.
Implementasi Pengenalan Musuh Berbasis Image Processing Pada Robot Penjaga Di Daerah Pos Pengamanan Di Wilayah Papua
Authors: Muzakki Arif , Widiatmoko Dekki , Irwansyah Achmad Afif , Syafaat Mokhammad , Sridaryono Aguk
Published: September 2024

Era Revolusi 4.0 telah memberikan perubahan disegala aspek kehidupan termasuk pada teknologi Militer yang saat ini telah menghadirkan beberapa teknologi militer seperti unmanned system, sistem robotik yang berkolaborasi dengan Big Data yang mampu untuk mendeteksi ancaman dan serangan musuh. Indonesia sebagai negara yang kaya terhadap sumber alam masih belum terbebas dari konflik bersenjata. Papua menjadi wilayah paling tinggi terjadi konflik bersenjata di Indonesia.  KKB diklaim sebagai dalang dari setiap serangan yang terjadi.  Serangan yang digencarkan KKB tidak hanya melukai warga sipil, namun juga personel TNI-POLRI hingga Pos Militer atau Pos jaga. Belum adanya teknologi khusus pada Pos Militer atau Pos Jaga sehingga dalam mengantisipasi serangan mengandalkan kesigapan dari personel yang bertugas. Berdasarkan kondisi tersebut, penulis ingin menghadirkan robot penjaga yang dapat diaplikasikan pada Pos Militer di Papua. Robot penjaga yang diusulkan peneliti didukung dengan senjata SS2 v2 serta terintegrasi dengan kamera yang memungkinkan mengenali musuh atau kawan. Proses pengenalan musuh atau kawan mengimplementasikan image processing. Hasil pengenalan akan dikirim ke layar yang ada di Pos Militer melalui video sender. Hasil dari penelitian diperoleh sistem dapat bekerja dengan baik dari jarak 2 meter hingga 12 meter. Perubahan sudut juga memberikan pengaruh terhadap hasil pengenalan objek musuh atau kawan dan hasil pengenalan terbaik diperoleh pada sudut 0o hingga 60o. Intesitas cahaya yang terukur 0 Lux menghasilkan pengenalan objek musuh atau kawan yang buruk dan pengujian perubahan intensitas meliputi 169, 271, 341 dan 412 masih menghasilkan pengenalan objek musuh atau kawan dalam kategori baik.
Kebun Pintar dalam Ruang Berbasis Internet of Things
Authors: Ardiansyah Vicky , - Thiang , Putro Iwan Handoyo
Published: September 2024

The concept of smart farming emerged as a sign that technology developments in the agricultural sector are growing rapidly. Home gardening activities have become a trend during the pandemic and are often still done manually. This research aims to create a prototype of an indoor smart garden that is connected via the Blynk application to control and display sensor readings. The plant used as a test was a peppermint plant with a testing period of 4 weeks with 2 different treatments. Based on the results of the overall system testing that has been carried out, the monitoring and control system can work well. The soil moisture sensor is prone to corrosion, so it needs to be replaced every 6-7 days. Peppermint plants, in the second test which were given a set point of soil moisture of 60% and provided light for 23 hours, experienced an increase in plant height, the color of the leaves is green and number of leaves increases when compared to the first test which was given a set point of soil moisture of 50% and provided light for 9 hours.
Unveiling the interplay of financial and moral incentives in elevating employee performance within healthcare organizations: A Klang Valley, Malaysia exploration
Authors: Bawazir Abdullah Abdulaziz , Munap Rudzi Binti , Al-Tahitah Ali Nasser , Jamaludin Ahmad Syukri , Win Loo Veen , Gomez Joshua Deon
Published: September 2024

In today’s rapidly evolving organizational landscape, understanding the dynamics of employee incentives is crucial for fostering high performance. This research delves into the intricate interplay between moral and financial incentives and their repercussions on employee performance within the dynamic context of healthcare organizations. Drawing upon a comprehensive analysis of 226 respondents from three healthcare organizations in Klang Valley, Peninsular Malaysia, the study employs a quantitative approach to explore the relationships between independent variables (career growth, recognition, decision-making, salary, bonus, promotion) and the dependent variable of employee performance. The research unveils that moral incentives, including career growth, recognition, and decision-making, significantly impact employee performance. Professionals motivated by opportunities for growth, acknowledgment, and participation in decision-making demonstrate heightened engagement and commitment. In the financial realm, competitive salaries, performance-based bonuses, and transparent promotion pathways are identified as crucial factors influencing employee performance. The study advocates a holistic approach, emphasizing the synergistic integration of both moral and financial incentives. Healthcare organizations are encouraged to tailor their incentive structures to create a supportive and rewarding workplace, addressing the multifaceted needs and motivations of healthcare professionals. The implications extend beyond academia, offering practical guidance for organizations seeking to optimize workforce dynamics, foster job satisfaction, and ensure the sustainability of healthcare organizations.
Identifying factors of online shopping and influence on purchase intention of product among Saudi consumers: The mediating role of consumer trust
Authors: Habib Sufyan , Alkhuraydili Abdulaziz , Haider Ahsanuddin , Khan Mohammed Arshad
Published: September 2024

The rise of Internet technology has transformed consumer shopping behaviors, offering convenience and a wide range of options, making online shopping increasingly popular. In Saudi Arabia, this trend has grown significantly due to higher internet penetration, technological advancements, and shifting consumer preferences. However, building and maintaining consumer trust remains a crucial challenge. Despite the growing interest, there is limited research on the unique aspects of Saudi consumers’ online shopping behaviors. This study aims to address this gap by identifying key factors influencing these behaviors and examining their impact on purchase intentions, with a focus on the mediating role of consumer trust. This study explores factors influencing online shopping behavior and their impact on purchase intention, with a focus on consumer trust as a mediator. Using a survey of 573 respondents from Jeddah and Medina, Saudi Arabia, key factors identified through literature review include perceived usefulness, ease of use, risk perception, website quality, and social influence. The quantitative analysis revealed that customer service and return policies, information quality, perceived convenience, ease of use, usefulness, cost-saving, product variety, and social influence significantly affect consumer trust, which in turn enhances purchase intention. These findings provide valuable insights for businesses to optimize digital strategies, enhance consumer engagement, and foster long-term customer relationships, thereby boosting satisfaction and online business success.
Celestial Mechanics: The Non-Stability of The Newtonian Model
Authors: Barceló Gabriel
Published: September 2024

Multiple researchers and the three-body problem highlight a potential instability in the Classic model of the Solar System, indicating a possible fundamental instability in the system's dynamics.
Therefore, we consider that the Classical model does not reflect reality, and we propose an alternative model that we call: Theory of Dynamic Interactions, providing a brief exposition of the studies carried out by Advanced Dynamics, until defining a Rotational Dynamics of Interactions, applicable to bodies subjected to multiple pairs of successive non-coaxial forces.
Time-harmonic finite element model for brushless doubly-fed induction machine in natural operating mode
Authors: Jagieła Mariusz , Łukaniszyn Marian
Published: September 2024

The natural mode of operation for the brushless doubly-fed induction machine is a particular instance of synchronism at a so-called natural rotor velocity when one stator winding is powered by an AC and the other by a DC voltage source. Consequently, in addition to the rotating magnetic field, there exists a magnetic field that is fixed to the stator frame of reference. Analysis in this specific mode is essential as the natural velocity arises from the choice of pole numbers, thereby determining machine efficiency. However, this presents a significant challenge when it comes to mathematical modeling using complex-valued steady-state models through either equivalent-circuit or finite element analysis. This paper presents a study on the extension of the recently-proposed steady-state complex-valued finite element model for the brushless doubly-fed induction machine to enable its application in the natural operating mode. A high correlation with the data obtained from a time-stepping model is obtained for the extended model when subjected to both low and high levels of saturation of the magnetic circuit. This extension makes the whole approach applicable in all operating conditions and modes of the brushless doubly-fed induction machine. Considering the nearly two orders of magnitude lower computational costs associated with analysis via the proposed model compared to time-stepping analysis, it is particularly useful in scenarios that involve extensive computations and require multiple cases to be considered such as design sensitivity analysis, topology optimization or a connection with machine learning techniques.
Research on optimal scheduling strategy of source-grid-load-storage in rural low-voltage distribution network
Authors: Lei Zhimin , Pei Qianwen , Lian Junrong , Song Jinjin , Zhang Zhaoxu
Published: September 2024

The increasing demand for centralised power consumption in rural agriculture may result in low voltage and three-phase imbalance issues at the end of the distribution network. Furthermore, the extensive implementation of distributed photovoltaic (PV) systems may cause an imbalance between agricultural power load and PV output, exacerbating the issue of PV losses. In this paper, an optimal scheduling strategy for an integrated source-networkload- storage system is proposed to solve the above problems. The proposed strategy involves a dispatch model that includes PV power generation and energy storage, with the objective of maximising PV consumption and minimising operating costs. The PPO algorithm is finally employed to solve the cooperative optimisation model for source-grid-load-storage. The proposed scheduling strategy has been validated through examples, and the results demonstrate its effectiveness in ensuring the economic and safe operation of rural distribution networks.
Analysis and estimation of power losses in high-power, high-frequency transformers for isolated DC/DC converters
Authors: Grzejszczak Piotr , Barlik Roman , Wolski Kornel
Published: September 2024

The paper presents an in-depth analysis of the power losses in the windings and the cores of two different transformers as applied to a phase-shifted full bridge (PSFB) converter at the power level of 20–25 kW and the switching frequency of 50 kHz. The main difference in the construction of the considered devices was the method of winding realization as well as the shape and material of the cores. The influence of the winding geometry on the conduction losses was analyzed, and the losses in the cores were analytically estimated, taking into account the operating conditions of these elements in the full-bridge system with phase-shift modulation. Original resistive models of windings made of cooper sheets are presented. The obtained results were verified by experimental tests carried out in an isolated full-bridge DC/DC converter.
Introduction to the Investigation of reproduction of the real geometry of UBM panels
Authors: Walentyński Ryszard , Cybulski R , Myrcik Henryk
Published: September 2024

The article concisely describes UBM technology (Ultimate Building Machine), that can be used as a solution for buildings and roof structures. The structure of this technology is made of double corrugated thin-walled steel profiles manufactured on the construction site by a self-contained UBM manufacturing factory on wheels. These panels serve as both the building envelope and the structural system. The specificity of the construction poses many design problems, especially the determination of the strength parameters and stiffness of the double-corrugated panels from which the structure is made. The article presents the results of spatial scanning tests of double-corrugated steel sheets, which were carried out using commonly available 3D scanning devices: Leica 3D Disto and MagiScan app. Additionally, results of numerical analyses performed on scanned samples and a comparison of these results with preliminary laboratory tests have been presented in the article. The purpose of scanning was to obtain an accurate and real geometry of the UBM panels, to implement it into a numerical software, and then to perform numerical analyses. Commonly available 3D scanning devices were used because using advanced 3D scanners is not popular nowadays for economic reasons, and hand-built geometric models pose a lot of problems and are not accurate enough. Promising results were obtained, which form the basis for further research.
The Role of Corporate Governance in Shaping IFR Practices of Listed Companies in Sri Lanka
Authors: Raveeswaran Kiruththika , Vickneswaran Anojan
Published: September 2024

Objective – This study investigates the relationship between corporate governance characteristics and Internet Financial Reporting (IFR) in the context of listed companies in the Colombo Stock Exchange (CSE).Design/Methodology – The key metrics for assessing corporate governance characteristics include board size, independence, CEO duality, women in the board, audit committee independence, and ownership concentration. These data were derived from the published annual reports. Employing content analysis, the study measures IFR disclosure using secondary data from the published annual reports of 201 listed companies in Sri Lanka.Results – The mean value of listed companies' IFR disclosure is 50.66% in Sri Lanka. Regression results show that audit committee independence and ownership concentration significantly impact the IFR. However, no statistical evidence supports other variables' significant impact on IFR.Research limitations/implications – This study relies upon only 201 listed firms’ data for a financial year in Sri Lanka and the statistical model includes only five corporate governance-related variables. The study's findings contribute to the reform of corporate governance practices, corporate governance literature, and information asymmetry theory. Moreover, results have practical implications for the composition of the board and audit committee.Novelty/Originality – This study contributes to the existing literature and lays the groundwork for future research by providing new insights into the adoption of IFR practices in Sri Lanka and other similar nations. Examining the impact of corporate governance on IFR practices in listed firms within a developing country sheds light on the significance of corporate governance and IFR.
Moderating Role of ESG Disclosure on Minority Shareholders and Tax Avoidance
Authors: Widiastutik Rika Nur , Iqbal Syaiful , Rusydi Mohamad Khoiru
Published: September 2024

Objective - This study examined and analyzed the effect of minority shareholders on tax avoidance, which covers Environmental, Social, and Governance (ESG) disclosure as a moderating variable in manufacturing companies listed on the IDX for the 2018–2022. The minority shareholders consist of foreign ownership, institutional ownership, and managerial ownership.Design/Methodology - This study used secondary data taken from annual reports, sustainability reports, and the company’s OSIRIS database. Moreover, a total of 254 research sample companies were selected for this study using a purposive sampling technique. In addition, this study used a quantitative method using panel data regression analysis.Results - This study discovered that the higher the foreign and managerial ownership, the lower the tax avoidance actions within the company, while institutional ownership has no effect on tax avoidance. Furthermore, the results of the moderation regression analysis showed ESG disclosures can strengthen the effect of foreign and managerial ownership on tax avoidance, but cannot moderate institutional ownership on tax avoidance.Research limitations/implications - As a solution to tax avoidance in Indonesia, OJK may take into account and strengthen ESG disclosures made by companies.Novelty/Originality - This study discussed tax avoidance actions in Indonesia based on company ownership, with a focus on minority shareholders. Additionally, ESG disclosure was investigated to determine whether it could affect the relationship between minority shareholders and tax avoidance.
Exploring the Role of Internal Control Systems in Financial Statement Quality under Coercive Pressure in Government Entities
Authors: Mulyantoro Cahyo Dwi , Nurkholis Nurkholis , Atmini Sari
Published: September 2024

Objective - This study examines the influence of coercive pressure on financial statement quality and the mediating role of internal control systems in central government accounting entities of ministries/institutions partnering with the State Treasury Service Office of Malang (KPPN Malang).Design/Methodology - The research sample was collected by purposive sampling questionnaire with an 84.38% return rate. It comprised 216 respondents of staff and managers responsible for preparing financial statements in central government work units of ministries/institutions partnering with KPPN Malang. Research data were analyzed using the Partial Least Squares (PLS) technique to predict the relationships between coercive pressure, financial statement quality, and the mediating role of internal control systems.Results - It was found that coercive pressure positively influences financial statement quality. Coercive pressure has a positive effect on internal control systems, and internal control systems have a positive impact on financial statement quality. Meanwhile, internal control systems can mediate the relationship between coercive pressure and financial statement quality.Research limitations/implications - The research findings have practical implications that suggest coercive pressures from stakeholders demanding high-quality financial statements should be responded to by implementing effective internal control systems to ensure the quality of presented financial statements.Novelty/Originality - The novelty of this research lies in examining the mediating role of internal control systems in the relationship between coercive pressure and financial statement quality, which has not been discussed in existing studies.
Challenges in Achieving Coherence and Cohesion: A Discourse Analysis of Chinese EFL Students’ Essays
Authors: Tso Anna Wing Bo
Published: September 2024

This paper aims to highlight the prevalent challenges faced by Hong Kong Chinese students in utilizing connectives and ensuring coherence in their English academic writing. Research indicates that constructing fluent, effective, and cohesive English written texts that maintain logical flow poses a significant challenge for Chinese learners studying English as a foreign language (EFL). This difficulty is likely attributed to the substantial cultural differences between the English and Chinese languages, regardless of the learners’ grasp of English grammar and vocabulary usage. In order to raise awareness among EFL teachers regarding the struggles Chinese students encounter in mastering connectives and cohesion in English writing, this qualitative study analyses five sample English term papers written by Hong Kong university students majoring in English in 2023. The findings reveal that the Hong Kong EFL participants tend to present their arguments in an indirect and circular manner within their English essay assignments. Additionally, due to cultural norms and politeness, the true intent of their writing is often withheld, and the thesis statement is implicitly inferred towards the end of the essay rather than being explicitly stated at the beginning in the introduction. Furthermore, a focus group interview with the student participants conducted after the written analysis demonstrated that their English academic essays reflect the influence of Chinese social interaction norms on rhetorical patterns. The relevance of this study lies in its potential to inform EFL instruction, enabling educators to better support Hong Kong Chinese students in navigating the complexities of English academic writing and to adapt pedagogical approaches that bridge cultural and linguistic gaps.