bluuebunny/crossref_metadata_2025 huggingface dataset:
AIGC (Artificial Intelligence Generated Content) infringes the copyright of human artists
Authors: Zhuang Lyulin
Published: February 2024

With the rapid development of artificial intelligence technology, content generated by artificial intelligence has been rapidly applied to people's lives. At the same time, it is also accompanied by many infringement lawsuits, whether AIGC has really caused different degrees of infringement to human artists. Through the analysis of the existing literature on copyright issues and the walkthrough of Stable Diffusion, an AI-generated image platform, this article digs into the main factors that the AI-generated platform causes infringements on human artists. Provide references for using AI by enterprises and related media, and let more scholars pay attention to this issue. The study found that in the workflow of the AI generation platform, taking Stable Diffusion as an example, the two processes of model training and image generation may cause copyright infringement to a certain extent. Based on this, the AI generation platform has unauthorized use of copyright works, excessive plagiarism and adaptation of copyright works, and the generated images are not marked with watermarks or sources, which damages the copyright owner's rights.
From a new product: Apple Vision Pro Impact of VR technology development on VR gaming
Authors: Hu Jingrui
Published: February 2024

Apple vision pro renewed interest in VR technology. Whether academia and communities all have many discussions. In this article, three main questions could be discussed after the selecting of some data. The first is how it is being applied in the future. The second is what experience does this current application provide to the user. The last question is what direction could be taken in the future. This research may answer some of the doubts and bring some thoughts to the table
Exploration of special children education based on AR technology
Authors: Wang Xi
Published: February 2024

Augmented Reality (AR) is a nascent technical advancement that enables the seamless amalgamation of real-world and virtual-world data. The notion of materializing virtual information to enable its perception by human senses is a subject of significant scholarly interest, resulting in a perceptual experience that transcends the confines of conventional reality. Because of the popularity of augmented reality, artificial intelligence, and Internet support, distance learning systems with contextualized learning, gamified learning, and collaborative learning will empower all traditional classrooms or home education, rapidly adding massive learning resources. This research evaluates the current literatures related to AR as well as the influence and efficacy of AR on the education of special needs children, such as hearing impairment, intellectual disability, autism, emotional-behavioral disorders, etc. The purpose of this research is to uncover the benefits and challenges of AR, as well as recommendations and future directions, by studying pertinent situations.
Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network
Authors: Tan Haoyu
Published: February 2024

Automotive fire robots that are used in factories can carry out diverse operations in regard to patrolling, fire detection, and programmed fire rescue. An accurate detection of fire sources in factories is significantly crucial for unmanned firefighting robot in terms of building a reliable sensor system. An approach proposed by this paper to recognize the color and dynamic shape of varying flames based on HSV color algorithms and Convolutional Neural Network. As a comparison to traditional RGB image processing, this approach is more efficient in isolating colors in environment and more adaptive to a fire site that includes multiple noise factors. The research in this paper uses image processing algorithms that is trained by CNN to detect flames in simulated factory environments, followed by a HSV color locating algorithm to compute the coordinates of target fire to perform inverse kinematic analysis on an unmanned firefighting robot.
Research on path planning technology of mobile robot in the restaurant scene based on A* and D* algorithm
Authors: Han Jiahao
Published: February 2024

Intelligent robots have found widespread applications in various fields including medical treatment, industrial production, and social services. These robots are specifically designed to fulfill diverse tasks in different environments, the design and control of these robots play a crucial role in ensuring their efficiency and security. In response to the high demand for fast and convenient service, the catering field is a crucial and promising application field for intelligent robots. This article is primarily focused on the robot in the restaurant scene where the robot could automatically deliver food to the customers, thus reducing the burden on human service providers. In this special environment, the delivery robot must possess the capability to identify its surroundings within the restaurant and autonomously calculate an obstacle-free path from the starting point to the desired destination without colliding with either obstacles or individuals. In this article, the restaurant scene is built in the Gazebo, and map scanning and conversion methods are utilized to enable the robots to recognize the environment effectively. Furthermore, A* and D* algorithms are employed in Python to achieve global and local path planning, respectively, thus validating the feasibility of the methods. The combination of these two algorithms demonstrates a successful path-planning application within the context of a restaurant scene.
Development and analysis of autonomous driving technology for wheeled robots
Authors: Chen Baiquan
Published: February 2024

A The remarkable progress achieved in the evolution and utilization of autonomous driving technology in wheeled robots is thoroughly analysed in this extensive review article. It begins by stressing the significance of autonomous driving technology in the functioning of wheeled robots. The article proceeds to explore the advancement and application of the core components of autonomous driving technology, namely perception, decision-making, and control, in wheeled robots. Moreover, the comprehensive examination sheds light on the main stumbling blocks and restrictions faced in the current implementation of self-driving technology in wheeled robots, including the need for advanced perception systems and decision-making abilities. Touching on future directions in this domain and offering suggestions for further investigation, the review ultimately emphasizes the capacity of self-driving technology to augment the efficiency and security of wheeled robots, while stressing the inevitable requirement for continuous research and innovation to surmount existing hurdles and actualize this potential in its entirety.
Investigation of selection and application of Multi-Armed Bandit algorithms in recommendation system
Authors: Chen Panyangjie
Published: February 2024

The Multi-Armed Bandit (MAB) algorithm holds significant prominence as a recommendation system technique, effectively presenting user-centric content preferences based on the analysis of collected data. However, the application of the basic MAB algorithm in real-world recommendation systems is not without challenges, including issues related to data volume and data processing accuracy. Therefore, the optimization algorithm based on the MAB algorithm is more widely used in the recommendation system. This paper briefly introduces the multi-armed bandit algorithm, that is, the use of MAB in the recommendation system and the problems of the basic MAB algorithm. Aiming at the problems of the basic MAB algorithm, it introduces the MAB-based optimization algorithm used in the recommendation system. At the same time, this paper also analyzes and summarizes such algorithms. This paper introduces two different MAB-based optimization algorithms, namely The Details of Dynamic clustering based contextual combinatorial multi-armed bandit (DC3MAB) and Binary Upper Confidence Bound (BiUCB). In addition, this paper also introduces the application of algorithm in recommended system. Finally, this paper summarizes the introduced algorithms and proposes the future prospects for MAB optimization algorithms.
An investigation of progress related to stochastic stationary bandit algorithms
Authors: Liu Yizhi
Published: February 2024

The Multi-armed Bandit algorithm stands as a consequential tool for informed decision-making, distinct from reliance on intuitive selections, given its systematic proclivity to meticulously assess accessible alternatives with the intent of discerning the most auspicious outcome. Amid the repertoire of algorithmic variations, the Stochastic Stationary Bandit algorithm assumes a foundational and enduring role, finding versatile application across diverse domains, including but not limited to digital advertising, price optimization, and recommendation systems. With these considerations in view, the present study embarks upon a comprehensive scrutiny of this subject matter. This paper reviews on the Explore-Then-Commit algorithm, Upper Confidence Bound algorithm, and Thompson Sampling algorithm by explaining, comparing their formulation, features, and expected results. Explore-Then-Commit algorithm has distinct phase to explore all the choices uniformly. Upper Confidence Bound algorithm make decisions by calculate an upper confidence index which is an overestimate for each choice. Thompson Sampling algorithm depends on randomness to make choices. Explore-Then-Commit algorithm faces the problem of when to explore and when to stop. Upper Confidence Bound algorithm and Thompson Sampling algorithm solve this problem by avoid certain phases. Multi-armed Bandit algorithm could deal with the process of displaying items of potential interest to users in a recommendation system, the delivery of resources in resource allocation, or the way to maximize revenue in a business.
Joint optimization of utility and privacy in mobile edge computing
Authors: Lu Miou
Published: February 2024

In mainstream research on Mobile Edge Computing (MEC), considerations for user privacy are often overlooked. This paper introduces Differential Privacy techniques to MEC, which obfuscate specific parameter values when transmitting user data, preventing leakage of sensitive information. Simultaneously, privacy, latency, and energy are incorporated as components of the utility function. We model the task offloading process using a deep reinforcement learning model and propose the use of the Deep Deterministic Policy Gradient (DDPG) algorithm in conjunction with a Laplacian privacy budget for joint optimization of privacy and utility during offloading. After sufficient training, the DDPG model can effectively approximate the optimal solution. Compared to traditional work, the optimized framework gives ample consideration to user privacy needs and outputs the optimal task offloading ratio solution with relatively low time complexity. This approach effectively safeguards user privacy while also considering the efficiency of task offloading.
A reshaped intelligent autonomous driving by using the influence and progress of human factor engineering concept
Authors: Fan Qiwei
Published: February 2024

In modern society, with the improvement of people's taste of life and the change of people's consumption awareness and lifestyle, cars are no longer just a driving tool or means of transportation. People's requirements for cars are constantly improving, such as the comfort of car seats and the convenience of reversing radar. This paper examines the role of human factor engineering in the intelligent driving and gives some examples of the usage of the human factor engineering, and analyse it by Single-vehicle level analysis and Macro system level analysis. Then, the paper lists different human factor problems affecting intelligent driving. The paper discusses one iHCl system of human factor engineering which based on the Cognitive theory at three levels of perception. The paper analyses two cognizant. For the human users, it has the three-layer scene elements, data-driven, and goal-driven information processing mechanisms utilize dynamic mechanisms for information collection and subsequent response, allowing human users to perceive the updated information obtained in the dynamic environment. For intelligent system, For intelligent systems, it has a "top-down" decision-making processing mechanism. The feedback mechanism is composed of subjects such as goals, plans, and tasks.
Research on facial recognition system based on deep learning
Authors: Yao Yiming
Published: February 2024

This essay explores the integration of deep learning techniques in facial recognition, delving into the advancements, challenges, and prospects in future of this transformative technology. Deep learning, a subset of artificial intelligence, has revolutionized facial recognition by enabling automatic feature learning and robust representations from raw facial data. This paper examines the key components of deep learning in facial recognition, including feature extraction, facial alignment, and face recognition. It highlights the advantages of using deep learning over traditional methods by analysing related research. It also addresses the challenges deep learning faces in facial recognition, such as data bias, privacy concerns, and adversarial attacks. The essay also explores the promising future of deep learning in facial recognition, with ongoing research aiming to improve robustness, fairness, and privacy preservation.
Review on natural language processing models
Authors: YIKUN LI
Published: February 2024

Accessing information has grown simpler as a result of the internet's expanding use and the arrival of the big data era. Compared to traditional approaches, employing NLP for information condensation and amalgamation proves to be a highly effective method. This article focuses primarily on the sentiment analysis aspect of NLP, offering a comprehensive exploration of two deep learning models: BERT and CNN. It delves into the intricacies of their principles, analyzes their respective strengths and weaknesses, and proposes potential avenues for enhancement. By delving into these models, Researchers and practitioners can obtain a better understanding of sentiment analysis and its applications in diverse fields.
The application of artificial intelligence in aerospace engineering
Authors: Li Wenyue
Published: February 2024

In recent years, there has been considerable interest in applying Artificial Intelligence (AI) in the field of aerospace engineering. However, the existing literature on this topic is not sufficiently comprehensive. This paper is purposed to solve this problem by providing a thorough analysis and overview of the current state of AI in aerospace engineering. The paper is divided into four sections. Firstly, the use of AI in autonomous navigation and flight control is explored, focusing on advanced algorithms and sensor technologies that enable highly autonomous and efficient aircraft navigation. Secondly, the application of AI in image recognition and computer vision is discussed, highlighting its significance in remote sensing and aerospace component quality inspection. The third section examines the integration of AI in unmanned aerial vehicles (UAV), covering the control system and the utilization of machine learning techniques for improved UAV capabilities. Lastly, the paper explores the impact of AI on data analysis and prediction in the aerospace industry, encompassing weather forecasting, resource allocation, and decision-making processes. Finally, this paper gives a general overview of the nowadays application of AI in aerospace engineering.
Application of matrix in signal processing
Authors: Du Haotian
Published: February 2024

Signal processing, a foundational discipline in modern technology, encompasses a diverse array of applications, ranging from audio and image processing to communication systems and medical imaging. This review investigates how matrix-based techniques are widely used to advance signal processing methodologies. In order to discretize continuous-time signals for digital processing, which occurs in the first section of the paper, matrices play a crucial role in signal sampling. A key principle, the Nyquist-Shannon Sampling Theorem, directs appropriate sampling rates to prevent aliasing, with matrices permitting effective signal representation. The effectiveness of matrix-based filtering methods for frequency modulation and noise reduction, such as convolution and correlation, is then investigated. By utilising matrix operations, these methods enable real-time signal processing. The Fourier Transform and Wavelet Transform are also featured in matrix-driven signal transformation, providing insights into frequency analysis and non-stationary signal characterization. By reducing noise components, matrix-based approaches, particularly Singular Value Decomposition (SVD) denoising, are essential for improving signal quality. Additionally, image compression employs SVD. Matrix-based compressive sensing revolutionises signal recovery from sparse data and results in data-efficient reconstruction. Signal processing has been transformed by matrix-based approaches, which have enabled previously unheard-of levels of efficiency, accuracy, and adaptability. The review highlights their significant influence on several signal processing fields.
A review of 3D reconstruction methods based on deep learning
Authors: Wang Yuanchun
Published: February 2024

In computer vision, an important research area is three-dimensional reconstruction. Using computer technology to reconstruct three-dimensional models of objects has become an indispensable part of in-depth research in many fields. This thesis presents the development process of 3D reconstruction methods that use deep learning. Compared with traditional methods, the 3D reconstruction method based on deep learning has more flexible input and output and higher efficiency. This thesis classifies the methods by the type of 3D model representation and discusses different frameworks for 3D reconstruction based on deep learning. With the introduction of the method NeRF (Neural Radiance Field), the three-dimensional reconstruction work based on deep learning has got a great development. NeRF can achieve good results in a very short period of time in the face of various complex scenes. With the continuous improvement of NeRF by researchers, this method has achieved more amazing results. Finally, the existing problems in the field of 3D reconstruction, the causes of problems and possible solutions are analyzed. Finally, the future development trend and direction of this field are hypothesized and discussed.
Surgical robot navigation based on SLAM technology
Authors: Lai Wenxin
Published: February 2024

With the widespread application of surgical robots and the development of computer vision, SLAM-applicated surgery is receiving increasing attention. However, due to the unique surgical environment, SLAM faces some challenges. Two key issues will be discussed in this article: dynamic object detection and image segmentation, as well as scene reconstruction under data scarcity. Firstly, dynamic object detection and image segmentation is an important issue in SLAM applications. During the surgical process, doctors often use surgical instruments, which may partially or completely obscure the object, making it difficult to detect the target. Methods based on traditional feature matching may not be able to accurately detect dynamic targets perform image segmentation. Therefore, this article will combine semantic networks for analysis to improve the performance. In addition, scene reconstruction under data scarcity is another challenge in SLAM applications. Traditional SLAM methods typically rely on a large amount of feature points or map data. But in surgery, due to the complexity of occlusion and geometric structure, reliable data may not be easily obtained. This article will develop with the steps of reconstruction and analyze feasible methods that can improve the accuracy and stability of reconstruction. To conclude, this article will concentrate on these two issues, analyze recent papers, and ultimately summarize some feasible solutions, providing ideas and references for other researchers in this field.
Current study on multi-robot collaborative vision SLAM
Authors: Bao Chenxi , Hu Yuanfan , Yu Zhuoqi
Published: February 2024

Simultaneous Localization and Mapping (SLAM) stands as a vital technology for automatic control of robots. The significance of vision-based multi-robot collaborative SLAM technology is noteworthy in this domain, because visual SLAM uses cameras as the main sensor, which offers the benefits of easy access to environmental information and convenient installation. And the multi-robot system has the advantages of high efficiency, high fault tolerance, and high precision, so the multi-robot system can work in a complex environment and ensure its mapping efficiency, these may be a challenge for a single robot. This paper introduces the principles and common methods of visual SLAM, as well as the main algorithms of multi-robot collaborative SLAM. This paper analyzed the main problems existing in the current multi-robot collaborative visual SLAM technology: multi-robot SLAM task allocation, map fusion and back-end optimization. Then this paper listed different solutions, and analyzed their advantages and disadvantages. In addition, this paper also introduces some future research prospects of multi-robot collaborative visual SLAM technology, aiming to provide a reference direction for subsequent research in related fields.
Current status and future prospects of SLAM technology in the field of blind navigation
Authors: Hu Sijia , Yang ZhongJin
Published: February 2024

This comprehensive review paper explores the progressive integration of Simultaneous Localization and Mapping (SLAM) technology within the realm of blind navigation. The study delves into the current landscape of research and practical implementations where SLAM techniques are harnessed to empower visually impaired individuals in navigating their surroundings. The paper takes a focused approach by elucidating the advancements achieved through RGB-D-based SLAM navigation systems and the intriguing developments within the realm of LSD-SLAM-based navigation. A paramount facet of this exploration involves the design and deployment of interactive devices that bridge the gap between blind individuals and navigation systems. Notably, the paper examines the intricacies of these interaction interfaces, highlighting their pivotal role in enhancing the efficacy and user-friendliness of SLAM-driven navigation. Furthermore, this review critically assesses the existing limitations that persist within the current application of SLAM technology for blind navigation. The discussion encompasses challenges related to environmental diversity, real-time processing constraints, and the need for robustness in complex scenarios. By acknowledging these limitations, the paper paves the way for a comprehensive examination of potential avenues for refinement and enhancement. In conclusion, this paper serves as a comprehensive guide to the progressive incorporation of SLAM technology into blind navigation. It underscores the transformative potential of RGB-D and LSD-SLAM systems, underscores the significance of interaction interfaces, and underscores the evolving trajectory towards a more inclusive and efficient navigation solution for the visually impaired.
The investigation of application related to ChatGPT in foreign language learning
Authors: Liu Chen
Published: February 2024

The integration of Chat Generative Pre-trained Transformer (ChatGPT) into the realm of education has garnered significant attention recently. Due to its capability of providing answers to inquiries, the feasibility of ChatGPTs implementation in education has been examined. This review explores the potential benefits of ChatGPT in foreign language learning. Initially, different methods used to research the ChatGPTs plausibility of improving language skills are shown. Furthermore, this review discusses some different application scenarios of ChatGPT, also presenting its drawbacks and limitations. Despite recognized concerns, the viability of ChatGPT as a valuable instrument for foreign language education remains substantiated. The technology exhibits promise in offering personalized and interactive language practice. While acknowledging potential pitfalls, the study underscores the need for further investigation to harness ChatGPT's potential optimally. By emphasizing ongoing research and development, this review envisions a future where ChatGPT contributes significantly to language education, underscoring the necessity for careful exploration of its evolving capabilities.
Beyond traditional networks: Stock predictions via enhanced backpropagation
Authors: Wu Wentao
Published: February 2024

Predicting stock prices has long been a challenge due to the inherent volatility and intricacies of stock markets, and addressing these forecasting challenges has broad implications for the financial sector and those involved in market activities. This research holds pivotal importance as it equips traders, investors, and financial institutions with enhanced tools, facilitating better decision-making and optimized strategies. Beyond its academic significance, an accurate model becomes an indispensable asset in the real-world navigation of stock markets, especially in a realm where slight prediction discrepancies can result in substantial financial impacts. This study endeavours to introduce and validate an enhanced Backpropagation neural network with the core objective of elevating stock price prediction accuracy and reliability to a benchmark level. Adopting a meticulously crafted Backpropagation neural network, designed specifically for improved accuracy in stock price forecasting, we employed rigorous evaluation methods, measuring the model's performance against key metrics MAE and MSE. Additionally, visual representations were given to provide a more intuitive understanding of the model's prowess. The results were clear: the enhanced model demonstrably excelled in prediction precision, as evidenced by the favourable MAE and MSE outcomes. Visual narratives further accentuated its adeptness at tracing the complex oscillations inherent to stock market behaviours, underscoring both its academic contribution and potential as a transformative tool in the practical landscape of stock forecasting.
A survey of dynamic programming algorithms
Authors: Zhang Yunong
Published: February 2024

Dynamic programming is an important algorithmic idea with widespread applications in computer science and other disciplines. With the continuous improvement of computing power and the increasing complexity of practical problems, dynamic programming algorithms have also received more and more attention. However, due to the limitations of the dynamic programming algorithm itself, it also brings challenges to the optimization of the algorithm and its application in different fields. This paper uses the methods of literature review and case analysis to systematically summarize and sort out the basic principles, complexity analysis, classic problems, and applications of dynamic programming algorithms. First of all, this paper discusses the basic concepts of splitting and optimal substructure characteristics of related problems in dynamic programming; secondly, it discusses the time complexity and space complexity of dynamic programming algorithms in detail; then, in the classic dynamic programming The case part focuses on the knapsack problem, including the 0-1 knapsack problem and multiple knapsack problems; finally, this paper discusses the wide application and optimization of dynamic programming algorithms in practice, covering natural language processing, bioinformatics and How to optimize the dynamic programming algorithm to improve the efficiency of problem solving and reduce resource consumption. To sum up, this paper fully demonstrates the basic principles and applications of dynamic programming algorithms, as well as optimization methods and development trends, and provides guidance and references for their academic and practical applications.
Research on image style transfer based on deep learning
Authors: Li Junlin , Lin Chen , Zhao Yingbo
Published: February 2024

In many fields, image style transfer has been a popular topic. It also has a long history; the earliest examples date back to the previous century. A number of style transfer techniques are currently flourishing, from manual modeling to the use of neural networks. Image style transfer methods is improving and taking less time. This paper is separated into two main categories to analyze based on picture iteration and model iteration - and summarizes the various techniques of image style transfer based on deep learning in accordance with the timeline. This paper introduces maximum mean difference, Markov random field, and deep image analogy in picture iteration-based image style transfer. In terms of model iteration-based image style migration, this paper introduces generative models and image reconstruction decoders. Finally, the paper presents some recommendations and outlook on the future.
A study on the CNN-based face recognition across various facial angles
Authors: Chen Zibin
Published: February 2024

Ace recognition is a prominent computer technology with a broad range of applications, such as identity verification and face payment. Over the years, significant advancements have been made in the research and practical implementation of face recognition in academia. However, the variability of face images in real-world scenarios poses challenges to accurate face recognition. Specifically, the variation in facial angles affects the extraction of crucial face information by the model, yet there is a lack of research focusing on the angle factor's impact on face recognition. In this study, we collect and process face datasets, construct a convolutional neural network (CNN) model, capture images containing faces via a camera, detect and track faces from it automatically, and then recognize the detected faces. We investigate the accuracy of face detection and recognition under different angles. Experimental results demonstrate that when the angle ranges from 0 to 30, face detection and recognition achieve excellent accuracy. Within the angle range of 30 to 45, the performance remains within an acceptable range. However, when the angle exceeds 45 and reaches 60, face detection and recognition accuracy decline significantly, resulting in poor performance.
Comportamiento morfológico del pico de loro (Heliconia psittacorum) y matico (Piper aduncum) en concentraciones de lixiviado de residuos sólidos municipales en una zona tropical del Perú
Authors: Perez Julian Fredy Andres , Barrera Quispe Jeferson Walter
Published: February 2024

La generación de lixiviados por residuos sólidos afecta la vegetación en las zonas tropicales, cuya capacidad de atenuación natural aún es desconocida. Es por ello, que el presente estudio evalúa el comportamiento morfológico de la Heliconia psittacorum y Piper aduncum a 4 tratamientos con concentraciones diferentes de lixiviados (T1: 250 ml, T2: 500 ml; T3:1 L y T0: 0 ml) cada una con 3 repeticiones con una duración de 21 días. Se realizaron pruebas estadísticas para conocer el impacto de ambas plantas en diferentes dosis de lixiviado. Los resultados indicaron resistencia de las plantas a las diferentes concentraciones probadas con significativas reducciones en el grosor del tallo y tamaño de hoja de Heliconia psittacorum y Piper aduncum sugiriendo una adaptación de las mismas a las concentraciones de lixiviado lo que las vuelve en potenciales fitoremediadores de lixiviados en la selva central del Perú.
Evaluation of Various Algorithms' Performance in Supervised Binary Classification for Occupant Detection Using a Dataset from a Residential Building
Authors:   Department of the Built Environment, Andersen Kamilla Heimar , Schaffer Markus ,   Department of the Built Environment, Johra Hicham ,   Department of the Built Environment, Marszal-Pomianowska Anna ,   Department of the Built Environment, O'Brien William ,   Carleton University, Heiselberg Per Kvols ,   Department of the Built Environment
Published: February 2024

This technical report describes the evaluation process of various machine learning algorithms' performance used for supervised binary classification for occupant detection, using a dataset from a residential building in the North of Denmark.
Gestational administration of aqueous leaf extract of Jatropha tanjorensis alleviated postpartum emotional and cognitive dysfunction in rats (Wistar strain)
Authors: Antai Atim , Ukoh Imoh , Sunday Bisong , Edet Emem , Johnny Martha
Published: February 2024

This study examines the effect of gestational administration of aqueous leaf extract of Jatropha tanjorensis (JT) on postpartum-like behavioural outcomes to delineate its possibility as a prophylactic, therapeutic agent in the treatment of postpartum symptoms. Inseminated female rats (120-150g) were grouped into two-control and JT group (n=10). Control received 20 ml/kg of distilled water and JT group received 500 mg/kg of JT orally once daily for 21 days in gestation. Non-pregnant rats were excluded from the study. Parameters assessed at postpartum include antidepressant-like (force swim test, FST; tail suspension-test, TST), locomotor (open field test, OFT), anxiolytic-like (elevated plus maze, EPM; light-dark box, LDB), learning and memory (T-maze; novel object recognition task, NORT), social (nest score) and analgesic-like (hot plate test, HPT; tail flick test, TFT) behaviours. JT increased (P<0.05) mobility and latency to immobility durations in FST and TST; open arm entry (P<0.001) and duration (P<0.01) in EPM and light box duration (P<0.05) in LDB; locomotion and exploration, but reduced anxiety-like levels in EPM, LDB and OFT. It increased nest score (P<0.05); mean retraction time (P<0.01) of TFT. JT showed positive score for short and long term memory in NORT and improved percentage alternation in T-maze though not significant compared to control. In conclusion, the aqueous extract has a therapeutic effect that reduces postpartum-like depression and anxiety, and improves locomotor activity. JT can be a preventive and adjuvant therapeutic option for pregnant women
Analysis of chemotherapy-induced toxicity and its amelioration in patients with cancer in a tertiary care hospital
Authors: V Raghav , A Geetha , K Mamatha , R Yuvarani , S Purushotham
Published: February 2024

Background: According to the World Health Organization, an adverse drug reaction (ADR) is any response to a medication that is unpleasant, unexpected, and occurs at recommended doses utilized in men for prevention, diagnosis, or therapy. Pharmacovigilance is of utmost importance to ensure safe and effective medications, especially in palliative health care services. ADRs associated with cancer chemotherapy in a hospital setup give insight into the causality, severity, and preventability of the identified ADRs. Hence, the study aims to analyze the various adverse effects of drugs used for cancer chemotherapy and the measures taken to ameliorate the toxicity of these drugs.

Aims and Objectives: The objective of our study was to analyze the various adverse effects of drugs used for cancer chemotherapy and the measures taken to ameliorate the toxicity of these drugs.

Materials and Methods: It was a cross-sectional study done from October 2021 to May 2022. The study was conducted in the Department of Medical Oncology at Victoria Hospital, attached to Bangalore Medical College and Research Institute, with 40 patients who were diagnosed with cancer and were undergoing chemotherapy. ADRs were recorded using the CDSCO–ADR form, graded according to severity, and uploaded to PVPI.

Results: The data demonstrated that most of the patients who experienced ADR were females (75%) and in the age group of 18–60 years (70%). The most common chemotherapeutic agent was cisplatin and the most common ADR was anemia (42.5%) and nausea (27.5%). Out of the 21 types of adverse reactions, nine ADRs were treated with ameliorating therapy. Even though a number of adverse reactions were reported, only 43% of patients were treated with ameliorating therapy.

Conclusion: Patients diagnosed with cancer and who are on chemotherapy are already in distress and experience a lot of ADRs. Identifying and treating these ADRs is quite challenging. These ADRs are inevitable, and in terms of the risk-benefit ratio, the benefit is greater than the risk due to therapy. So, we must create awareness about ameliorating toxicity to positively impact the quality of life. The study shows only some reactions are treated with ameliorating therapy for chemotherapy-induced toxicity.
EARTHQUAKES AND BUSINESS RECOVERY STRATEGIES: A SYNTHESIS AND RESEARCH AGENDA
Authors: ALADAĞ Ömer Faruk , KARACA KALKAN Pelin , ERDEM ALADAĞ Münire Tuğba
Published: February 2024

This study presents the results of a systematic review of the literature on business recovery strategies after earthquakes. Business recovery strategies represents an important research area that has generated a substantial body of knowledge due to devastating earthquakes occurred in the worldwide. Therefore, the authors employ a systematic literature review method to delve into the existing body of literature. A total of 66 articles on this subject published in the period between 1998-2023 were examined in the review. As the main contribution, this study integrates prior fragmented research on business recovery by categorizing studies based on common themes. Thus, the research draws a picture of the literature on business recovery strategies after earthquakes by outlining its characteristics. After identifying knowledge gaps in the literature, the authors offer future research avenues for scholars with high contribution potential.
Proposing an Approach for the Diffusion of Building Integrated Photovoltaics (BIPVs)—A Case Study
Authors: Amo Awuku Samuel , Bennadji Amar , Muhammad-Sukki Firdaus , Prabhu Radhakrishna , Sellami Nazmi
Published: February 2024

Consistent probing into building integrity has led to the exploration of clean energy options such as building integrated photovoltaic (BIPV). BIPV has proven to be aesthetically pleasing, architecturally feasible, and capable of making buildings energy producers instead of mere energy consumers. Despite the enormous benefits of BIPV, its adoption and diffusion have been relatively sluggish and remain far below expectations, especially in developing countries like Ghana. This empirical study aims to assess the impact of advertising on BIPV awareness in Ghana. It also highlights the aesthetic preferences of various respondents. The study uses online surveys to gather quantitative data from 412 respondents across all 16 regions of the country. An initial study conducted on the awareness of BIPV in Ghana indicated a low rate of awareness. Therefore, a sensitisation poster and architectural visualization (AV) were adopted to boost awareness across all 16 regions of the country. Awareness of BIPV increased from 18% to 79.5% after the introduction of the sensitisation poster. Also, 88.8% of the respondents preferred BIPV to Building Applied Photovoltaic (BAPV) mainly because of aesthetics (beauty) and the cost benefits. The respondents indicated that aesthetics is paramount when choosing solar panels for their homes. This study therefore recommends high investment in awareness creation, development of specific design guidelines for BIPV applications and establishment of demo projects in developing countries. The findings of this study contribute to the existing literature on BIPV adoption and may be useful for BIPV manufacturers, marketers, government, and other stakeholders as it provides evidence on the often-neglected approach to BIPV diffusion.
PERENCANAAN PAJAK PPH PASAL 21 SEBELUM DAN SESUDAH BERLAKUNYA PMK 66 TAHUN 2023
Authors: Sinambela Tongam , Reni Anggraeni
Published: February 2024

Tujuan dari penelitian ini adalah untuk mengetahui perhitungan pajak penghasilan pasal 21 berdasarkan metode net, gross, dan gross up serta dampak penerapan metode tersebut setelah berlakunya PMK Nomor 66 Tahun 2023. Penelitian ini menggunakan metode deskriptif kualitatif yang berasal dari rekap perhitungan gaji karyawan tetap tahun 2022 yang diolah sendiri dari penulis. Hasil penelitian ini menunjukan bahwa metode gross lebih menguntungkan dalam menghitung PPh Pasal 21 terutang dan PPh Pasal 29 sebelum dan setelah berlakunya PMK Nomor 66 Tahun 2023.
Role of Hydrogen in I.C Engines
Authors: Journal IJSREM
Published: February 2024

The development of combustion models, paired with the usage of biofuels, has been viewed as an effective way to minimize pollutant emissions such as CO, HC, NOx, and smoke. Indeed, homogenous Charge Compression Ignition is a novel strategy to significantly reducing NOx emissions and smoke due to lower cylinder temperature and a higher rate of homogenous air fuel mixture as compared to compression ignition engines. The experimental findings demonstrated the maximum brake thermal efficiency, despite a 4.1% decrease compared to diesel in normal mode. Hydrogen energy is a key technical pathway for achieving carbon peak and neutrality. One method of utilizing hydrogen energy is through a direct injection engine. It has the advantage of increasing thermal efficiency and reducing aberrant combustion phenomena.The impact of using hydrogen to increase fuel economy and considerably reduce exhaust emissions. The use of hydrogen in S.I. and C.I. engines has been researched. Keywords: 1) Hydrogen. 2) Internal Combustion Engine. 3) Combustion with High Pressure Injection 4) Zero emissions.
Lipoprotein (a) as a cause of premature coronary artery disease; a case report of a 34-year-old male patient presenting with ST elevation myocardial infarction.
Authors: Edward Kateta , Chanda Kabwe Jane , Mtaja Agness , Kabwe Lorrita
Published: February 2024

Background

Premature coronary artery disease (PCAD) is considered when coronary artery disease (CAD) occurs in the younger population, less than 45 years old in males. Modifiable risk factors are present in about 80% of patients with PCAD such as smoking, diabetes, high blood pressure and obesity. High serum lipoprotein (a) level has recently emerged as a risk factor for PCAD. However, it is not routinely investigated in young patients presenting with acute coronary syndromes.

Case Presentation

A 34-year-old African man presented to our hospital with 2 hours of worsening left sided chest pain. He gave a history of long-standing chest pain in the past that was being managed as peptic ulcer disease. He also complained of a slow heart rate. He had no traditional cardiovascular risk factors. The only significant history was a recent recovery from COVID-19 infection two months prior. His physical examination was only significant for a bradycardia, ECG revealed a hyper acute inferior STEMI without right ventricular involvement. Echo showed a normal left ventricle with reduced ejection fraction (EF: 50%), wall motion abnormalities consistent of an inferior MI. He immediately received streptokinase with good clinical and ECG response. His coronary angiogram showed a severe lesion and plaque rupture in the mid-distal segment of a dominant right coronary artery. Percutaneous coronary intervention (PCI) was successfully done, and one drug eluting stent was implanted. He was placed on guideline directed medical therapy for secondary prevention. The only significant possible aetiology found was an elevated Lipoprotein (a) (137mg/dl). Subsequent serum Lipoprotein (a) remained persistently elevated despite therapy with high dose niacin. He was recommended for a PCSK9 receptor blocker, but this medication is not available in the country and the patient could not meet the costs. He remains stable to current medications with no recurrence of chest pain, two years after the index event, the Lipoprotein (a) levels are still high.

Conclusions:

Lipoprotein (a) appears to be an important overlooked cardiovascular risk factor for premature coronary artery disease especially in the absence of traditional risk factors. It should always be considered in the screening of premature coronary cardiovascular disease. The acute presentation in this patient was probably triggered by SARS COV-2 infection.
Factores del Crecimiento de Largo Plazo y Declinación en Argentina: Un análisis del Siglo XX
Authors: Dabús Carlos , Delbianco Fernando
Published: February 2024

This paper explores the historical long-term determinants of economic growth in Argentina along the XX century. The evidence indicates that at the beginning economic openness plays a key role to explain the successful performance achieved until the great crisis of 1930. From there on, and until the middle of the ´70s, Argentina maintained a similar growth of the previous period, which was driven by the domestic demand via investment. Nonetheless, in the last quarter of the past century economic growth slowed drastically, which was mainly associated with economic instability, approximated by inflation and the per capita growth rate output volatility. The intuition is that once the country lost foreign markets for its exports, could temporarily sustain growth thanks to domestic demand, but once this strategy petered out the economy was signed by a long period of turbulence and stagnation, specifically since 1975 onwards. Thus, economic policy recommendations are to develop new led export-oriented sectors, so that alleviate the external restriction, as to avoid procyclical policies in order to reduce the economic instability.
Reception date: 02/06/2023Acceptance date: 03/08/2023
Taxonomías de Empresas de Software Libre y Open Source. Sus limitaciones a partir de casos de PyMEs argentinas
Authors: Morero Hernán Alejandro , Vélez Juan Gabriel , Juncos Ignacio , Motta Jorge José
Published: February 2024

The creation of the Free/Libre Open Source Software (FLOSS) and its growth, have modified the value chain of the software industry. However, there isn’t good statistical information showing the importance and the role in the economy of the FLOSS firms. This article aimed to contribute to the improvement of the data collection and the discussion about the way of categorization of the FLOSS firms. The methodology was qualitative, drew on seven case studies of Argentineans FLOSS SME, focusing on the degree of collaboration with the FLOSS community and its business model. This allowed to make a critical analysis of the current categorization of FLOSS firms in the literature, as a first step in the way to developing a typology of FLOSS firms feasible of be identified in technological surveys.
Reception date: 03/05/2023Acceptance date: 24/11/2023
Handling Stunting in Indonesia: Challenges, Progress and Recommendations
Authors: Suparji  , Wahito Nugroho Heru Santoso , Surtinah Nani
Published: February 2024

Stunting is a nutritional problem that has an impact on children's growth and development. This article discusses the prevalence of stunting in Indonesia, the efforts made by the government, and Indonesia's position in dealing with the stunting problem. Data from various sources, including WHO and the Global Nutrition Report, is used to provide a comprehensive picture. In a global context, stunting affected approximately 144 million children under five years in 2019, with a higher prevalence in low- and middle-income countries . In Southeast Asia, Indonesia, the Philippines and Timor Leste have a high prevalence of stunting. Even though Indonesia has shown progress in reducing stunting rates, there are still challenges and targets that must be achieved. Indonesia's contribution to the global stunting problem, both in terms of causes and solutions. Efforts that have been made, such as improving nutrition for children and pregnant women, quality of health services, access to sanitation, and participation of all parties, are highlighted as important steps in dealing with stunting.