bluuebunny/crossref_metadata_2025 huggingface dataset:
Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm
Authors: Bernanda Devi Yurisca , Jawawi Dayang N.A. , Abd Halim Shahliza , Adikara Fransiskus
Published: February 2024

Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the needs of stakeholders and the goals of the organization. The research objectives in this research to the process collected and integrating data from multiple sources and ensuring interoperability. Conclusion in this research is determining is the clustering algorithm help the collection data and elicitation process has a somewhat greater impact on the ratings provided by professionals for pairs that belong to the same cluster. However, the influence of POS tagging on the ratings given by professionals is relatively consistent for pairs within the same cluster and pairs in different clusters.
Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest
Authors: Alzawali Murad Ibrahim Husin , Yusoff Yusliza , Alwee Razana , Yunos Zuriahati Mohd , Talib Mohamad Shukor , Hassan Haswadi , AL-Dhief Fahad Taha , Albadr Musatafa Abbas Abbood , Alsemawi Majid Razaq Mohamed , Ahmad Sharifah Zarith Rahmah Syed
Published: February 2024

يتم تقييم معظم أنظمة التعرف على مشاعر الوجه البشرية على أساس الدقة فقط، حتى لو كان يُعتقد أيضًا أن معايير الأداء الأخرى مهمة في عملية التقييم مثل الحساسية والدقة وقياس F ومتوسط G. علاوة على ذلك، فإن المشكلة الأكثر شيوعًا التي يجب حلها في أنظمة التعرف على عواطف الوجه هي طرق استخراج الميزات، والتي يمكن مقارنتها بطرق استخراج الميزات اليدوية التقليدية. هذه الطريقة التقليدية غير قادرة على استخراج الميزات بكفاءة. بمعنى آخر، هناك كمية زائدة من الميزات التي تعتبر غير مهمة، والتي تؤثر على أداء التصنيف. في هذا العمل، تم اقتراح نظام جديد للتعرف على مشاعر الوجه البشري من الصور. يتم استخدام HOG (الرسوم البيانية للتدرجات الموجهة) للاستخراج من الصور. بالإضافة إلى ذلك، يتم استخدام الخوارزمية الجينية الثنائية (BGA) كاختيار للميزات من أجل تحديد الميزات الأكثر فعالية لـ HOG. تعمل Random Forest (RF) كمصنف لفئات مشاعر الوجه لدى الأشخاص وفقًا لعينات الصور. أمثلة الوجه البشري للصور التي تم استخراجها من مجموعة بيانات Yale Face، حيث تحتوي على تعبيرات الوجه البشري الأحد عشر هي كما يلي؛ عادي، نور يسار، بلا نظارات، فرح، وسط نور، حزين، نعسان، غمز ومتفاجئ. يتم تقييم أداء النظام المقترح فيما يتعلق بالدقة والحساسية (أي الاستدعاء) والدقة وقياس F (أي درجة F1) ومتوسط G. أعلى دقة لطريقة BGA-RF المقترحة تصل إلى 96.03%. علاوة على ذلك، كان أداء BGA-RF المقترح أكثر دقة من نظيراته. وفي ضوء النتائج التجريبية، أثبتت تقنية BGA-RF المقترحة فعاليتها في التعرف على مشاعر الوجه البشري باستخدام الصور.
Hybrid Framework To Exclude Similar and Faulty Test Cases In Regression Testing
Authors: Siddique Muhammad Asim , Wan-Kadir Wan M.N. , Ahmad Johanna , Ibrahim Noraini
Published: February 2024

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to identity and exclude redundant, similar and faulty test cases from the given regression suite. Our experimental results clearly show the benefits of the ETCPM framework in terms of reduction in the testing time, optimization of the resource allocation, and improvement in the overall quality of regression test suite. ETCPM enables software development teams to achieve faster and reliable regression testing by intelligent exclusion of similar and fault test cases, which yields in reduction in the software delivery cycles and better end user satisfaction.
Exploring Important Factors in Predicting Heart Disease Based on Ensemble- Extra Feature Selection Approach
Authors: Abubaker Howida , Muchtar Farkhana , Khairuddin Alif Ridzuan , Nuar Ahmad Najmi Amerhaider , Yunos Zuriahati Mohd , Salimun Carolyn
Published: February 2024

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficacy of several classification algorithms on four reputable datasets, using both the full features set and the reduced features subset selected through the proposed method. The results show that the feature selection technique achieves outstanding classification accuracy, precision, and recall, with an impressive 97% accuracy when used with the Extra Tree classifier algorithm. The research reveals the promising potential of the feature selection method for improving classifier accuracy by focusing on the most informative features and simultaneously decreasing computational burden.
Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature
Authors: Zhang Dayong , Abdul Wahab Nur Haliza , Zin Adi Wira Mohd
Published: February 2024

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm.
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
Authors: Kadhim Karrar A. , Mohamed Farhan , Najjar Fallah H , Salman Ghalib Ahmed
Published: February 2024

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images.
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
Authors: Suhaili Sinarwati Mohamad , Salim Naomie , Jambli Mohamad Nazim
Published: February 2024

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluated based on its sparse-categorical loss during training and BLEU in the inference phase, utilizing a neural generative attention-based additive scoring function. Despite memory constraints that limited ADAMW to ten epochs, this optimizer showed promising results compared to configurations using early stopping techniques. SGD provided higher BLEU scores for generalization but was very time-consuming. The results highlight the importance of finding a balance between optimization performance and computational efficiency, positioning ADAMW as a promising alternative when training efficiency and generalization are primary concerns.
Prioritized Text Detergent: Comparing Two Judgment Scales of Analytic Hierarchy Process on Prioritizing Pre-Processing Techniques on Social Media Sentiment Analysis
Authors: Zaki Ummu Hani’ Hair , Ibrahim Roliana , Abd Halim Shahliza , Kamsani Izyan Izzati
Published: February 2024

تستخدم معظم الشركات بيانات وسائل التواصل الاجتماعي للأعمال. يقوم تحليل المشاعر تلقائيًا بجمع التحليلات وتلخيص هذا النوع من البيانات. من الصعب إدارة بيانات وسائل التواصل الاجتماعي غير المنظمة. حيث تمثل البيانات المزعجة او غير متناسقة تحديًا لتحليل المشاعر. نظرًا لأن أكثر من 50٪ من عملية تحليل المشاعر هي معالجة مسبقة للبيانات، فإن معالجة بيانات الوسائط الاجتماعية الضخمة تمثل تحديًا أيضًا. إذا تم إجراء المعالجة المسبقة بشكل صحيح، فقد تتحسن دقة البيانات. أيضًا، يعتمد سير العمل في تحليل المشاعر بشكل كبير. نظرًا لعدم وجود تقنية معالجة مسبقة تعمل بشكل جيد في جميع المواقف أو مع جميع مصادر البيانات، فإن اختيار أهم المصادر أمر بالغ الأهمية. تحديد الأولويات هو أسلوب ممتاز لاختيار الأكثر أهمية.باعتبارها إحدى طرق اتخاذ القرار متعدد المعايير (MCDM)، تُفضل عملية التحليل الهرمي (AHP) التعامل مع تحديات صنع القرار المعقدة باستخدام عدة معايير. تم استخدام درجات نسبة الاتساق (CR) لفحص المقارنات الزوجية لتقييم AHP. استخدمت هذه الدراسة مقياسين للحصول على الحكم الأكثر اتساقًا. أولاً، مقياس حكم Saaty (SS)، ثم المقياس المتوازن المعمم (GBS).  لقد تم التحقق فيما إذا كان هناك مقياسان مختلفان لحكم AHP سيؤثران على صنع القرار. المعايير الرئيسية لتحديد أولوية تقنيات المعالجة المسبقة في تحليل المشاعر هي علامات الترقيم والإملاء والرقم والسياق. تحتوى هذه المعايير الأربعة أيضا معايير فرعية. تكون مقارنات الزوجية GBS أقرب إلى قيمة CR من SS، مما يقلل من نسب وزن البدائل.تشرح هذه الورقة كيف يساعد AHP في اتخاذ القرار المنطقي. يمكن أن يكون تحديد أولوية تقنيات المعالجة المسبقة باستخدام AHP نموذجًا لمراحل تحليل المشاعر الأخرى. باختصار، تضيف هذه الورقة مساهمة أخرى إلى مجال تحليلات البيانات الضخمة.
SBOA: A Novel Heuristic Optimization Algorithm
Authors: Diao Qi , Junaidi Apri , Chan WengHowe , Zain Azland Mohd , Yang Hao long
Published: February 2024

تم تقديم طريقة تحسين إرشادية جديدة تعتمد على الإنسان، تسمى خوارزمية التحسين المستندة إلى السنوكر (SBOA)، في هذه الدراسة. الإلهام لهذه الطريقة مستوحى من سمات نخبة المبيعات - تلك الصفات التي يطمح كل مندوب مبيعات إلى امتلاكها. عادةً ما يسعى مندوبو المبيعات إلى تعزيز مهاراتهم من خلال التعلم الذاتي أو من خلال طلب التوجيه من الآخرين. علاوة على ذلك، فإنهم يشاركون في اتصالات منتظمة مع العملاء للحصول على الموافقة على منتجاتهم أو خدماتهم. بناءً على هذا المفهوم، تهدف SBOA إلى إيجاد الحل الأمثل ضمن مساحة بحث معينة، واجتياز جميع المواضع للحصول على جميع القيم الممكنة. لتقييم جدوى وفعالية SBOA مقارنة بالخوارزميات الأخرى، أجرينا اختبارات على عشر وظائف ذات هدف واحد من الوظائف المعيارية لعام 2019 للحساب التطوري (CEC)، بالإضافة إلى أربع وعشرين وظيفة ذات هدف واحد من CEC لعام 2022. وظائف مرجعية، بالإضافة إلى أربع مشاكل هندسية. تم استخدام سبع خوارزميات مقارنة: خوارزمية التطور التفاضلي (DE)، خوارزمية بحث العصفور (SSA)، خوارزمية جيب التمام (SCA)، خوارزمية تحسين الحيتان (WOA)، خوارزمية تحسين الفراشة (BOA)، تحسين سرب الأسد (LSO)، و تحسين ابن آوى الذهبي (GJO). وتمت مقارنة نتائج هذه التجارب المتنوعة من حيث الدقة وسرعة منحنى التقارب. تشير النتائج إلى أن SBOA هو نهج مباشر وقابل للتطبيق ويتفوق بشكل عام على الخوارزميات المذكورة أعلاه.
Oil spill classification based on satellite image using deep learning techniques
Authors: Abba Abubakar Salihu , Mustaffa Noorfa Haszlinna , Hashim Siti Zaiton Mohd , Alwee Razana
Published: February 2024

التسرب النفطي هو تسرب في خطوط الأنابيب أو السفن أو منصات النفط أو الناقلات يؤدي إلى انطلاق المنتجات البترولية في البيئة البحرية أو على اليابسة بشكل طبيعي أو بسبب عمل بشري، مما يؤدي إلى أضرار جسيمة وخسائر مالية. تعد صور الأقمار الصناعية إحدى الأدوات القوية المستخدمة حاليًا لالتقاط المعلومات الحيوية والحصول عليها من سطح الأرض. لكن التعقيد والكم الهائل من البيانات يجعل من الصعب على البشر معالجتها ويستغرق وقتًا طويلاً. ومع ذلك، مع تقدم تقنيات التعلم العميق، أصبحت العمليات الآن محوسبة للعثور على المعلومات الحيوية باستخدام صور الأقمار الصناعية في الوقت الحقيقي. طبقت هذه الورقة ثلاث خوارزميات للتعلم العميق لتصنيف صور الأقمار الصناعية، بما في ذلك ResNet50، وVGG19، وInceptionV4؛ تم تدريبهم واختبارهم على مجموعة بيانات صور الأقمار الصناعية مفتوحة المصدر لتحليل كفاءة الخوارزميات وأدائها وربط دقة التصنيف والدقة والاستدعاء ودرجة f1. وأظهرت النتيجة أن InceptionV4 يعطي أفضل دقة تصنيف بنسبة 97% للغيوم والصحراوية والمناطق الخضراء والمياه، يليه VGG19 بنسبة 96% تقريبًا وResNet50 بنسبة 93%. أثبتت النتائج أن خوارزمية InceptionV4 مناسبة لتصنيف الانسكابات النفطية وعدم الانسكابات باستخدام صور الأقمار الصناعية على مجموعة بيانات تم التحقق من صحتها.
Qin Seal Script Character Recognition with Fuzzy and Incomplete Information
Authors: Ou Yun , Zhou Zhen-Jie , Kang Di-Wen , Zhou Pan , Liu Xue-Wei
Published: February 2024

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
Authors: Lin Shengqing , Ali Nor Azizah , Zain Azlan bin Mohd , Amin Muhalim Mohamed Amin
Published: February 2024

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.
Simplified Novel Approach for Accurate Employee Churn Categorization using MCDM, De-Pareto Principle Approach, and Machine Learning
Authors: Al Abid Faisal Bin , Bakri Aryati Binti , Alam Md. Golam Rabiul , Uddin Jia , Chowdhury Shefayatuj Johara
Published: February 2024

يعد طرد الموظفين من المنظمات مشكلة خطيرة. يجب حل مشكلة تدوير الموظفين أو اطردهم من داخل المنظمة نظرًا لأن لها تأثيرًا سلبيًا على المنظمة. يعد الاكتشاف اليدوي لتسرب الموظفين أمرًا صعبًا للغاية، لذلك تم استخدام خوارزميات التعلم الآلي ML  بشكل متكرر لاكتشاف تسرب الموظفين بالإضافة إلى تصنيف الموظفين وفقًا للاستبدالهم.
 باستخدام التعلم الآلي،  بحثت دراسة واحدة فقط في تصنيف الموظفين حتى الآن. تم اقتراح نهج جديد لاتخاذ القرار متعدد المعايير   MCDM  إلى جانب مبدأ DE-PARETO لتصنيف الموظفين. يشار إلى هذا باسم مخطط SNEC .
تم تصميم نموذج AHP-TOPSIS DE-PARETO PRINCIPLE (AHPTOPDE) الذي يستخدم نظام MCDM على مرحلتين لتصنيف الموظفين. في المرحلة الأولى، تم استخدام عملية التسلسل الهرمي التحليلي  AHP  لتعيين الأوزان النسبية لعوامل إنجاز الموظف. في المرحلة الثانية، تم استخدام TOPSIS للتعبير عن أهمية الموظفين في إجراء تصنيف الموظفين. تم تطبيق قاعدة 20-30-50 البسيطة في مبدأ DE PARETO لتصنيف الموظفين إلى ثلاث مجموعات رئيسية وهي الموظفون المتحمسون والسلوكيون والمضطربون. يتم بعد ذلك تطبيق خوارزمية الغابة العشوائية كخوارزمية أساسية لإطار عمل الموظفين المقترح للتنبؤ بخسارة الموظفين على أساس الفصل والذي يتم اختباره على مجموعة بيانات قياسية لنظام معلومات الموارد البشرية  HRIS، ويتم تقييم النتائج التي تم الحصول عليها باستخدام طرق تعلم الآلة الأخرى. تتمتع خوارزمية Random Forest ML في مخطط SNEC بدقة إجمالية مماثلة أو أفضل قليلاً وMCC مع تعقيد زمني أقل مقارنةً بمخطط ECPR باستخدام خوارزمية  CATBOOST
Sentiment Analysis on Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique
Authors: Noureen  , Huspi Sharin Hazlin Huspi , Ali Zafar
Published: February 2024

Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which performs better for Roman Urdu Sentiment analysis. Our suggested model employs the BiLSTM network to maintain the context in both directions and eventually, results for ternary classification are obtained by using the final softmax output layer. A manually labeled data set was used to evaluate the model, data is collected from the HEIs of Pakistan. Model was empirically evaluated on two datasets of Roman Urdu, the newly developed student’s feedback dataset and RUSA-19 publically available data set of Roman Urdu. Our model performs effectively using the word embedding and BiLSTM layer. The proposed model is compared with the baseline models of CNN, RNN, GRU and classic LSTM. The experimental findings demonstrate the proposed model's efficacy with an F1score of 90%.
Exploring the actual implementation of e-wallet application in Malaysia
Authors: Yeh Jia Yee , Seah Choon Sen , Loh Yin Xia , Low Mei Peng , Nuar Ahmad Najmi Amerhaider , Jalaludin Farah Waheeda
Published: February 2024

E-wallet, also referred to as digital wallet, is a software application designed to replace physical wallets, with the primary purpose of facilitating online transactions when users wish to make virtual payments. Nowadays, E-wallets are not limited to mobile applications, but they have also been extended to wearable devices, such as smartwatches, enabling users to make payments via their watches. This research study focuses on three main E-wallet service providers in Malaysia, namely TouchNGo E-wallet, Boost, and Grab pay, as they are the top three E-wallets in the country. The aim of this paper is to explore the real-world implementation of E-wallets among mobile phone users in Malaysia, employing the Technology Adoption Model as the theoretical framework. Six independent variables were identified to study the implementation of E-wallets, and a total of 500 respondents shared their opinions on retaining the usage of E-wallet in Malaysia. The data collected were analysed using SPSS for Pearson Correlation Analysis and Multiple Linear Regression. Out of the six variables, five independent variables were accepted, and one independent variable was rejected due to the greater significant level. The highest correlation coefficient falls under Business Resources with 0.704. The study delves into the implications and constraints, providing insights for the future advancement of E-wallets within the Malaysian context.
Plan de negocio para la creación de una plataforma online de venta de prendas de vestir y accesorios con diseños peruanos en el ámbito de Lima moderna
Authors:   , Huaira Ñahui Fran Luis , Diaz Quispe Prisset , Nakakado Sánchez Monica Kazuko , Paredes Otiniano Nelly Andrea , Remond Alayza Jean Patrick
Published: February 2024

El presente trabajo plantea una alternativa novedosa de plan de negocio que nos permite articular dos temáticas interesantes en la presente coyuntura mundial de la pandemia: las ventas online y la inclusión socioeconómica de los artesanos y diseñadores peruanos. La primera es una creciente tendencia que se ha ido adaptando muy bien y de manera rápida a las exigencias del consumidor online, mientras que la segunda posibilita la reactivación laboral y económica de estos pequeños productores. Para poder analizar la viabilidad del presente negocio, que llamamos Pachasanta Store, hemos realizado dentro del primer capítulo de este documento, la formulación de la idea de negocio, bajo el hallazgo de una demanda insatisfecha que da origen al modelamiento del producto y al mercado objetivo inicial. En el segundo capítulo, realizamos el análisis del entorno, detallando el análisis externo e interno. Al pasar al capítulo tercero, nos centramos en el Plan Estratégico de Pachasanta Store, dando origen a su visión, misión, valores, objetivos, su estrategia y las fuentes generadoras de ventaja competitiva frente a otros. En el cuarto capítulo, vamos aterrizando al negocio Pachasanta Store con un detallado Estudio de Mercado, en donde determinamos que nuestro público objetivo se ubica en Lima Moderna, hacemos un sondeo del mercado local para dar pase a la determinación del tamaño de la muestra, muy importante para deducir nuestro número de ventas diarias de productos y finalizamos este capítulo con la metodología de recolección de datos, en donde se realizaron encuestas online y entrevistas a profundidad, ambas de mucho valor para conocer las características principales del posible público consumidor de Pachasanta Store.
Evaluación de la percepción de inseguridad y el uso del espacio público del mercado modelo N° 1 del distrito de Chorrillos, Lima, 2018
Authors:   , Moreano Rueda Fortune
Published: February 2024

La percepción de inseguridad es aquella sensación de ansiedad o miedo a ser víctima de un delito generada por experiencias pasadas, ya sean propias o del entorno, que no solo trae consigo una falta de seguridad ciudadana y desconfianza en la población sino que genera también consecuencias negativas en la ciudad como el abandono de los espacios públicos y vida comunitaria, el cambio de hábitos de los individuos y la búsqueda por nuevas alternativas privadas que satisfagan las necesidades sociales. En Lima, la inseguridad ciudadana es el problema principal que más aqueja a los limeños, siendo los ciudadanos de la zona sur de Lima los que se sienten más inseguros. El objetivo central del presente estudio es analizar cómo el uso peatonal del espacio público ubicado al exterior del Mercado Modelo N°1 del distrito de Chorrillos influye en los niveles de percepción de inseguridad de sus usuarios, poniendo en evidencia en los siguientes capítulos cómo una adecuada planificación urbana con enfoque humano influye positivamente en los niveles de percepción seguridad de una ciudad. De esta manera, se hace una propuesta urbano arquitectónica a partir de una intervención urbana táctica para la recuperación del espacio y devolver al peatón su confianza, seguridad, apropiación por espacio público y sobre todo, calidad de vida.
Use of Climate Smart Agricultural Technologies in dry-season peri-urban agriculture in West Africa Sahel: A case study from Saga, Niger
Authors: Tambol¹ Tinawaen , Derbile Emmanuel K. , Soulé Moussa
Published: February 2024

Climate change affects peri-urban agricultural systems. However, most studies focused on impacts on peri-urban and urban agriculture. This study only investigated peri-urban farming systems in West African Sahel cities. Globally, agricultural productivity improvement requires applying technologies and resource access, particularly in dry-season farming. The achievements of Sustainable Development Goals (SDGs) in developing countries rely on utilising Climate-Smart Agriculture Technologies (CSAT) to address climate change, youth unemployment and food insecurity. The study employed a mixed-method research design, employing field and household surveys of 142 peri-urban smallholder farmers, key informants, and desktop-based research in collecting data. The results showed that biopesticides/crop and pest management are the most used CSAT in dry-season farming (p = .002). These technologies eradicate pests and disease outbreaks of crops, vegetables and farm animals. The other technologies included fertilizer micro dose, organic manure and compost application, flood-tolerant improved varieties, irrigation based on green energy, tele-irrigation, early maturing varieties and planting pits. These technologies were ranked 2nd, 3rd, 4th, 5th, 6th, 7th, 8th and 9th respectively, using mean weighted values. The study underpins local climate change trends and assessment, together with the availability, opportunities and implicit implications of scaling up CSAT. The study also recommends including peri-urban agriculture in climate and land use planning policy, programmes and projects in Niamey city.
Economic burden of breast cancer prevalence by 2030 in India: Projections using Autoregressive Integrated Moving Average (ARIMA) model
Authors: Pillai Rakesh N , Alex Aleena , S Narassima M , Verma Vivek , Shaji Ajil , Pavithran Keechilat , Vijaykumar D. K. , John Denny
Published: February 2024

Background
Breast cancer is an important public health issue in India comprising 28 percent of all diagnosed cancers with a high economic burden. This study provides a new way to predict the number of patients with breast cancer (based in prevalence rates) and estimated its economic burden in India based on the autoregressive integrated moving average (ARIMA) model.
Methods
Data on the number of patients with breast cancer in India from 2000 to 2019 (prevalence) were obtained from the Global Burden of Disease databases. The ARIMA model was used to fit and predict the number of patients with breast cancer (prevalence) till 2030. The direct and indirect economic burden of breast cancer was estimated by the bottom-up approach and the human capital approach, respectively.
Results
The results of coefficient of determination (0.99), mean absolute percentage error (25%), mean absolute error (1794.555), and root mean squared error (2233.528) showed that the ARIMA (2,2,0) model fitted well. Akaike information criterion (543.13) and Bayesian information criterion (546.69) indicated the ARIMA (1, 1, 1) model was reliable when analyzing our data. The result of the relative error of prediction (0.23%) also suggested that the model predicted well. The number of patients with breast cancer from 2019 to 2030 was predicted to be about 1.12 million, 1.18 million, 1.25 million, 1.32 million, 1.38 million, 1.44 million, 1.51 million, 1.56 million, 1.62 million, 1.68 million, 1.74 million, and 1.80 million respectively. The total economic burden of breast cancer from 2019 to 2030 was estimated to be $8.16 billion, $9.06 billion, $10.01 billion, $11.01 billion, $12.05 billion, $13.16 billion, $14.36 billion, $15.66 billion, $17.08 billion, $18.6 billion, $20.23 billion respectively.
Conclusion
In India, the number of people with breast cancer and its financial burden will both keep rising. Between 2020 and 2030, India will see an average annual increase in the number of breast cancer patients of 0.05 million (5.6 per cent). In the meantime, an average of $19.55 billion more would be spent annually on breast cancer in India.
Intercomparison of DCA and EPR scoring for validation with individual physical dosimetry
Authors: Chang Kang-Wei , Cheng Po-Ching , Lin Yi-Chun , Lee Shih-Ying , Huang Wen-Sheng , Chiu Chuang-Hsin
Published: February 2024

Radiation is widely used in industrial, medical, agricultural, and military applications. The improper use and disposal of manmade radiation sources have resulted in various events that have caused different levels of human radiation exposure. These experiences indicate that estimating only the amount of radioactive contamination and dose after an incident was not sufficient to realize in detail and determine the exact exposure dose owing to the incident. Therefore, a retrospective assessment of radiation exposure and dose reconstruction is a necessary and crucial method for risk analysis. In this studies, we used one healthy volunteer peripheral blood and fingernail samples for the project were irradiated with 0–5 and 0–20 Gy Co-60 irradiation for establish a dose-dependent curve with DCA and EPR dose-response curve assays, respectively. Further, another 20 volunteers participated in this clinical trial; their DCA and EPR scores were calculated, and followed by an evaluation of their equivalent radiation doses and corresponded with their monthly physical dosimetry. Through this study, we established the dose-response curve of DCA and EPR after radiation exposure, and calculated the correlation between physical dosimetry and biological doses by 20 volunteers participating in clinical trials. This evidence will promote estimating the personal absorbed doses and as a reference for medical treatment for the safety of radiation workers and normal populations.
Rationally designed catalytic nanoplatform for enhanced chemoimmunotherapy via deploying endogenous plus exogenous copper and remodeling tumor microenvironment
Authors: Sun Daxi , Yu Liting , Wang Gang , Xu Yuxue , Wang Peng , Wang Ningning , Wu Zhengyan , Zhang Guilong , Zhang Jia , Zhang Yunjiao , Tian Geng , Wei Pengfei
Published: February 2024

Chemodynamic therapy represents a novel tumor therapeutic modality via triggering catalytic reactions in tumors to yield highly toxic reactive oxygen species (ROS). Nevertheless, low efficiency catalytic ability, potential systemic toxicity and inefficient tumor targeting, have hindered the efficacy of chemodynamic therapy. Herein, a rationally designed catalytic nanoplatform, composed of folate acid conjugated liposomes loaded with copper peroxide (CP) and chloroquine (CQ; a clinical drug) (denoted as CC@LPF), could power maximal tumor cytotoxicity, mechanistically via maneuvering endogenous and exogenous copper for a highly efficient catalytic reaction. Despite a massive autophagosome accumulation elicited by CP-powered autophagic initiation and CQ-induced autolysosomal blockage, the robust ROS, but not aberrant autophagy, underlies the synergistic tumor inhibition. Otherwise, this combined mode also elicits an early onset, above all, long-term high-level existence of immunogenic cell death markers, associated with ROS and aberrant autophagy -triggered endoplasmic reticulum stress. Besides, CC@LPF, with tumor targeting capability and selective tumor cytotoxicity, could elicit intratumor MHC-II positive dendritic cells (mainly attributed to CQ) and tumor infiltrating CD8 positive T cells, upon combining with PD-L1 therapeutic antibody, induce significant anti-tumor effect. Collectively, the rationally designed nanoplatform, CC@LPF, could enhance tumor chemoimmunotherapy via deploying endogenous plus exogenous copper and remodeling tumor microenvironment.
Incorrect and incomplete distribution data can mislead species modeling: a case study of the endangered Litsea auriculata (Lauraceae)
Authors: Tan Chao , Ferguson David Kay , Yang Yong
Published: February 2024

Global warming has caused many species to become endangered or even extinct. Describing and predicting how species will respond to global warming is one of the hot topics in the field of biodiversity research. Species distribution modeling predicts the potential distribution of species based on species occurrence records. However, it remains ambiguous how the accuracy of the distribution data impacts on the prediction results. To address this question, we used the endangered plant species Litsea auriculata (Lauraceae) as a case study. By collecting and assembling six different datasets of Litsea auriculata, we used MaxEnt model to perform species distribution modeling and then conducted comparative analyses. The results show that the distribution of Litsea auriculata is mainly in the Dabie Mountain region, southwestern Hubei and northern Zhejiang, and that mean diurnal temperature range (bio2) and temperature annual range (bio7) play important roles in the distribution of Litsea auriculata. Compared with the correct data, the dataset including misidentified specimens leads to a larger and expanded range in the predicted distribution area, whereas the species modeling based on the correct but incomplete data predicts a smaller and contracted range. According to the analysis of the local protection status of Litsea auriculata, we found that only about 23.38% of this species is located within nature reserves, so there is a large conservation gap. Our study suggests that the accurate distribution data is important for species modeling, and incomplete and incorrect data normally gives rise to misleading prediction results. In addition, our study also revealed the distribution characteristics and conservation gaps of Litsea auriculata, laying the foundation for the development of rational conservation strategies for this species.
Volatility and Risk – Median Shortfall or Value at Risk
Authors: Ditlhong Kago
Published: February 2024

The value-at-risk (VaR) is a widely used risk measure by financial institutions and regulators even though it fails to capture risk at the tail end. The purpose of this research is to contribute to the ongoing discourse on risk management by assessing the median shortfall (MS) as an alternative risk measure by comparing it with the VaR. We investigate the performance of VaR forecasts considering the following models: historical simulation (HS) and Glosten-Jagannathan-Runkle-generalized autoregressive conditional heteroscedasticity (GJR-GARCH). For MS forecasts, we consider only the HS. The comparison was based on an accuracy test and loss function on the following stock indices: the S&P 500, the Russell 2000 small cap and the DAX (Germany) from January 1, 2006, to December 31, 2020. Empirical analysis indicated the superiority of the MS approach in capturing tail risk for the overall testing period. In addition to the aforementioned findings, there was a gain in using the GJR-GARCH(1, 1) VaR in capturing tail risk, especially in turbulent periods.
Diversität in Erklärvideos auf YouTube
Authors: Honkomp-Wilkens Verena , Wolf Karsten D.
Published: February 2024

YouTube ist ein zentrales Social Video Network für Jugendliche und junge Erwachsene. Bisherige Untersuchungen zur genderspezifischen Darstellung von YouTuber:innen weisen auf eine genderstereotype Darstellung hin. Dieser Beitrag fokussiert die Frage, inwiefern mit stereotypen Darstellungen von Geschlecht in Erklärvideos und Tutorials auf YouTube gebrochen wird und mit welchen Differenzkategorien Gender dabei verwoben ist. Auf Basis einer Analyse von 59 Interviews mit 14- bis 22-Jährigen werden zunächst beliebte YouTube-Kanäle zu schulischen und freizeitbezogenen Themen identifiziert und dann bzgl. ihrer Diversität (Gender, Alter, Race/ethnicity) analysiert. In einem zweiten Schritt werden die Bedeutung von Diversitäts-Dimensionen auf die Auswahlstrategien der Rezipient:innen sowie deren Zuschreibung von (Erklär-)Kompetenz und Fachexpertise der Videoprotagonist:innen analysiert. Im audio-visuellen Bildungsraum YouTube finden sich Belege für eine vergleichsweise höhere Diversität der erklärenden Videoprotagonist:innen in ausserschulischen im Vergleich zu schulischen Themenfeldern, in denen überwiegend weiss und männlich gelesene Personen im Alter zwischen Anfang Zwanzig bis Mitte Vierzig zu sehen sind. Frauen* und PoC werden beinahe ausschliesslich in freizeitorientierten Themenfeldern sowie insgesamt häufiger in englischsprachigen Erklärvideos rezipiert. Doch auch der Bereich der ausserschulischen Erklärvideos und Tutorials ist durch eine genderspezifische Ordnung gekennzeichnet, in deren Rahmen Themenfelder entsprechend stereotypen Vorstellungen von Männlichkeit und Weiblichkeit bespielt werden. Diese Ordnung wird von den Schüler:innen überwiegend nicht hinterfragt, sondern aus stereotypen Vorstellungen über die Wahrnehmung von Geschlecht begründet. Davon abweichende Selbstinszenierungen werden als potenzielle Bereicherung – allerdings nur für Mädchen und Frauen – beschrieben. Aus den Ergebnissen können sowohl medienpädagogische Bedarfe bzgl. einer gendersensiblen Medienbildung identifiziert werden als auch vertiefende Forschungsfragen zu sprachraum- und themenbezogenen Unterschieden bildungsbezogener Stereotypisierungen abgeleitet werden.
The use of blockchain in the insurance industry: A bibliometric analysis
Authors: Fathi Eletter Shorouq
Published: February 2024

Blockchain technology has surfaced as a formidable catalyst, capable of reshaping the operational dynamics of conventional businesses. The integration of blockchain technology within the insurance industry has prompted a need for comprehensive understanding and assessment. This study aims to elucidate the relevance of blockchain in the insurance sector, identify key trends, influential research, and the overarching goals motivating the incorporation of blockchain in insurance practices. The study employed a systematic review to collect 125 research articles published between 2017 and 2023 focused on blockchain in insurance from the Web of Science database. This study used Biblioshiny. The analysis encompasses a wide range of parameters, including publication trends, influential authors, thematic clusters, and the geographic distribution of research. IEEE Access emerges as a prominent platform, hosting 11 articles dedicated to this topic. The results of keyword co-occurrence highlighted two keywords: blockchain and insurance. The results of the country collaboration map highlighted particular shortcomings, such as the concentration of research in countries like China, the USA, India, KSA, Malaysia, and Australia. A lack of research in developing and underdeveloped countries and insufficient knowledge sharing among researchers highlights a research gap. In conclusion, the analysis offers valuable insights into the evolving landscape of blockchain within the insurance industry.
Influence of sludge deposit layer on sludge treatment reed beds treating septage
Authors: Huong Yu Zhe , Tan Yee Yong , Tang Fu Ee , Saptoro Agus
Published: February 2024

There is a lack of study on the influence of the sludge deposit layer on the performance of sludge treatment reed beds (STRBs) treating septage as well as the sludge deposit characteristics due to the operational parameters of solids loading rate (SLR) and resting periods. The laboratory-scale STRB was operated under varying SLRs and resting periods to investigate the sludge deposit characteristics, dewatering efficiency, and the quality of the rejected water. Inadequate operation, such as excessive loading and insufficient resting period, resulted in prolonged surface ponding, and the total solids (TS) content failed to meet the disposal standard of 20%. This study found that the STRB operated with 100 kg/m2/year SLR and 6-day resting period performed the best in sludge dewatering and stabilization, as the final TS content and total volatile solids/TS ratio of the sludge deposit were 23 and 33%, respectively. Meanwhile, the moisture content always remained higher than the plastic limit, preventing severe surface cracking that led to poor rejected water quality. In addition, the average COD and TS removals in the rejected water from all STRBs reached 96 and 88%, respectively, revealing that the proposed system is promising and reliable in treating septage.
Metabolic Derangement in Non-Alcoholic Fatty Liver Disease: Opportunities for Early Diagnostic and Prognostic Markers
Authors: Verma Abhinav , Rishabh Mittal , Mathiyazhagan Navaneethakannan , Ahirwar Sonu Singh , Mukherjee Sukhes , Kotnis Ashwin
Published: February 2024

Non-alcoholic fatty liver disease is a globally prevalent disorder that can
rapidly progress if not detected early. Currently, no accepted markers exist for early
diagnosis and prognosis of NAFLD. This review describes derangement in major
metabolic pathways of lipid, carbohydrate, and amino acids in NAFLD. It suggests that
measuring levels of thrombospondin, TyG index, asymmetric dimethylarginine, LAL-A,
GLP-1, FGF-21, and GSG index are potential markers for early diagnosis of NAFLD. A
single marker may not indicate early NAFLD, and further large-scale studies on
correlating levels of Thrombospondin-2, triglyceride-glucose index, and FGF-21 with
NAFLD are warranted.
The Role of Quorum Quenching in Medical Application
Authors: Mohammad Ghada A. , Waleed Hadi Huda
Published: February 2024

The attempts are continuing in the various fields of life sciences to resolve a big problem, which is the ability
of bacteria to cause pathogenicity for humans, animals, and plants, whether by chemical or biological methods
and in ways that are hoped to be safe. Among these attempts, the control of the Quorum Sensing (QS) mechanism that occurs naturally in bacteria under certain conditions helps to increase the virulence of bacteria,
starting from its ability to adhere and form a biofilm. Then, the tissues are invaded with various enzymes
according to the tissue type, increasing antibiotic resistance. Therefore, the idea came to solve these problems
through a mechanism opposite to the Quorum Quenching (QQ), which lies in the investigation of substances
that can disrupt the QS pathway, whether at the molecular level or the physiological level, as well as benefiting
from different organisms (Prokaryotes or Eukaryotes) that live in the same environment and produce substances that inhibit bacterial signaling molecules. Lastly, the discovery of varying novel QQ agents from extreme environmental bacteria will be most interesting in the future.
Keywords: Quorum sensing, quorum quenching, acyl homoserine lactones, medical application
Digital Smoking Cessation Intervention for Cancer Survivors: Analysis of Predictors and Moderators of Engagement and Outcome Alongside a Randomized Controlled Trial
Authors: Andree Rosa , Mujcic Ajla , den Hollander Wouter , van Laar Margriet , Boon Brigitte , Engels Rutger , Blankers Matthijs
Published: February 2024

Background

Recent studies have shown positive, though small, clinical effects of digital smoking cessation (SC) interventions for cancer survivors. However, research on associations among participant characteristics, intervention engagement, and outcomes is limited.

Objective

This study aimed to explore the predictors and moderators of engagement and outcome of MyCourse-Quit Smoking (in Dutch: “MijnKoers-Stoppen met Roken”), a digital minimally guided intervention for cancer survivors.

Methods

A secondary analysis of data from the randomized controlled trial was performed. The number of cigarettes smoked in the past 7 days at 6-month follow-up was the primary outcome measure. We analyzed interactions among participant characteristics (11 variables), intervention engagement (3 variables), and outcome using robust linear (mixed) modeling.

Results

In total, 165 participants were included in this study. Female participants accessed the intervention less often than male participants (B=–11.12; P=.004). A higher Alcohol Use Disorders Identification Test score at baseline was associated with a significantly higher number of logins (B=1.10; P<.001) and diary registrations (B=1.29; P<.001). A higher Fagerström Test for Nicotine Dependence score at baseline in the intervention group was associated with a significantly larger reduction in tobacco use after 6 months (B=–9.86; P=.002). No other associations and no moderating effects were found.

Conclusions

Overall, a limited number of associations was found between participant characteristics, engagement, and outcome, except for gender, problematic alcohol use, and nicotine dependence. Future studies are needed to shed light on how this knowledge can be used to improve the effects of digital SC programs for cancer survivors.

Trial Registration

Netherlands Trial register NTR6011/NL5434; https://onderzoekmetmensen.nl/nl/trial/22832