bluuebunny/crossref_metadata_2025 huggingface dataset:
Purchasing planning for pharmaceuticals inventory: a case study of drug warehouse in hospital
Authors: Yawara Praphan , Supattananon Naratip , Siwapornrak Pinpicha , Akarrungruangkul Raknoi
Published: July 2023

Lack of purchasing planning and proper demand forecasting causes hospitals to suffer from drug inventory mismatches with actual demand; in other words, the inventory management cost is high if the quantity exceeds or less the demand. Therefore, this research aimed to plan an appropriate inventory purchase to reduce inventory costs and effectively meet the hospital's pharmaceutical inventory needs in a case study: i) demand forecasting for 29 AV drugs using Minitab 19, ii) economic order quantity (EOQ) and Newsboy form when drug demand is stable and non-steady, respectively, and iii) design a ready-made program using Excel program to help control, make purchase decisions and be easy to use. There were 5 forecasting methods used. Each drug forecasting method was selected from the one with the slightest error. Twenty-four drugs and five drugs were determined using EOQ and Newsboy forms for re-order point (ROP), safety stock (SS), and total costs. The total cost of drug inventory management per year was 1,780,336.98 baht; compared with the current method, it reduced the cost by 506,569.10 baht per year or a 22.15% reduction.
Intelligence feeder system for stray cats
Authors: Saadoon Alaa Mohsin , Koyuncu Hakan
Published: July 2023

Recently, the care of stray cats has become an important matter, given the difficulty for these animals to obtain food or water, whether in wild or remote areas, and this may cause the death of these animals. The aim of this work is to design and implement a low-cost feeding system for feral and indoor cats. The system is controlled by a jetson nano and arduino mega 2,560. The cat detection feeder system is built using the single shot multibox detector (SSD) MobileNet V2 algorithm. The system provides food and water for the cats. The SSD on jetson nano is implemented in real time. Jetson nano takes a picture using a webcam then runs the SSD algorithm. When a cat is detected, the arduino turns on a servo motor and a water pump. The system also includes a sim800l and GPS NEO-6M modules to send an alert message when the food and water tanks are empty. The message also contains the location of the feeder. When testing the system to determine the effectiveness of the functions, the SSD algorithm succeeded in recognizing cats, the system successfully provided food for the cats, and all parts of the device worked in high harmonic.
Performance analysis of bitcoin forecasting using deep learning techniques
Authors: Tripathy Nrusingha , Hota Sarbeswara , Mishra Debahuti
Published: July 2023

The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.
GPS-based fall detection system for old and specially-abled people
Authors: Farhan Mazin N. , Ayoub Mohammed G. , Al-Jader Ali Rakan
Published: July 2023

Falls are a serious public health concern for older people across the world. Modern telemedicine now depends heavily on remote monitoring of older patients and the ability to spot threats to human health. If a fall is not assisted in time, it can significantly reduce an older person's mobility, independence, and his/her quality of life. Older people who experience post-traumatic problems or mortality frequently do so because of falls. As a result, preventing falls consequences or providing essential help on time may depend on the early identification of falls. In this article, we propose an internet of things (IoT) based system that makes use of low-power wireless sensor networks, smart devices and cloud computing to detect falls and track positions for older and specially-abled people. The tracking is done by sending links of positions from the proposed system every 15 seconds to a specified google drive. On the other hand, an alert message will be delivered to the caregiver whenever a fall is happened. Thus, a MPU-6050 sensor and NEO-6M global positioning system (GPS) module are used with ESP32 microcontroller for the aforementioned purposes. A pilot study with several protocols was carried out to validate the cost-effective proposed system and achieved good results.
Arabic text steganography using lunar and solar diacritics
Authors: Ali Rawaa Hamza , Dhannoon Ban Nadeem , Hamel Mohamed Iedan
Published: July 2023

The need to hide essential information has rapidly increased as mobile devices and the internet has overgrown. Steganography is a method created to create hidden communication. Recently, methods have been developed to hide important information using text steganography. This work-study takes advantage of the possibility of concealing data in all diacritics after the two letters ال)) in the cover text. In the presented study, we propose a new algorithm in steganography in Arabic text as a cover text. After pre-processing the cover text, the algorithm hides the elements of secret messages inside the Arabic letters by adding appropriate diacritics (like Hamzah Al-Wasl) on the extracted words beginning with (ال) according to its third letter type (solar or lunar). In the proposed algorithm, the length of the secret message is determined so that the intended recipient can extract the hidden message accurately. The proposed algorithm is robust against the attack because the change in the cover text is small and imperceptible. On the other hand, since Arabic is used as a cover text, the breadth of the inclusion depends on the number of words beginning with (ال) definition.
Predictive source management for low power domestic direct current grids
Authors: Narayan Satish B. Ashwath , Usha Pasumarthi
Published: July 2023

The decrease in the price of solar photo voltaic (PV) panels has led to the widespread adoption of the solar power as a renewable energy source, not only at the grid level but also on the roof tops of the residential buildings. The solar PV panels produce direct current (DC) and can be readily used to drive DC powered loads or charge batteries. Direct powering of the loads from the solar panels is hindered by the highly variant nature of solar power generation which depends on a number of external as well as internal factors. The paper proposes a prediction based direct connection between PV panel and DC loads, only when the panel will be able to supply the required power to the load. The loads have been categorized based on how they need to be powered and their priority levels. The data specific to the rooftop was collected over two years using which the prediction of the panel output was carried out. Combining the load categorization and the power prediction a smart management system was designed which was able to decide on how the loads need to be powered and hence were connected to the appropriate source.
Enterprise information security risks: a systematic review of the literature
Authors: Sandoval Jenner Lavalle , Celis Domingo Hernández , Cabanillas-Carbonell Michael , Andrade-Arenas Laberiano
Published: July 2023

Currently, computer security or cybersecurity is a relevant aspect in the area of networks and communications of a company, therefore, it is important to know the risks and computer security policies that allow a unified management of cyber threats that only seek to affect the reputation or profit from the confidential information of organizations in the business sector. The objective of the research is to conduct a systematic review of the literature through articles published in databases such as Scopus and Dimension. Thus, in order to perform a complete documentary analysis, inclusion and exclusion criteria were applied to evaluate the quality of each article. Then, using a quantitative scale, articles were filtered according to author, period and country of publication, leaving a total of 86 articles from both databases. The methodology used was the one proposed by Kitchenham, and the conclusion reached was that the vast majority of companies do not make a major investment in the purchase of equipment and improvement of information technology (IT) infrastructure, exposing themselves to cyber-attacks that continue to grow every day. This research provides an opportunity for researchers, companies and entrepreneurs to consult so that they can protect their organization's most important assets.
Leukemia detection system using convolutional neural networks by means of microscopic pictures
Authors: Shafi Pathan Mohd , Bidve Vijaykumar , Bhapkar Haribhau , Dhotre Prashant , Singh Veer Bhadra Pratap
Published: July 2023

All over the world, there are a significant number of patients suffering every year from blood cancer. Most of the people are unaware of the risk involved in such a disease. A majority of these diseases are dangerous and may cause death. The patient who have been diagnosed with such a disease, feels very afraid. The patient may feel that the disease is very uncontrolled. Such diseases are very uncommon, and the patient may get very less assistance and information available about this disease. This symptom is called as acute lymphocytic leukemia (ALL) in medical science. In such a kind of cancer, white blood cells are mostly affected. In case of children, this disease is mainly detected i.e. children are more prone to this disease. If the disease is diagnosed in the early stage, the chances of recovery are maximum. Hence, there should be an accurate and guaranteed mechanism available to detect such type of blood cancers in the patients. This work proposes a system to distinguish the three different types of ALL using a convolutional neural network (CNN) by means of microscopic pictures of peripheral blood smears (PBS) and obtain accuracy levels that surpass those of practicing physicians.
Custom application programming interface data extractor applied to the Klarna e-commerce dataset
Authors: Attaoui Anas El , Boukhalfa Alaeddine , Rhouas Sara , Hami Norelislam El
Published: July 2023

The use of smart technologies, the internet of things (IoT), social media, and others produce a billion or more pieces of data in different formats. Big data has risen to become the most sought-after field in computer science. The e-commerce evolved significantly and continued to flow until now and even after the pandemic. So, big data technologies helped with the development and approach to collecting, storing, processing, and extracting the data in this field. This paper proposes an application programming interface (API) data extractor tool applied to a collection of e-commerce public websites named “Klarna dataset” to extract its data, and an analysis of the results. The study of e-commerce sales has given results matching universal e-commerce sales tendencies. The peak of the number of e-commerce transactions and sales was between 2018-2019. Thus, the highest e-commerce sales price was in the United States for “luxury” or “fancy” products, and the highest sales in Europe were in Frankfurt, Germany, for hardware and gaming material.
A scoping review of topic modelling on online data
Authors: Mohd Sharif Mohd Mukhlis , Maskat Ruhaila , Baharum Zirawani , Maskat Kamaruzaman
Published: July 2023

With the increasing prevalence of unstructured online data generated (e.g., social media, online forums), mining them is important since they provide a genuine viewpoint of the public. Due to this significant advantage, topic modelling has become more important than ever. Topic modelling is a natural language processing (NLP) technique that mainly reveals relevant topics hidden in text corpora. This paper aims to review recent research trends in topic modelling and state-of-the-art techniques used when dealing with online data. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology was used in this scoping review. This study was conducted on recent research works published from 2020 to 2022. We constructed 5 research questions for the interest of many researchers. 36 relevant papers revealed that more work on non-English languages is needed, common pre-processing techniques were applied to all datasets regardless of language e.g., stop word removal; latent dirichlet allocation (LDA) is the most used modelling technique and also one of the best performing; and the produced result is most evaluated using topic coherence. In conclusion, topic modelling has largely benefited from LDA, thus, it is interesting to see if this trend continues in the future across languages.
Early fire detection technique for human being using deep learning algorithm
Authors: Deeba Kannan , Dasarathan Sattianadan , Kandula Srinivasa Rao , Sheela Krishnasamy Selva , Ramkumar Ravindran , Ashokkumar Nagarajan , Karthikeyan Dhandapani
Published: July 2023

Fire and smoke detection in today’s world is a must, especially in clustered areas where a quick response can prevent significant damages and save lives. Early detection plays a significant role in preventing the fire from spreading by alerting the emergency response personnel. It may not be possible to install traditional fire and smoke detectors everywhere. As a result, incorporating fire and smoke detection into existing closed circuit television (CCTV) systems in various places can provide a warning to the appropriate authorities, allowing for quick action to prevent the fire from spreading. This work aims in developing an early fire and smoke prediction model with CCTV footage images and video frames. The images and videos are collected from multiple datasets available online. A convolutional neural network (CNN) model is developed for early detection and prevention of the spreading of fire and compares it with transfer learning models ResNet50 and VGG19. The model obtain an accuracy of around 94% using CNN model, 95% using VGG19 and 98% using ResNet 50. A model with high accuracy can replace traditional fire detection systems which can be both cost-effective and easy to implement to existing surveillance cameras.
A machine learning model for predicting recovery rates of COVID-19 patients
Authors: Abdo Amany , Elzalama Kholoud Mohamed , Yakoub Ahmed Elsayed
Published: July 2023

During disease epidemics, any trial to improve healthcare systems entails preserving lives. Therefore, predicting which patients are at high risk becomes critical and challenging when confronted with a novel virus. The recent COVID-19 changed many people’s perspectives on how to approach diseases. According to the lack of medical resources, it is important to identify the patients who need instant medical care. This research proposes a machine learning model to identify high-risk patients that require specific medical attention. Specifically, extreme gradient boosting (XGboost), random forest (RF), and logistic regression (LR) are used in the ensemble method to classify COVID-19 patients at high risk. The dataset consists of 361 medical records for severe COVID-19 patients which have included 195 survivors. The most correlated features (neutrophils (%), hypersensitive c-reactive protein, lactate dehydrogenase, age, procalcitonin, and neutrophils count) are selected to be used in classification. Different machine learning classifiers are applied to the mentioned dataset to find out the optimum classifiers to be used in the ensemble method. 98% is the most optimal accuracy achieved with the proposed model.
Design of intelligent agent on Moodle to automate the learning assessment process
Authors: Ouassam Elhoucine , Hmina Nabil , Bouikhalene Belaid
Published: July 2023

Assessment is a key element in today’s School system, whether face-to-face or distance learning, as it helps students understand their learning and get feedback on their progress. In addition, distance learning assessment is becoming increasingly popular as it is convenient for students with busy schedules who cannot attend face-to-face assessments. In this paper, we focus on the use of intelligent agents on the Moodle platform to improve the assessment process of distance learning. We present three contributions that aim to improve the developed models: firstly, the digitisation of assessment to collect, store and analyse data; secondly, the adoption of a multi-agent skills assessment environment to automate some assessment tasks; thirdly, the adoption of the leadership and management development (LMD) programme to improve the continuous training of learners by offering greater flexibility, adaptability and relevance to their needs.

 
Conceptualising flood warning system for connected vehicles
Authors: Sung Feu Che , Razak Siti Fatimah Abdul , Yogarayan Sumendra , Abdullah Mohd. Fikri Azli , Kamis Noor Hisham , Azman Afizan
Published: July 2023

Floods are a common natural hazard in Malaysia during the monsoon season. It affects millions of people each year that leads to severe deaths and infrastructure destruction. In recent time, flood warning system (FWS) has been a notable topic but it has not been extensively implemented in Malaysia. In this study, we developed a FWS that can interface with connected vehicles in order to provide alerts to drivers while also sending warnings to end users. This type of FWS enables vehicles to connect with one another within a particular radius to broadcast flood information via long range (LoRa) communication technology. When the water level rises over a certain point, the system sends a warning to drivers indicated through a mobile application. Drivers have the option to take alternative route, reducing the likelihood of damage when driving into or near a flooded area. The developed application demonstrates that the warning was able to be instantly displayed to the driver if there is a significant increase in water level. Experimental evidence shows that the driver was able to receive the water level alert and a visual interpretation of the immediate area affected by flood through the application.
Conceptual framework of recommendation system with hybrid method
Authors: Panyatip Tammanoon , Kaenampornpan Manasawee , Chomphuwiset Phatthanaphong
Published: July 2023

Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.
Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network
Authors: Bharamagoudar Shobha R. , Saboji Shivakumar V.
Published: July 2023

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These networks pose several challenges such as network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes are considered as promising technique solution to deal with these issues. In this domain, opportunistic routing has gained huge attention because it follows the broadcasting nature of wireless communication and focus on selection of relay node for packet transmission to ensure the better quality of service (QoS) and energy efficiency. This work focuses on optimizing the next hop selection process and introduced reinforcement learning approach which considers distance, energy and link connectivity to assign the reward for different actions to identify the suitable relay node. Moreover, geo-context and social behaviour based opportunistic routing models are used to increase the reliability of next hop selection. Similarly, social model considers social profiling, social connectivity, and social interaction model to identify the relay node. The outcome of proposed approach is compared with several existing approaches such as prophet, spray and wait, and epidemic routing in terms of packet delivery, and network overhead. The relative study shows that the proposed approach achieves the average packet delivery as 47.22% and minimizes the network overhead.
Study and analysis of electric vehicles adoption: a middle eastern country as a case study
Authors: Al-Ani Muzhir Shaban , Mohammed Thabit Sultan , Aljebory Karim M.
Published: July 2023

Adaption of hybrid electric vehicles (HEVs) and electric vehicles (EVs) is an important choice that has many positive environmental and economic impacts, where, it helps in reducing exhaust emission, participation in dropping down the amount of noise pollution, and improving the air quality. With these encouraging impacts as well as the reduction in the price of fuel consumption, electric vehicles become at the top of car industry. A study on electric vehicles and their impact on improving the quality of environment is the main motive of this research. Selected theories and factors related to the electric vehicles’ adoption are investigated and analysed. The main factor that led to the spread of electric vehicles is the reliance on electric energy, so it is considered as zero emissions. But there is another effect, which is that these vehicles need continuous electric charging daily, which in turn needs to generate electricity, which leads to environmental pollution indirectly.
Airport traffic optimisation and airdrome analysis using mathematical modelling
Authors: Zbakh Douae , Benhadou Marwane , Benkacem Abderrahmane , Lyhyaoui Abdelouahid
Published: July 2023

An airport is considered to be a complex system, so managers nowadays are looking for verifiable solutions that could be tested and virtually simulated, in order to optimize safety, improve regularity, and reduce delays and risks of collision. To respond to this dilemma, many solutions were tested using safe simulation methods. The specific problem tackled in this paper refers to aerodrome capacity optimization, and specialy arcrafts mouvements. For this purpose, we introduce a mathematical model that was applied to Mohamed V Casablanca Airport, and the corresponding event actualization and graph was constructed taking into account uncertainty in flight schedules, aircraft types and their stand. Finally, the airdrome model was simulated using a special parameter in Arena tool.
Investigation into facial expression recognition methods: a review
Authors: Devarapalli Ajaykumar , Gonda Jora M.
Published: July 2023

Facial expression recognition (FER) is a rapidly emerging topic in computer vision that has gotten a lot of interest because of its numerous applications in fields including psychology, sociology, human-computer interaction (HCI), and security. FER seeks to recognise and analyse human facial expressions in order to determine emotions and other mental states. Several strategies, including feature-based, kernel-based, and deep learning-based methods, have been developed and implemented in FER in recent years. FER’s major goal is to extract and identify the most discriminating elements that accurately represent the emotions expressed by facial expressions. The literature reviewed in this field shows that deep learning-based methods have outperformed traditional feature-based and kernel-based methods in terms of accuracy and robustness in recognizing facial expressions. However, these deep learning-based methods also pose several challenges, such as the need for large labeled-data-sets, robustness to different facial poses and illumination conditions, and generalization to unseen data. Despite these challenges, the field of FER is expected to continue growing, and future research will likely focus on addressing these challenges and improving the accuracy and robustness of FER systems.
Cyber security: performance analysis and challenges for cyber attacks detection
Authors: Salih Azar Abid , Abdulrazzaq Maiwan Bahjat
Published: July 2023

Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.
Identification of mango variety using near infrared spectroscopy
Authors: Tripathi Mukesh Kumar , Reddy Praveen Kumar , Neelakantappa M. , Andhare Chetan Vikram , Shivendra Shivendra
Published: July 2023

The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation de- duction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4.5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4.5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.
Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks
Authors: Kerrakchou Imane , Hassan Adil Abou El , Chadli Sara , Emharraf Mohamed , Saber Mohammed
Published: July 2023

With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic investigation and protection countermeasures must be evolved. These countermeasures comprise network forensics and network intrusion detection systems. To this end, a well-organized and representative data set is a crucial element in training and validating the system's credibility. In spite of the existence of multiple networks, there is usually little information provided about the botnet scenarios used. This article provides the Bot-IoT dataset that embeds traces of both legitimate and simulated IoT networks as well as several types of the attacks. It provides also a realistic test environment to address the drawbacks of existing datasets, namely capturing complete network information, precise labeling, and a variety of recent and complex attacks. Finally, this work evaluates the confidence of the Bot-IoT dataset by utilizing a variety of machine learning and statistical methods. This work will provide a foundation to enable botnet identification on IoT-specific networks.
Identification and categorization of diseases in arecanut: a machine learning approach
Authors: Hegde Ajit , Sadanand Vijaya Shetty , Hegde Chinmay Ganapati , Naik Krishnamurthy Manjunath , Shastri Kanaad Deepak
Published: July 2023

Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural network (CNN) to identify and categorize diseases in arecanuts, trunks and leaves also suggesting effective preventative measures. Proprietary dataset consists of 1,100 photos of healthy and diseased arecas. The ratio between the train and test data is 80:20. Binary cross entropy is employed as the loss function for model construction, with accuracy serving as the metrics and Adam serving as the optimizing function. In identification and categorization of arecanut diseases, the suggested approach was shown to be efficient with 93.05% accuracy.
Automated decision classification model for tax appeals commission in Morocco using latent dirichlet allocation
Authors: Aouichaty Soufiane , Maleh Yassine , Hajami Abdelmajid , Allali Hakim
Published: July 2023

This research paper focuses on extracting and classifying information from the Moroccan National Tax Appeals Commission, which is presently nonexistent in the country's legal and tax landscape. This study examines 201 decisions selected from a pool of 562, released between 1999 and 2018, pertaining to corporate tax and involving 550 disputes spanning various corporate tax classifications. The paper aims to propose latent dirichlet allocation (LDA) for topic modeling and compare it with our previous results obtained from the bidirectional encoder representations from transformers (BERT) model. The findings suggest that the rulings can be classified into two primary classifications: those that uphold or reject the tax administration's position. The proposed model shows a good performance, achieving a precision of 9.25% and an accuracy of 9.51%. This highlights the effectiveness of both LDA and BERT models for understanding and classifying topics in tax decision analysis.
Kidney stones detection based on deep learning and discrete wavelet transform
Authors: Tahir Fouad Shaker , Abdulrahman Asma Abdulelah
Published: July 2023

The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).
El desafío del desarrollo. trayectorias de los grandes economistas latinoamericanos del siglo XX
Authors: López Terán Héctor
Published: August 2023

El libro “El desafío del desarrollo. Trayectorias de los grandes economistas latinoamericanos del siglo XX” coordinado por Juan Odisio y Marcelo Rougier - publicado en Santander, España en 2022 por la editorial de la Universidad de Cantabria y la Universidad de Rosario, Colombia, dentro de su Colección Sociales #72 - reúne y despliega la configuración intelectual de diez economistas latinoamericanos atraídos por los problemas del desarrollo de la región. Desde una exploración sustancial, histórica e intelectual, muestran el entrecruce entre reminiscencias personales, desarrollo profesional y evolución intelectual de autores concernientes al núcleo cardinal del estructuralismo latinoamericano.
Por uma sociologia para a emancipação: Pablo González Casanova e a sociologia militante latino-americana
Authors: Barbosa Lia Pinheiro
Published: August 2023

O presente artigo tem por objetivo apresentar a contribuição teórico-analítica e de método de Pablo González Casanova (1922-2023) à construção de uma sociologia militante e para a emancipação. Para tanto, recupero quatro categorias da teoria social do pensador mexicano, as quais considero centrais em seu pensamento intelectual e que demarcam o trânsito entre a sociologia da exploração à sociologia da emancipação: o colonialismo, a exploração, hegemonia e crise, e a autonomia de classe. Ao apresentá-las, destaco as rotas analíticas de González Casanova e, em particular, suas articulações com a história, a cultura e as lutas empreendidas pelo campo popular para pensar e construir horizontes emancipatórios.
Rediscovery of a syntype of Chalcocopris hesperus (Olivier, 1789) (Coleoptera, Scarabaeidae, Scarabaeinae), a possible specimen from Captain Cook’s first voyage round the globe (1768–1771)
Authors: CUPELLO MARIO , RIBEIRO-COSTA CIBELE S. , BARCLAY MAXWELL V.L.
Published: July 2023

Chalcocopris hesperus (originally Scarabaeus hesperus) (Coleoptera, Scarabaeidae, Scarabaeinae, Dichotomiini) is a common and unmistakable dung beetle species from the Atlantic Forest of Brazil, Paraguay and Argentina. In 2015, a neotype was designated for its valid name based on the belief that the original type series had been lost. However, the designation was invalid for failing to comply with Article 75.3.1 of the Code. Moreover, even if it had been originally valid, the neotype would now lose its type status for we found a syntype precisely where it was originally said to be, in the material of the Joseph Banks collection, now preserved in the Natural History Museum, London. We present the evidence supporting the recognition of this specimen as a syntype and designate it as the lectotype. The specimen, from Rio de Janeiro, Brazil, may have been collected by Sir Joseph Banks himself during the first Cook voyage round the world (1768–1771), but we discuss aspects that might indicate that it may actually have been caught later and by someone else. Finally, we show that Scarabaeus hesperus was fixed as the type species of Chalcocopris not in 2016, as recently argued by one of us, but in 1998 by Olivier Montreuil. The data gathered and discussed here will hopefully prove to be valuable to anyone investigating the history and type status of specimens from the Banksian entomological collection.
Mayflies (Insecta: Ephemeroptera) of the Indian Himalaya and future challenges
Authors: VASANTH M. , SUBRAMANIAN K. A. , SELVAKUMAR C. , KUBENDRAN T.
Published: July 2023

A concise overview of the diversity and distribution of Ephemeroptera of Indian Himalaya, a global biodiversity hotspot, is presented. The current study summarizes the diversity and distribution of Ephemeroptera of the seven Himalayan states of India: Jammu and Kashmir, Ladakh, Himachal Pradesh, Uttarakhand, Utter Pradesh, Sikkim, and Arunachal Pradesh. This study documents 10 families, 34 genera, 89 species. A total of 57 species are considered endemic to the region. The richest diversity is reported from the states of Himachal Pradesh and Arunachal Pradesh and the maximum diversity is seen within three families and three genera. Updated information on Ephemeroptera of the Himalaya parts of Pakistan, Nepal, Bhutan and China is required to comprehensively understand the diversity and endemicity of the hotspot. Further, to advance our knowledge of Ephemeroptera of the region, a synthesis of the taxonomy of adults and larvae with description of all life history stages within a molecular phylogenetic framework is urgently required.
Using distribution models to identify range shifts of four Acroneuria Pictet, 1841 (Plecoptera: Perlidae) species in the Midwest USA
Authors: HOGAN PHILLIP N. , DEWALT R. EDWARD
Published: July 2023

Regional faunal assessments of stoneflies in the United States Midwest (herein defined as Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio, and Wisconsin) indicate increasing imperilment resulting from human disturbance and climate change. Large-bodied perlid stoneflies with multivoltine life cycles are among the most at risk for regional extirpation, with losses reported in several midwestern states. Species distribution modeling was undertaken to describe distribution shifts for four widespread riverine species: Acroneuria abnormis (Newman, 1838), A. frisoni Stark & Brown, 1991, A. internata (Walker, 1852) and A. lycorias (Newman, 1839). The distribution modeling algorithm MaxEnt was selected to predict both the historical (i.e., pre-1960) and contemporaneous distributions for each species using separate occurrence datasets. These models permit the identification of suitable habitat loss through range contractions associated with human disturbance. Predictions of suitable habitat losses were recorded for multiple species but were greatest for A. abnormis and A. internata. These models serve to guide future collection efforts and to further describe patterns of regional biodiversity loss.
Baetis venkataramani sp. nov., a new species of the genus Baetis Leach, 1815 (Ephemeroptera: Baetidae) from Tamil Nadu, India
Authors: SIVARUBAN T. , SRINIVASAN PANDIARAJAN , BARATHY S. , ISACK RAJASEKARAN
Published: July 2023

As part of the ongoing research in the southern Western Ghats, a new species of Baetis Leach is described based on the nymphs collected in the Tamil Nadu, Southern India. Baetis venkataramani sp. nov. does not belong to any of the Baetis species groups proposed so far. The most closely related species to B. venkataramani sp. nov. is B. collinus Müller-Liebenau & Hubbard. However, the new species is distinguished from B. collinus by the presence of tergalius I, posterio-tergal spines absent on the tergites I–VIII and present on the tergum IX and by the size of the labial palp segment III. The main differences between B. venkataramani sp. nov. and other oriental species of Baetis are also discussed.
A review of Asian mayfly species of Habrophlebiodes (Ephemeroptera: Leptophlebiidae)
Authors: ZHENG XUHONGYI , GONG DE-WUN , ZHOU CHANG-FA
Published: July 2023

The mayfly genus Habrophlebiodes Ulmer, 1920 has four Asian species (three from China). To show the real characters of Asian species and to ensure the differences between the two clades of the genus, we studied all three endemic Chinese species (H. gilliesi Peters, 1963, H. tenella Kang & Yang, 1994, H. zijinensis You & Gui, 1995) with examining specimens from more than 30 sites. Adults of Chinese Habrophlebiodes can be separated by the male genitalia, female ovipositor and spots on adult wings, nymphs can be recognized by the abdominal color and prostheca shape of mandibles. Comparatively, the fourth species H. prominens Ulmer, 1939 is more different by its longer apical segment of maxillary palpi and colored wings. A key to the four Asian species is given. The status of the latter species, the exact difference between Asian and American clades, and their distribution pattern need more fresh materials to confirm.
Stonefly drumming behavior descriptions of three Soliperla Ricker, 1952 species (Plecoptera: Peltoperlidae)
Authors: SANDBERG JOHN B.
Published: July 2023

One new drumming call signal description and two updated call descriptions of three Soliperla Ricker, 1952 species in the stonefly family Peltoperlidae are presented. It was determined after recording and analysis that these signals differed from the previous descriptions, warranting a revision. Percussive signal interval patterns for the three species are described for the first time. The previous monophasic call signal pattern descriptions of Soliperla quadrispinula (Jewett, 1954) and S. thyra (Needham & Smith, 1916) are updated to the repeated monophasic pattern. The male call and response of S. sierra Stark, 1983 and the response signal of S. quadrispinula are described for the first time. The call descriptions of the three California species consist of one to six monophasic signals with inconsistent inter-call intervals. Sequenced monophasic female answers did not follow all repeated male calls. Male monophasic responses occasionally followed female answers in sequenced 3-way exchanges.
Mayfly Larvae (Ephemeroptera) in Thailand: Diversity and Science Communication
Authors: BOONSOONG BOONSATIEN
Published: July 2023

Mayflies (Ephemeroptera) are one of the most common components of aquatic assemblages in freshwater environments and contribute to ecosystem services. Mayflies have been widely used as indicators of water quality and frequently play an important role in biomonitoring protocols. Mayflies exhibit a decrease in taxa richness with increases in stream or river pollution and degradation. A series of taxonomic studies on Thai mayflies reported 19 families, 73 genera, and approximately 176 species. The families Baetidae and Heptageniidae are the most diverse and widespread groups of Thai mayflies. Our understanding of the diversity of Thai mayflies has steadily increased in recent years. New genera are being described: Cymbalcloeon Suttinun, Gattolliat & Boonsoong, 2020; Elatosara Malzacher, 2020; Megabranchiella Phlai-ngam & Tungpairojwong, 2022; Mekongellina Malzacher, 2019; Sangpradubina Boonsoong & Sartori, 2016; and Thainis Malzacher, 2020. New species and new country records are constantly being discovered in Thailand. Nevertheless, the number of genera and species seems to be lower than the species diversity estimation, and several genera and species remain undescribed. Research on the systematics and ecology of mayflies has been increasing in recent years. DNA barcoding can provide a powerful supplement to the traditional morphological approach to species delimitation. The book entitled “Mayfly Larvae in Thailand” (Thai version) and the boardgame “Thai Mayflies” were developed for environmental science communication. Further efforts are required to assess the conservation status of mayfly species. Thus, data on mayfly diversity and ecological requirements could be used as tools to evaluate environmental impacts on water resources and to drive future research on biodiversity conservation management strategies.
Ecology and phenology of stoneflies (Plecoptera) in the northern slopes of the Central Caucasus in winter and spring seasons
Authors: CHERCHESOVA SUSANNA K. , SHAPOVALOV MAKSIM I. , MAMAEV VITALY I. , PALATOV DMITRY M.
Published: July 2023

The species of winter- and spring emerging plecopterans inhabiting the northern slopes of the Central Caucasus (territory of the Republic of North Ossetia-Alania, Russia) were examined. A total of 33 species were found at 59 sites. These findings expand the knowledge of ecology and phenology of six species for which we knew little. New localities were detected for a few rare stoneflies: Filchneria balcarica Balinsky, 1950, Plesioperla sakartvella (Zhiltzova, 1956), P. brachystyla Zhiltzova, 1988 and others.
Nanohorn-mediated high-performance impedance spectroscopy sensor for diagnosing osteoporosis
Authors: Yin Jun , Gopinath Subash C. B. , Han Leixiang
Published: August 2023

Osteoporosis (OS) is a silent bone disease involved in the dysregulation of bone formation, metabolism, desorption causes a porous bone. There is a necessity for regular monitoring of the bone condition of aged people with bone-related issues. Osteocalcin (OC) is a well-known biomarker
for OS, correlated with bone desorption and formation. A carbon-based nanohorn-modified impedance sensor was introduced here to quantify the OC level by a sandwich assay. Nanohorn was conjugated with an anti-osteocalcin capture antibody and attached to the impedance electrode through a carbonyldiimidazole
linker and then, osteocalcin was sandwiched with detection anti-osteocalcin antibody. This sandwich immunoassay on the nanohorn attached impedance electrode identifies the osteocalcin from 7 pg/mL of osteocalcin. Further, serum-spiked experiments revealed the selective identification of OC
and the control performances with negative targets and other antibodies did not show any significant response, indicating the selective identification of OS. This research assists to quantify the OC level and diagnose its condition.
Predictors of Facial Fractures in Trauma Patients: Retrospective Review at a Level I Trauma Center
Authors: Sanders Thomas , Rhodes Heather , Boscia Joseph , Biswas Saptarshi
Published: August 2023

Background

The incidence and causes of facial fractures differ between patients, but patterns arise within populations. These patterns vary by gender, age, and between countries. This study aims to determine variables to identify patients at risk for facial fractures in a United States trauma population.

Methods

This is a single-center study of Trauma Registry data, inclusive of years July 1, 2016, to January 31, 2022. Inclusion criteria were based upon all trauma patients. Confirmation of a non-isolated facial fracture (dependent variable) was verified using ICD10 diagnosis codes. A logistic regression was performed in SPSS to ascertain the effects of predictor variables on the likelihood that a trauma patient will experience a facial fracture.

Results

20377 patients were included in the analysis based upon the requirements specified in the methods section; 1575 (7%) had a positive facial fracture. The logistic regression model was statistically significant (N = 18507, P < .01). Significant risk factors for facial fracture identified included helicopter transport (OR = 1.35, P < .01) and increasing injury severity scores (OR = 1.07, P < .01). Modes of injury most likely to predict facial fracture included assault (OR = 6.62, P < .01), moped (OR = 2.02, P < .01), and motorcycle trauma (OR = 1.55, P < .01). The discharge disposition most likely among facial fracture patients included short-term general hospital (OR = 1.71, P < .01) and intermediate care facility (OR = 4.47, P < .01).

Conclusions

Patients with traumatic injuries from assault, moped, and motorcycle accidents were more likely to present with facial fractures. These patients had more severe injuries, seen as increased ISS scores, higher likelihood of transport by helicopter, and the need for additional care after discharge.