bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network
Authors: Khalid Saifullah Muhammad , Monirul Kabir Md. , Rafiqul Islam Kazi
Published: June 2023

This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system.
Ranking load in microgrid based on fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution algorithm for load shedding problem
Authors: Tran Tung Giang , Nguyen Thai An , Le Trong Nghia , Nguyen Ngoc Au , Huynh Thi Ngoc Thuong
Published: June 2023

This paper proposes a method to rank the loads in the microgrid by means of a weight that combines the criteria together in terms of both technical and economic aspects. The fuzzy analytic hierarchy process technique for order of preference by similarity to ideal solution (fuzzy AHP TOPSIS) algorithm is used to calculate this combined weight. The criteria to be considered are load importance factor (LIF), voltage electrical distance (VED) and voltage sensitivity index (VSI). The fuzzy algorithm helps to fuzzy the judgment matrix of the analytic hierarchy process (AHP) method, making it easier to compare objects with each other and remove the uncertainty of the AHP method. The technique for order of preference by similarity to ideal solution (TOPSIS) algorithm is used to normalize the decision matrix, determine the positive and negative ideal solutions to calculate the index of proximity to the ideal solution, and finally rank all the alternatives. The combination of fuzzy AHP and TOPSIS algorithms is the optimal combination for decision making and ranking problems in a multi-criteria environment. The 19-bus microgrid system is applied to calculate and demonstrate the effectiveness of the proposed method.
High rejection self-oscillating up-conversion mixer for fifth-generation communications
Authors: Es-saqy Abdelhafid , Abata Maryam , Fattah Mohammed , Mazer Said , Mehdi Mahmoud , El Bekkali Moulhime , Algani Catherine
Published: June 2023

This paper presents the design of a pseudomorphic high electron mobility transistor (pHEMT) self-oscillating mixer (SOM) for millimeter wave wireless communication systems. The 180° out-of-phase technique is chosen to both improve the desired lower sideband (LSB) signal and to achieve a satisfactory rejection of the unwanted signals (LO, USB and IF). This SOM is designed on the PH15 process of UMS foundry which is based on 0.15 µm GaAs pHEMT. The signal is up-converted from 2 GHz-IF frequency to 26 GHz-LSB frequency, using an autogenerated 28 GHz-LO signal. Simulations were performed using the advanced design system (ADS) workflow. They show 6.4 dB conversion gain and a signal rejection rate of 29.7 dB for the unwanted USB signal. the chip size is 3.6 mm2.
Support vector machine-based object classification for robot arm system
Authors: Cong Vo Duy , Hiep Thai Thanh
Published: June 2023

In this paper, a support vector machine (SVM) model is trained to classify objects in the automatic sorting system using a robot arm. The robot arm is used to grab objects and move them to the right position according to their shape predicted by the SVM model. The position of objects in the image is identified by using the contouring technique. The centroid of objects is calculated from the image moment of the object's contour. The calibration is conducted to get the parameters of the camera and combine with the pinhole camera model to compute the 3D position of the objects. The feature vector for SVM training is the zone feature and the SVM kernel is the Gaussian kernel. In the experiment, the SVM model is used to classify four objects with different shapes. The results show that the accuracy of the SVM classifier is 99.72%, 99.4%, 99.4% and 99.88% for four objects, respectively.
Triple-layer remote phosphor geometry: an excellent selection to improve the optical properties of white light-emitted diodes
Authors: Bui Thuc Minh , Nguyen Thi My Hanh , Quoc Anh Nguyen Doan , Thai Nguyen Le
Published: June 2023

High performance white-light-emitting diodes (WLEDs) have been the goal of recent research on phosphor-in-glass (PiG) devices. In this paper, we introduce a configuration of WLED that achieves high color rendering index (CRI), and correlated color temperature with the addition of Zn2SiO4:Mn2+, As5+ and YAl3O4B12:Eu3+. The technique is lower the temperature during the creation process of phosphor in glass and control the consistent thickness in between 0.5 to 0.7 mm to yield high color quality PiG, high CRI above 80 WLEDs, and extend the color temperature range to 3,900 to 5,300 K. The consistent heat generation combined with extraordinary CRI for PiG prove that low temperature sintering has the potential to create WLEDs with advanced quality. The improved WLEDs can be utilized in many high-demand lighting fields such as chromatic examination, medical analysis, and aesthetic lighting.
Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging
Authors: Chikkalingaiah Shankara , Rao Hari Prasad Subbarao Anantha Padmanabha , Uggregowda Latha Dabbegatta
Published: June 2023

Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.
Web server load prediction and anomaly detection from hypertext transfer protocol logs
Authors: Benova Lenka , Hudec Ladislav
Published: June 2023

As network traffic increases and new intrusions occur, anomaly detection solutions based on machine learning are necessary to detect previously unknown intrusion patterns. Most of the developed models require a labelled dataset, which can be challenging owing to a shortage of publicly available datasets. These datasets are often too small to effectively train machine learning models, which further motivates the use of real unlabeled traffic. By using real traffic, it is possible to more accurately simulate the types of anomalies that might occur in a real-world network and improve the performance of the detection model. We present a method able to predict and categorize anomalies without the aid of a labelled dataset, demonstrating the model’s usability while also gathering a dataset from real noisy network traffic. The proposed long short-term memory (LTSM) based intrusion detection system was tested in a real-world setting of an antivirus company and was successful in detecting various intrusions using 5-minute windowing over both the predicted and real update curves thereby demonstrating its usefulness. Our contribution was the development of a robust model generally applicable to any hypertext transfer protocol (HTTP) traffic with almost real-time anomaly detection, while also outperforming earlier studies in terms of prediction accuracy.
Random forest model for forecasting vegetable prices: a case study in Nakhon Si Thammarat Province, Thailand
Authors: Kaewchada Sopee , Ruang-On Somporn , Kuhapong Uthai , Songsri-in Kritaphat
Published: June 2023

The objectives of this research were developing a model for forecasting vegetable prices in Nakhon Si Thammarat Province using random forest and comparing the forecast results of different crops. The information used in this paper were monthly climate data and average monthly vegetable prices collected between 2011 – 2020 from Nakhon Si Thammarat meteorological station and Nakhon Si Thammarat Provincial Commercial Office, respectively. We evaluated model performance based on mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE). The experimental results showed that the random forest model was able to predict the prices of vegetables, including pumpkin, eggplant, and lentils with high accuracy with MAPE values of 0.09, 0.07, and 0.15, with RMSE values of 1.82, 1.46, and 2.33, and with MAE values of 3.32, 2.15, and 5.42, respectively. The forecast model derived from this research can be beneficial for vegetable planting planning in the Pak Phanang River Basin of Nakhon Si Thammarat Province, Thailand.
Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective
Authors: Ratra Ritu , Gulia Preeti , Gill Nasib Singh
Published: June 2023

Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.
Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime
Authors: Rawat Romil , Ayodele Oki Olukayode , Sankaran Sakthidasan , Florez Hector , Ajagbe Sunday Adeola
Published: June 2023

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.
Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm
Authors: Novianty Inna , Sholihah Walidatush , Mindara Gema Parasti , Nurulhaq Muhammad Iqbal , Faricha Anifatul , Sinambela Rismen , Purwandoko Pradeka Brilyan , Nanda Muhammad Achirul
Published: June 2023

Indonesia is the world’s largest producer of palm oil. To preserve its competitive advantages, the Indonesian oil palm sector must expand high-quality palm oil output. In oil palm quality control, the water content is a crucial parameter as it can be used as a reference to determine the right harvest time. Thus, this study proposed a near-infrared (NIR) spectroscopy as a fast and non-destructive analysis to assess oil palm water content. NIR spectra were processed using Shannon entropy to describe the characteristics at each wavelength. In this study, oil palm fruit samples at various maturity levels were collected with eight different maturity fractions. Based on the analysis, the Shannon entropy value is closely related to any changes in the water content of palm oil. The entropy value has a decreasing trend as the water content increases. The proposed technique can predict the water content of an oil palm with satisfactory performance with values of 0.9746 of coefficient of determination (R2) and 2,487 of root mean square error (RMSE). Application of this model will lead to a fast and accurate prediction system related to oil palm water content.
A stochastic approach for evaluating production planning efficiency under uncertainty
Authors: Wahyudi Mochamad , Sihotang Hengki Tamando , Efendi Syahril , Zarlis Muhammad , Mawengkang Herman , Vinsensia Desi
Published: June 2023

Planning production is an essential component of the decision-making process, which has a direct bearing on the effectiveness of production systems. This study’s objective is to investigate the efficiency performance of decision-making units (DMU) in relation to production planning issues. However, the production system in a manufacturing environment is frequently subject to uncertain situations, such as demand and labor, and this can have an effect not only on production but also on profit. The robust stochastic data envelopment analysis model was proposed in this study with maximizing the number of outputs as the objective function thus means of handling uncertainty in input and output in production planning problems. This model, which is based on stochastic data envelopment analysis and a method of robust optimization, was proposed with the intention of providing an efficient plan of production for each DMU of stage production. The model is applied to small and medium-sized businesses (SMEs), with inputs consisting of the cost of labor, the number of customers, and the quantity of raw materials, and the output consisting of profit and revenue. It has been demonstrated through implementation that the proposed model is both efficient and effective.
A novel cost-based replica server placement for optimal service quality in cloud-based content delivery network
Authors: Dharmapal Priyanka , Channakrishnaraju Channakrishnaraju , Krishnamur Chethan Bommalingaiahanapalya
Published: June 2023

Replica server placement is one of the crucial concerns for a given geographic diversity associated with placement problems in content delivery network (CDN). After reviewing the existing literatures, it is noted that studies are more for solving placement problem in conventional CDN and not much over cloud-based CDN architectures, which some few studies are reported towards replica selection are much in its nascent stages of development. Moreover, such models are not benchmarked or practically assessed to prove its effectiveness. Hence, the proposed study introduces a novel design of computational framework associated with cloud-based CDN which can facilitate cost-effective replica server management for enhanced service delivery. Implemented using analytical research methodology, the simulated study outcome shows that proposed scheme offers reduced cost, reduced resource dependencies, reduced latency, and faster processing time in contrast to existing models of replica server placement.
Analysis of the learning object-oriented programming factors
Authors: Batiha Qais Ali , Abd Majid Nazatul Aini , Sahari Noraidah , Ali Noorazean Mohd
Published: June 2023

Students often feel overwhelmed by object-oriented programming courses. They find it difficult and complex to learn, requiring a high cognitive load to use the concepts in coding. These issues lead to demotivation in learning programming. This research aims to identify and verify factors that contribute to learning object-oriented programming from two perspectives: interviews and surveys. A literature review was conducted to identify these factors, followed by interviews with five experts who have been teaching object-oriented programming for over ten years to confirm them. Based on the interview results, a questionnaire was developed and administered to 31 bachelor students and 19 lecturers with master’s or doctorate degrees in computer science. The responses indicated that the identified factors were acceptable, with scores ranging from 3.74 to 4.65. The outcomes of this study are a set of factors that should be considered in a programming environment to improve the teaching and learning of object-oriented programming and make it more accessible and engaging for students.
Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model
Authors: Nissa Nuzulul Khairu , Yulianti Evi
Published: June 2023

Customer review is a critical resource to support the decision-making process in various industries. To understand how customers perceived each aspect of the product, we can first identify all aspects discussed in the customer reviews by performing multi-label text classification. In this work, we want to know the effectiveness of our two proposed strategies using bidirectional encoder representations from transformers (BERT) language model that was
 pre-trained on the Indonesian language, referred to as IndoBERT, to perform multi-label text classification. First, IndoBERT is used as feature representation to be combined with convolutional neural network-extreme gradient boosting (CNN-XGBoost). Second, IndoBERT is used both as the feature representation as well as the classifier to directly solve the classification task. Additional analysis is performed to compare our results with those using multilingual BERT model. According to our experimental results, our first model using IndoBERT as feature representation shows significant performance over some baselines. Our second model using IndoBERT as both feature representation and classifier can significantly enhance the effectiveness of our first model. In summary, our proposed models can improve the effectiveness of the baseline using Word2Vec-CNN-XGBoost by 19.19% and 6.17%, in terms of accuracy and F-1 score, respectively.
Emotion detection on social media status in Myanmar language
Authors: Swe Thiri Marlar , Wah Naw Lay
Published: June 2023

Many social media emerged and provided services during these years. Most people, especially in Myanmar, use them to express their emotions or moods, learn subjects, sell products, read up-to-date news, and communicate with each other. Emotion detection on social users makes critical tasks in the opinion mining and sentiment analysis. This paper presents the emotion detection system on social media (Facebook) user status or post written in Myanmar (Burmese) language. Before the emotion detection process, the user posts are pre-processed under segmentation, stemming, part-of-speech (POS) tagging, and stop word removal. The system then uses our preconstructed Myanmar word-emotion Lexicon, M-Lexicon, to extract the emotion words from the segmented POS post. The system provides six types of emotion such as surprise, disgust, fear, anger, sadness, and happiness. The system applies naïve Bayes (NB) emotion classifier to examine the emotion in the case of more than two words with different emotion values are extracted. The classifiers also classify the emotion of the users on their posts. The experiment shows that the system can detect 85% accuracy in NB based emotion detection while 86% in recurrent neural network (RNN).
Predicting active compounds for lung cancer based on quantitative structure-activity relationships
Authors: Hanafi Hamza , Rossi Hassani Badr Dine , Aït Kbir M’hamed
Published: June 2023

Recently, advancements in computational and artificial intelligence (AI) methods have contributed in improving research results in the field of drug discovery. In fact, machine learning techniques have proven to be especially effective in this regard, aiding in the development of new drug variants and enabling more precise targeting of specific disease mechanisms. In this paper, we propose to use a quantitative structure-activity relationship-based approach for predicting active compounds related to non-small cell lung cancer. Our approach uses a neural network classifier that learns from sequential structures and chemical properties of molecules, as well as a gradient boosting tree classifier to conduct comparative analysis. To evaluate the contribution of each feature, we employ Shapley additive explanations (SHAP) summary plots to perform features selection. Our approach involves a dataset of active and non-active molecules collected from ChEMBL database. Our results show the effectiveness of the proposed approach when it comes to predicting accurately active compounds for lung cancer. Furthermore, our comparative analysis reveals important chemical structures that contribute to the effectiveness of the compounds. Thus, the proposed approach can greatly enhance the drug discovery pipeline and may lead to the development of new and effective treatments for lung cancer.
Analysis and prediction of seed quality using machine learning
Authors: Srinivasaiah Raghavendra , Meenakshi Meenakshi , Chennegowda Ravikumar Hodikehosalli , Jankatti Santosh Kumar
Published: June 2023

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Convolutional auto-encoded extreme learning machine for incremental learning of heterogeneous images
Authors: Madhusudhanan Sathya , Jaganathan Suresh , Prasad Dattuluri Venkatavara
Published: June 2023

In real-world scenarios, a system's continual updating of learning knowledge becomes more critical as the data grows faster, producing vast volumes of data. Moreover, the learning process becomes complex when the data features become varied due to the addition or deletion of classes. In such cases, the generated model should learn effectively. Incremental learning refers to the learning of data which constantly arrives over time. This learning requires continuous model adaptation but with limited memory resources without sacrificing model accuracy. In this paper, we proposed a straightforward knowledge transfer algorithm (convolutional auto-encoded extreme learning machine (CAE-ELM)) implemented through the incremental learning methodology for the task of supervised classification using an extreme learning machine (ELM). Incremental learning is achieved by creating an individual train model for each set of homogeneous data and incorporating the knowledge transfer among the models without sacrificing accuracy with minimal memory resources. In CAE-ELM, convolutional neural network (CNN) extracts the features, stacked autoencoder (SAE) reduces the size, and ELM learns and classifies the images. Our proposed algorithm is implemented and experimented on various standard datasets: MNIST, ORL, JAFFE, FERET and Caltech. The results show a positive sign of the correctness of the proposed algorithm.
Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region
Authors: Tkatek Said , Amassmir Samar , Belmzoukia Amine , Abouchabaka Jaafar
Published: June 2023

Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both
 high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Analysis of Marketing Strategy in Increasing Sales of Ibu Uun Spicy Noodles in West Jakarta
Authors: Talebong Novalia , Nasution Dito Aditia Darma
Published: June 2023

The purpose of this study is to find out the strategy in marketing Ibu Uun's Spicy Noodles in boosting sales. This qualitative and descriptive research approach is used. Essential and optional information is the type and source of information used, as well as the strategy for selecting information as a meeting and perception. Data were analyzed using descriptive analysis which refers to the 4P Marketing Mix (Product, Price, Place, Promotion). Based on the findings of the investigation, Ibu Uun's Spicy Noodles implemented a product strategy to boost sales. There are various levels of choice (level 1 to 3). Pricing strategy that allows for reasonable prices. Placement strategy, choosing a location that is comfortable and easy to reach. Initially, the promotion strategy was carried out through face-to-face sales, and then implemented through social media. Customers who are happy with the products and services provided also spread the word of mouth. It turned out that the implementation of this strategy had a significant impact on Ibu Uun's sales of Spicy Noodles, which have always increased since the store opened.
Reflexões sobre geoética e a instalação de megaempreendimentos: o caso do Terminal Ponta Negra e os beachrocks de Darwin, Jaconé (RJ)
Authors: Silveira Sílvia Maria Teixeira , Mansur Kátia Leite , Ramos Renato Rodriguez Cabral
Published: June 2023

A geoética busca discutir aspectos éticos envolvidos no fazer geocientífico. Neste trabalho, pretendemos refletir sobre a geoética no contexto dos megaempreendimentos, analisando o caso dos beachrocks de Darwin. Essas rochas sedimentares têm grande importância científica, didática, histórica e cultural; de fato, seu valor patrimonial foi reconhecido por sentença judicial em 2022. Os beachrocks estão ameaçados pela potencial instalação do Terminal Ponta Negra, na praia de Jaconé, municípios de Maricá e Saquarema (RJ). Foco de poderosos interesses econômicos e políticos, o megaempreendimento portuário já obteve sua licença de instalação junto ao Instituto Estadual do Ambiente, órgão ambiental responsável. As obras ainda não foram iniciadas. O Ministério Público do Estado do Rio de Janeiro apontou diversos vícios e falhas no processo de licenciamento ambiental, desconsiderados pelo órgão ambiental, pelo empreendedor e pelos seus consultores. Caso seja implantado, o empreendimento acarretará perda de patrimônio geológico e poderá gerar outros impactos socioambientais. Dessa forma, fica clara a importância de se levar em consideração todos os fatores contidos no processo de licenciamento ambiental de megaempreendimentos, de forma que garanta o desenvolvimento sustentável, a conservação de áreas tão relevantes do ponto de vista ambiental e o bem-estar das pessoas que nelas habitam. Nesse contexto, a geoética pode ser uma ferramenta para se propor novos modelos de desenvolvimento e para se discutir a atuação de empresas, órgãos públicos e geocientistas, com o objetivo de se garantir a proteção do patrimônio geológico e não apenas os interesses econômicos e políticos envolvidos na instalação de empreendimentos desse porte.
Proposta de geoconservação no distrito de Fazenda Nova, Brejo da Madre de Deus, Pernambuco, NE do Brasil
Authors: Arruda Ítalo Rodrigo Paulino de , Lima Gerlando Rodrigues de , Listo Danielle Gomes da Silva , Mariano Gorki , Guimarães Thaís de Oliveira
Published: June 2023

Os monumentos geológicos, geomorfológicos, arqueológicos e as paleopaisagens oriundas de processos mecânicos, químicos e biológicos representam um verdadeiro geopatrimônio com relevância científica e precisam ser reconhecidos, mensurados, catalogados e conservados. Essa variedade abiótica e natural da Terra é compreendida como geodiversidade, que apresenta valores superlativos de cunho informacional, científico, educacional, turístico e/ou econômico. Nesse sentido, os geossítios possuem informações que contam uma história do passado e que, com técnicas específicas, permitem compreender a evolução de um geoambiente. Com base em discussões que versam sobre a temática da geodiversidade, especificamente o ambiente semiárido, foi elaborado este estudo com o objetivo de valorizar os tanques quanto aos aspectos científicos resultantes do intemperismo físico, químico e biológico, bem como incentivar a geoconservação e o geoturismo desse geopatrimônio no distrito de Fazenda Nova, Brejo da Madre de Deus, no estado de Pernambuco.
This Past Must Address Its Future: Uses of African Noncontemporaneity in Contemporary Art from the French Borderscape
Authors: Bajorek Jennifer
Published: June 2023

In the last two decades, key sites in the European borderscape—the “jungle” of Calais, the dense patchwork of settlements around Melilla and Ceuta, myriad migrant or refugee camps along Europe's Mediterranean coastline and in the major train stations of its capital cities—have become art factories. In these spaces, artists from a range of backgrounds are making new work, much of which seeks to challenge the exoticist and primitivizing tropes that, in Europe, have characterized the representation of im/migrant presences at least since the official colonial period. Among the most conspicuous and intractable of these tropes have been those connected with the representation of Africa and Africans in the European borderscape. This essay explores a handful of particular strategies used by artists working in and around the French borderscape and speculates about their consequences for thinking about both contemporary art and contemporary politics.
Art, Signs, and Cultures: Iba Ndiaye and Jean Laude in Conversation with Roger Pillaudin
Authors: Ndiaye Iba , Laude Jean
Published: June 2023

This document, translated from the original French, is an edited transcript of a conversation between the Senegalese painter Iba Ndiaye, the French art historian Jean Laude, and a moderator, Roger Pillaudin. It took place on the occasion of the Festival des arts et cultures africaines in Royan, France (March 1977), and was later broadcast on the radio channel France Culture. What stands out in the conversation is the way Laude seeks to negate Ndiaye's cross-cultural experience and background, and arguably his very legitimacy as a contemporary artist. Laude's insistence on adhering to neat categories (linguistic, national, artistic) in engaging with Ndiaye, coupled with Ndiaye's steady defiance of these categories, point to broader tensions between structuralism and area studies, on the one hand, and poststructuralism and postcolonial studies, on the other. The conversation also invites careful scrutiny of the problem of Eurocentrism, in the sense that Laude's structuralist position is in fact anti-Eurocentric: colonialism is condemned for destroying local traditions, which are in turn revered at the expense of allegedly derivative expressions emerging from the same societies. Ndiaye nevertheless holds his own against Laude's aggressive paternalism, and manages to call into question a number of the art historian's assumptions around the perceived axes and obligations of artistic identity.
Art History, Postcolonialism, and the Global Turn
Authors: Cohen Joshua I. , Torshizi Foad , Zamindar Vazira
Published: June 2023

When taken as a conglomerate, the postcolonial, the global, and the decolonial might signal a coordinated “decolonizing” action—one of breaking with the Eurocentric, patriarchal, and nationalist foundations of art history. Yet from a disaggregating perspective, these three terms and their respective domains cannot be seen as synonymous or entirely harmonious. What particularly demands scrutiny is the tendency to dismiss the postcolonial, or announce its demise, by claiming it has been superseded by other paradigms, namely the global and the decolonial. This introductory essay, and its accompanying special issue of ARTMargins, seeks to trace the postcolonial, global, and decolonial as they have intersected with scholarship in art history over the past five decades, and to challenge postcolonialism's presumed obsolescence in the wake of the global turn. Postcolonial thought, we argue, has given rise to a generative series of critical interventions in art history at least since the 1970s and 1980s, and has proven to be nuanced and self-reflexive. Postcolonial lines of inquiry not only continue to offer ways of critically exploring colonial-era and subsequent artistic practices, but also allow for interrogations of formations of art and the discipline of art history as colonial forms of knowledge. As such, postcolonialism still vitalizes debates within the discipline regarding the constitution of its own objects, lineaments, and methods.
Questions Are All You Need to Train a Dense Passage Retriever
Authors: Sachan Devendra Singh , Lewis Mike , Yogatama Dani , Zettlemoyer Luke , Pineau Joelle , Zaheer Manzil
Published: June 2023

We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples. ART, in contrast, only requires access to unpaired inputs and outputs (e.g., questions and potential answer passages). It uses a new passage-retrieval autoencoding scheme, where (1) an input question is used to retrieve a set of evidence passages, and (2) the passages are then used to compute the probability of reconstructing the original question. Training for retrieval based on question reconstruction enables effective unsupervised learning of both passage and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning. Extensive experiments demonstrate that ART obtains state-of-the-art results on multiple QA retrieval benchmarks with only generic initialization from a pre-trained language model, removing the need for labeled data and task-specific losses.1

Our code and model checkpoints are available at: https://github.com/DevSinghSachan/art.
Visual Spatial Reasoning
Authors: Liu Fangyu , Emerson Guy , Collier Nigel
Published: June 2023

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: The human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs’ by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.1
History of Sub-Saharan Africa’s Long Journey towards Economic Emancipation
Authors: Simuziya Nsama Jonathan
Published: June 2023

The history of the African Growth and Opportunity Act (AGOA) i.e., the trade partnership between the US and sub-Saharan African states indicates that the initial Clinton Administration blueprint for this venture was to operate for eight years, from 2000 to 2008. However, in 2004 George Bush, the incoming US president, pushed the AGOA mandate to 2015. Before the AGOA order could expire, its mandate was again extended to 2025 by then US president Barack Obama. This study argues that the lofty ambitions inscribed in the design of AGOA seem not to have lived up to their billing. The study finds that the theoretical win-win proposition that was envisioned does not appear to have yielded the desired economic results for sub-Saharan states mainly because the enterprise is likely a one-sided trade deal in favour of the US. Data for this study was gathered from qualitative and quantitative sources. The study concludes that given the pattern of the US engagements with sub-Saharan Africa, which seems only focused on advancing US national interests, the sub-Saharan region needs to devise alternative trade partnerships if its economy is to grow. The study recommends that sub-Saharan Africa focuses on intra-Africa trade. Alternatively, it should strengthen development cooperation with emerging markets such as the BRICS (Brazil, Russia, India, China, and South Africa).
The Overseer of Linen (jmj-r sšrw) in the Old Kingdom
Authors: Sánchez-Casado Raúl , Ortiz-García Jónatan
Published: June 2023

This paper focuses on the figure of the overseer of linen (jmj-r sšrw) during the chronological period of the Old Kingdom. By choosing a wide selection of sources, we have undertaken the reconstruction of the main features that defined this title, looking in-depth at the specific roles associated with it and the contexts in which they were exercised. The nature of the sources has meant that we have taken a mainly iconographic approach, which, combined with textual and archaeological analysis, has allowed us to get the most out of the evidence available. Thus, the jmj-r sšrw is revealed as an essential character in the Old Kingdom household, overseeing the supply, storage, and distribution of linen within the estate. Additionally, the supervision of a product that is key for the well-being of the deceased in the netherworld granted the jmj-r sšrw an important role in the funerary cult of their deceased master, who aimed to ensure his eternal supply of linen.
Another Look at the Gendered Constitution of the Laws of War
Authors: Mégret Frédéric
Published: June 2023

This article argues that the gendered constitution of the regulation of war runs even deeper than typically understood. Instead of merely the laws of war’s internal categories being gendered, it is the entire apparatus of war’s regulation as expressed in various versions of its denomination that manifests different shades of gender. Specifically, the article shows how the laws of war emerged as the hegemonic masculine synthesis between different conceptions of manhood, from the most unconstrained to the most chivalrous. As that compromise has been challenged by more ‘feminine’ approaches, this has triggered repeated reassertions of the regulation of war’s inherently masculine character. The article speculates about the staying power of the masculine script of war through law at the intersection of misogyny, racism and the politics of legal expertise. It concludes by reflecting on what it might mean to transcend the implicit heteronormativity of the regulation of war by queering its categories.
Studies on soil physico-chemical properties and biochemical composition of selected vegetables collected from locally irrigated farmland with municipal wastewater in Minna, Niger State, Nigeria
Authors: Gado Aishatu A. , Salihu Ibrahim A. , Audu Muhammad A.I. , Abubakar Alhaji L. , Daudu Yusuf O.A. , Salihu Muhammad A.
Published: June 2023

Vegetables have become an indispensable part of human nutrition. This study aimed at analyzing the biochemical compositions of three leafy vegetables (Corchuorus olitorius, Telfairia occidentalis and Spinacia oleracea) collected from local farm irrigated with municipal wastewater. The biochemical parameters were assayed following standard laboratory protocols. Results revealed significantly (P < 0.05) higher Ca (207.33 mg/kg) and Mg (182.53 mg/kg) contents in wastewater with Mg contents in both clean water (72.80 mg/kg) and wastewater (182.53 mg/kg) exceeding the FAO safe limits. The wastewater had significantly (P < 0.05) higher (Fe) and (Cd) contents (1.72 and 0.42 mg/kg), than clean water from the modern farm (0.67 and 0.13 mg/kg). Both water samples had heavy metals, exceeding the FAO safe limits. The Mn, Fe and Pb contents determined in the three vegetables collected from both farms were within FAO safe limits except Cd content which ranged from 0.05–0.87 mg/kg which is above FAO acceptable limits of 0.02 mg/kg. Results on mineral composition revealed significantly higher (P < 0.05) Na, K, Ca and Mg contents in the three vegetables collected from the local farm, Telfairia occidentalis having the highest Na (123.33 mg/kg), K (6500 mg/kg) and P (6.19 mg/kg) while, Corchuorus olitorius had the highest Ca (2.15 mg/kg) and Mg (2.12 mg/kg). The result of this study shows that municipal wastewater irrigation could be utilized in improving the mineral compositions of leafy vegetables. However, it is highly recommended that the wastewater is treated properly to avoid heavy metal contamination.
The Quasi-Linear Relation between Planetary Outgoing Longwave Radiation and Surface Temperature: A Climate Footprint of Radiative and Nonradiative Processes
Authors: Cai Ming , Sun Jie , Ding Feng , Kang Wanying , Hu Xiaoming
Published: June 2023

The slope of the quasi-linear relation between planetary outgoing longwave radiation (OLR) and surface temperature (TS) is an important parameter measuring the sensitivity of Earth’s climate system. The primary objective of this study is to seek a general explanation for the quasi-linear OLR–TS relation that remains valid regardless of the strength of the atmospheric window’s narrowing effect on planetary thermal emission at higher temperatures. The physical understanding of the quasi-linear OLR–TS relation and its slope is gained from observation analysis, climate simulations with radiative–convective equilibrium and general circulation models, and a series of online feedback suppression experiments. The observed quasi-linear OLR–TS relation manifests a climate footprint of radiative (such as the greenhouse effect) and nonradiative processes (poleward energy transport). The former acts to increase the meridional gradient of surface temperature and the latter decreases the meridional gradient of atmospheric temperatures, causing the flattening of the meridional profile of the OLR. Radiative processes alone can lead to a quasi-linear OLR–TS relation that is more steeply sloped. The atmospheric poleward energy transport alone can also lead to a quasi-linear OLR–TS relation by rerouting part of the OLR to be emitted from a warmer place to a colder place. The combined effects of radiative and nonradiative processes make the quasi-linear OLR–TS relation less sloped with a higher degree of linearity. In response to anthropogenic radiative forcing, the slope of the quasi-linear OLR–TS relation is further reduced via stronger water vapor feedback and enhanced poleward energy transport.

Significance Statement

The slope of the quasi-linear relation between planetary outgoing longwave radiation (OLR) and surface temperature (TS) is an important parameter measuring the sensitivity of Earth’s climate system. The observed quasi-linear OLR–TS relation manifests a climate footprint of radiative (greenhouse effect) and nonradiative processes (poleward energy transport). Radiative processes alone can lead to a quasi-linear OLR–TS relation that is more steeply sloped. The atmospheric poleward energy transport alone can also lead to a quasi-linear OLR–TS relation by rerouting part of the OLR to be emitted from a warmer place to a colder place. The combined effects of radiative and nonradiative processes make the quasi-linear OLR–TS relation less sloped with a higher degree of linearity.