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Bioarcheology is the study of archived human and animal remains. The term was first proposed by the British archeologist Grahame Clark to denote the investigations of animal bones from archeological sites. Since its introduction in 1972, this term has been applied to encompass additional scientific domains and has also been used for the multidisciplinary papers collected in this special issue. Agriculture's first appearance on the archeological horizon is a matter of debate. Evidence provided by multidisciplinary studies encompassing archaeobotany and zooarchaeology and the emergence of agricultural practices in the “fertile crescent” (a crescent shaped area containing fertile lands amidst semiarid regions of Western Asia, the Nile valley, and northeast Africa) in Iran add to the science of ancient agriculture (S. Riehl et al.). Egyptian archeology has been a widely accepted aspect of bioarcheology. Here a wealth of information has been collected with the applications of new techniques to ancient tissues such as X-ray analyses applied in the field to archived human bones (F. Ruhli et al.). Mandibles excavated from Qubbet el-Hawa cemetery in Aswan, Egypt, have yielded information on the food consumed by middle class people during several periods of ancient Egyptian history. The elemental analysis of the bones hinted at social and climatic changes affecting ancient populations and shows the effects of state socialism (a variant socialism in which the power of the state is employed to creating an egalitarian society through the control of production of all goods) and agricultural conditions during the 17th Dynasty in Upper Egypt (G. D. AL-Khafif and R. El-Banna). South American frozen mummies from mountain tops of the high Andes have only recently become available for bioarcheological studies. Such studies have revealed important aspects of human sacrificial practices in pre-Hispanic times. Because of the exceptional preservation of the soft tissues of these mummies due to the intense cold and ambient dryness at altitudes at which they were found, important human pathologies accompanying life under Inca rule have become accessible to modern techniques (M. C. Ceruti). The histology of soft tissues is a well-accepted diagnostic method in modern human pathology. However, its application to ancient tissues brings challenges to the standard interpretation of histological stains because of the variable preservation of specimens depending on numerous unforeseeable factors. The postmortem interval before histological study is one crucial obstacle. The longer this time the less dependable the results. Paleopathological examinations provided guidelines to the tissues likely to be best preserved and allowed the most reliable diagnoses (C. Grove et al.). Successful and cheap methods recently developed for the extraction of ancient DNA (aDNA) have led to important studies in evolution and worldwide human migrations. East Asian mitochondrial DNA (mtDNA) especially of Koreans has been used to determine single nucleotide polymorphisms in mtDNA in human bone samples from premodern Joseon tombs in Republic of Korea (C. S. Oh et al.). Archeological materials useful for the study of bioarcheology are conserved in museums throughout the world. Curators prefer the study of their collections by noninvasive methods. The desire to maintain specimens intact is shared by bioarcheologists too. Nevertheless, invasive methods such as autopsies, endoscopies, and biopsies are sometimes unavoidable for accurate diagnosis. In this setting controversies are unavoidable but scientific criteria coupled with ethical considerations are proposed which may resolve disputes (D. Moissidou et al.). Monastic inhabitants of ancient institutions provide fertile grounds for the investigations of diseases and social aspects of a particularly privileged population. Regional differences are not often revealed but when such studies are restricted to defined populations such as those in Bavaria, Germany, insights into paleopathology, nutrition, and lifestyles of a well-defined human group become apparent (A. G. Nerlich et al.). Statistical modeling has advanced science in general and bioarcheology in particular. Growth lines in archived biological materials such as teeth and hair proved valuable in providing evidence of metabolic health, physiology, and lifestyles of long dead individuals. Thus new noninvasive methods for the analyses of curated archeological specimens could avoid ethical consideration in bioarcheological investigations (C. Qualls and O. Appenzeller). Computational methods could also predict the physical appearance, mood, and clinical diagnosis such as hirsutism (excessive hair growth) also known in some individuals as Cantu syndrome (C. Qualls et al.). Recent massive sequencing of ancient DNA has transformed the study of human evolution and human history. Such studies are only now beginning in bioarcheology but important insights into modern diseases are likely to be gleaned from analyses of bioarcheological specimens.

This paper proposes a new plagiarism detection system devoted to Arabic text documents. This system is based on modeling the relation between documents and their n-gram phrases. Part-of-Speech tagging is applied on the examined documents to support in resolving the morphological ambiguity during text normalization. Text indexing and stop-words removal are performed, employing a new morphological analysis based method. Heuristic pairwise phrase matching algorithm is used to build the documents TF-IDF model, considering substitution of words with their synonyms. The hidden associations of the unique n-gram phrases contained in the documents are investigated using the Latent Semantic Analysis. Then, the pairwise document similarity scores are derived from the Singular Value Decomposition computations. The performance of the proposed system was confirmed through experiments with various data sets, exhibiting promising capabilities in identifying literal and some types of intelligent plagiarism. Finally, the proposed system was compared to Plagiarism-Checker-X, and the proposed system outperformed Plagiarism-Checker-X, especially for intelligent plagiarism.

This paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is based on both BERT embeddings and WordNet. We simply use cosine similarity to find the closest synset of the target words. Our results show that using this simple approach greatly improves the system behavior. Our model is ranked 3rd in subtask-2 for SemEval-2020 task 3.

It is a great pleasure to welcome the participants of the 32nd National Radio Science Conference in the premises of MSA University. It is also an honor for the university to organize this high profile event for the third time during the lifetime of this prestigious national conference.

Abstract High-quality three-dimensional (3D) seismic reflection and borehole data are used to characterize in detail the structural style and evolution of inversion structures in Horus oil field. This field lies in the central part of Alamein Basin, northern Western Desert of Egypt, in the hanging-wall of a major Jurassic-Early Cretaceous normal fault. This fault delineates the northwestern margin of the basin and extends in an ENE-WSW direction, bounding a half-graben trough in its hanging-wall. This trough had continued subsidence during Early Cretaceous time with depositional thickening of the Lower Cretaceous Alam el Bueib Formation. Fault displacement had ceased near the top of Barremian level. Aptian-Lower Senonian seismically traced levels show a major NE-plunging asymmetrical anticline overlying the Jurassic-Barremian half-graben. This anticline is sub-parallel to the inherited Jurassic rifting fault. At shallower Cretaceous levels, this fault is replaced by a set of WNW-ESE-trending en echelon faults of considerable displacements, which probably indicates a strike-slip component of deformation. These faults display normal separation on all displaced stratigraphic levels and tip upwards in the upper part of the Upper Cretaceous succession. This marks a considerable change in the tectonic mode of the area. NW-SE extensional faults were developed perpendicular to the fold axis, dissecting the anticline into several blocks entrapping hydrocarbons. Detailed seismic structural analysis of growth strata indicates that the fold initiated in Early Senonian time was associated with the inversion of the earlier Jurassic-Barremian half-graben. This basin inversion is attributed to the Syrian-Arc event that dominated North Africa during the Late Cretaceous time and continued through to the Early Miocene. Low fold amplitude coupled with normal displacements on the deep-seated ENE-WSW-trending Jurassic-Barremian fault indicate a mild phase of positive inversion. The Upper Senonian-Eocene stratigraphic sequences increased in thickness in the back-troughs that were associated with basin inversion.

In statistical machine translation systems, it is a common practice to use one set of weighting parameters in scoring the candidate translations from a source language to a target language. In this paper, we challenge the assumption that only one set of weights is sufficient to pick the best candidate translation for all source language sentences. We propose a new technique that generates a different set of weights for each input sentence. Our technique outperforms the popular tuning algorithm MERT on different datasets using different language pairs.

While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A R ecursive Deep Learning Model for O pinion M ining in A rabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.

Abstract Objective In December 2019, the novel coronavirus (COVID-19) infection was first reported in Wuhan city, China, which had rapidly spread as a global pandemic. This infection was commonly presented by respiratory and /or gastrointestinal symptoms. However, it is still unclear whether COVID-19 infection could be associated with central nervous system (CNS) damage which would result in development of neuropsychiatric symptoms. Method A total of five cases of suddenly emerged manic episodes during the pandemic of COVID-19 were extensively described. We presented the symptoms and described the diagnosis, clinical course, and treatment of each case. Results All patients had positive findings of ribonucleic acid (RNA) tests for COVID-19 in specimens of their sputum. The patients later developed manic symptoms during and after the recovering period of their illness. Conclusions The case series of newly emerged manic symptoms associated with COVID-19 infection highlights the essential need for evaluation of mental health status and would contribute to our understanding of the potential risk of CNS affection by COVID-19 infection. The limited number of cases would limit the generalizability of association. Future research should investigate the behavioral changes accompanying and following COVID-19 infection.

A consistency analysis is performed for one of the famous Tajweed rules in the Holy Quran - Madd rules. They are tested on records of one of the famous reference reciters - Sheikh El-Hosary-to find a consistent boundaries and to evaluate the performance of other new learners. A vowel detection algorithm is used to detect the duration of the detected Madd patterns. This algorithm was applied on a dataset of 105 minutes of Quranic records of Sheikh El-Hosary. The consistency result is found to be normally distributed with a mean of 0.379 second of one movement time of Madd and standard deviation of 0.058 second and minimum consistency of 84.6% for 68% of the detected Madd patterns.