TOWARDS A DEVELOPMENT MODEL OF LOCAL CULTURAL TOURISM THROUGH THE INVOLVEMENT OF LOCAL ACTORS (PROVINCE OF CONSTANTINE, ALGERIA)

Since the nineties, cultural tourism is considered as a form of tourism that is carried out by groups of people or institutions, whose main motive is the fulfillment of an interest and knowledge more on the culture, the history and the heritage of the chosen destination. The city of Constantine, located in northeastern Algeria, is one of the oldest cities in the Mediterranean basin. It was elected "Capital of Arab Culture 2015" due to its history, cultural and architectural heritage. The aim of this study is to examine the influences of cultural tourism on local development, to highlight the perception of local actors' roles in the development of tourism and to determine their involvement in the preservation of Constantine's cultural heritage to achieve a development model of cultural tourism in Constantine. This study is based on a literature review and field surveys, the type of questionnaire includes different types of questions: open questions, Likert scale questions and multiple choice qualitative questions. A manual processing of the data was performed using the mean and standard deviation calculation. The results of this study reveal a misunderstanding of cultural tourism among local residents hence the need to develop a model of categorization of the objectives of the study (SPIP) which proposes four key principles for the development of local cultural tourism in the city of Constantine. However, unless the proposed model elements are incorporated, cultural tourism in this city would never emerge.

Detection of records of weak local earthquakes using neural networks

Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.

Semi-Manual Processing Of Blood Clamps Waste into Chitosan Powder

Blood clams (Anadara granosa) are endemic clams found in Southeast Asia and East Asia. Blood clams are widely consumed by the public as seafood dishes in coastal food stalls. The great potential of blood clams will increase the waste of clam shells produced. The accumulation of shellfish waste will cause pollution and reduce environmental aesthetics. The chitin content in blood clam shells can be used as chitosan. Chitosan is a polymer of -(1-4) glucosamine which is formed when the acetyl group in chitin is substituted by hydrogen to become an amine group. Chitosan has antibacterial and antifungal properties. Isolation of chitosan was carried out through the stages of demineralization, deproteination, and deacetylation. The limited use of laboratories during the pandemic is a major obstacle in the isolation process of chitosan. This study aims to process blood clam shell waste into chitosan in a simple way on a home scale. Processing includes deproteination, demineralization, and deacetylation were done using tools and materials available at home. Laboratory equipment such as beakers could be replaced with pots, the reflux process was replaced by using a cloth to filter, and measuring cups were replaced with glasses. The research used 1500 grams of blood clam shell powder and produced 1050 grams of white chitosan with a slightly hard texture

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.

Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method’s performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

An overview of information extraction techniques for legal document analysis and processing

<span>In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.</span>

Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools.

Identification of approximate symmetries in biological development

Virtually all forms of life, from single-cell eukaryotes to complex, highly differentiated multicellular organisms, exhibit a property referred to as symmetry. However, precise measures of symmetry are often difficult to formulate and apply in a meaningful way to biological systems, where symmetries and asymmetries can be dynamic and transient, or be visually apparent but not reliably quantifiable using standard measures from mathematics and physics. Here, we present and illustrate a novel measure that draws on concepts from information theory to quantify the degree of symmetry, enabling the identification of approximate symmetries that may be present in a pattern or a biological image. We apply the measure to rotation, reflection and translation symmetries in patterns produced by a Turing model, as well as natural objects (algae, flowers and leaves). This method of symmetry quantification is unbiased and rigorous, and requires minimal manual processing compared to alternative measures. The proposed method is therefore a useful tool for comparison and identification of symmetries in biological systems, with potential future applications to symmetries that arise during development, as observed in vivo or as produced by mathematical models. This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’.

Impact of used battery disposal in the environment

Different types of batteries (BT’s) are also used every day and a significant amount of waste BT’s are created at the end of the day. Waste BT’s can lead to grave contamination of the atmosphere. Currently, the major waste BT processes are incineration and waste disposal, solidification management, manual processing, wet recuperation technology, dry recovery techniques and bio metallurgical (BM) technology. This paper describes many waste BT disposal practises and its impact in recent years.