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Background Most diagnosed oral cancer cases in Saudi Arabia are in the Jazan region. A common type of smokeless tobacco \"Shammah\" is prevalent in this region. This study aimed to gain an in-depth understanding of the possible psychosocial determinants of Shammah consumption among adult Shammah users in Jazan region. Methods A qualitative study was conducted by means of one-on-one interviews among thirty adult Shammah users. Participants were recruited by means of a purposive sampling technique. Data were collected using a semi-structured interview guide utilizing face-to-face and phone-call interviews. Thematic analysis with hybrid approach was used to analyze the dataset. Results Twenty-four sub-codes within four overarching themes were generated. Participants revealed uncertainty related to Shammah composition, how to quit knowledge and Shammah prevention/cessation programs. Shammah use identified as a normal phenomenon in society. Its use was frequently reported in participants’ close network but most users faced family and peers’ disapproval. Some users expressed joy, happiness and focused when using Shammah. Others were disgusted or neutral. Many users believed Shammah causes cancer and tears oral tissues. Others believed it relieves toothache or has no effect. Majority of users were confident to quit and recalled some quitting aids. Toothache, craving, drinking tea and chewing Khat (leaves of Catha edulis plant that causes moderate euphoria) perceived to be triggers to use Shammah. Availability of Shammah, withdrawal symptoms, stress, lack of support, seeing others using Shammah, losing part of routine and toothache were barriers to quit. Conclusions Shammah use was associated with uncertainty about Shammah composition and quitting knowledge, social acceptability, influence from family/friends, a range of positive and negative attitudinal beliefs toward its use and high quitting efficacy beliefs. Future interventions targeting Shammah should address the acknowledged triggers and barriers in the present study including the dual use of Shammah and Khat.

The objective of this study is to highlight the reality of integrating disabled children according to the joint ministerial decision - between the Ministry of National Education and the Ministry of National Solidarity, Family, and Women's Issues - dated 11 Jumada al-Awwal of the year 1435 corresponding to March 13, 2014. This decision defines the modalities of opening special classes for disabled children within public educational establishments under the jurisdiction of the National Education sector, as well as the challenges and difficulties encountered in the field during integration. The importance of this study lies in obtaining a realistic assessment of the field and data to assist integration project managers in identifying priorities and developing and adopting educational policies in the planning of any future project concerning the category of disabled children, as well as to renew practices in the field and adapt them to the characteristics of this category, addressing their concerns. This study was conducted at the Directorate of Social Action and Solidarity of the Ouargla Province. Researchers used a descriptive methodology and employed interviews as a data collection tool. The study sample included educational staff. The study concluded that the reality of integrating disabled individuals according to the joint ministerial decision still requires further efforts to achieve the desired aspirations and hopes, and it requires special attention. It was also found that there are numerous material, human, and pedagogical challenges that hinder the integration process.

Short-term fluctuations in dissolved oxygen are difficult to capture in warm outdoor Nile tilapia (Oreochromis niloticus) ponds using periodic manual measurements, yet they strongly influence fish performance and farm management. This study presents a preliminary field evaluation of a low-cost IoT workflow for dissolved oxygen monitoring and short-horizon forecasting in pond-based tilapia culture. An ESP32-based sensing node continuously measured dissolved oxygen, temperature, and pH, transmitted readings to a cloud backend, and generated short-horizon forecasts from 5 min aggregated windows. During live validation from 1 to 10 April 2026, the 30 min forecast achieved a mean absolute error of 0.783 mg/L and directional accuracy of 60.23%, with only modest improvement over a persistence baseline. The 6 h forecast achieved 1.109 mg/L and 53.82%, respectively, indicating limited predictive value at the extended horizon. An extended 47-day field deployment (May–June 2026) captured four sensor-recorded low-DO events and two documented power outages, causing sensor downtime and providing additional field-deployment evidence. These results demonstrate the engineering feasibility of the integrated workflow, but they do not establish robust operational forecasting validity because the data were collected from one pond, high-frequency records were temporally correlated, and independent reference-meter validation was not available. The study is, therefore, best interpreted as a proof-of-concept field evaluation that identifies practical requirements for future low-cost aquaculture forecasting systems.

This scholarly article examines the pivotal role of digital administration in enhancing political rationality and governance in contemporary political systems. As governments worldwide increasingly adopt digital technologies, this study explores how these tools reshape decision-making processes, citizen engagement, and overall political efficacy. Through a comprehensive analysis of theoretical frameworks, existing literature, and case studies, the research investigates the impact of digital administration on transparency, accountability, and participatory governance. Findings suggest that while digital administration offers significant potential for improving political rationality and governance, it also presents challenges related to the digital divide, cybersecurity, and privacy concerns. The article concludes with policy recommendations and future prospects for the integration of emerging technologies into political governance.

Heavy metals are major stressors for benthic macroalgal communities in marine ecosystems. In this study, the effects of copper and cadmium on some physiological parameters along with antifouling defense of the marine macroalga were assessed under laboratory conditions. Macroalgal samples were treated with three concentrations (1 mg l , 3 mg l and 5 mg l ) of copper and cadmium for 2 and 7 days. After treatment, algal samples were analyzed for chlorophyll- , carotenoid, total polyphenol and total antioxidant capacity. Also, algal extracts were tested against biofilm-forming bacteria strains to understand differences in antifouling activity. The results indicated that exposure of to copper and cadmium, on the one hand, induced protective mechanisms such as total phenol production and antioxidant capacity against metal stress and, on the other hand, reduced photosynthesis. While the extract obtained from control algal samples showed a strong inhibitory effect on the growth of biofilm-forming bacteria, treatment with heavy metals resulted in reduced antibiofilm activity. In general, the results revealed that exposure of macroalgae to heavy metals can affect antifouling defense traits in addition to changes in photosynthetic pigment content.

Wildland fires are the most common peril for forests due to climate change. Furthermore, it is an uncontrollable disaster and poses a great deal of threat to human health and ecosystems. In Algeria, almost 40,000 hectares are burned each year, approximately 1% of all existing woodlands of the country. In this work, the forest fire event prediction is highlighted using machine learning. The study utilized data sets from several sources, including fire data obtained from the fire information system for resource management by NASA (FIRMS) and climate data accessed from the NASA energy project API, derived from the MODIS satellite (NASA forecasting of energy resources around the world). Fire data from NASA provides real-time information, spanning from 2000 to 2020. The methodology process of creating the prediction system involved collecting the data, pre-processing the data, finding the best models, training and testing the models, and evaluating them for validation. The machine learning model was trained and validated using 70% and 30% of the set features with a performance accuracy of up to 86%. Upon completion, we deployed our selected machine learning model to create a Web platform enables different end users to check possible future forest fires by select a geographical area on a world map. The objective of our machine learning model is to analyze the weather data of the selecting area on the map in real time and predict whether a fire will occur or not. This prediction system will enhance early detection, allowing prompt response measures to be implemented, reducing the risk of uncontrolled wildfires and safeguarding ecosystems and communities.

Trilobites are widespread in Lower Devonian deposits of north Gondwana, and some have been collected from two known sections of the Saoura Valley in SW Algeria, from the ‘Chefar el Ahmar’ Formation. This formation is considered to be from late Emsian to Frasnian in age, but only the lower parts of this formation have yielded trilobites. Nevertheless, no detailed studies have focused on their biodiversity and their morphological variability. New occurrences of phacopids including Barrandeops chattertoni sp. nov., Geesops fabrei sp. nov., Austerops legrandi sp. nov. and Phacops boudjemaai sp. nov. are described from this area and comparisons are made with closely allied species. These new occurrences have been integrated into analyses of intra- and inter-specific variability and biodiversity.

Trilobites are important elements of the Devonian macrobenthos; some of them were collected in the Chefar el Ahmar Formation, from two sections located near Beni Abbes in the Saoura Valley (Ougarta Basin, Saharan Algeria). This formation is characterized by alternations of claystones and limestones, and it is considered to be late Emsian to early Frasnian in age. Only the lower part of this formation has yielded trilobites so far; their presence has been known for a long time. Phacopines clearly dominate the trilobite assemblages, with Austerops, Barrandeops, Chotecops and Phacops s.l. as the main genera. Two new species are described (Austerops salamandaroides sp. nov. and Phacops ouarouroutensis sp. nov.), while some other taxa are presented in open nomenclature. Comparisons are made with closely allied species. These new trilobite occurrences have been analysed in terms of their intra- and interspecific variability and biodiversity. The occurrence of Struveaspis maroccanica, previously known from the Saoura Valley, provides an early Eifelian age, which is also confirmed by the presence of trilobites Thysanopeltis and Koneprusites, and ostracods Bairdiocypris devonica and Bufina ?subovalis.

This paper examines the complexities and challenges faced by Algerian diplomacy in the African Sahel region, with a focus on Mali, Niger, and Sudan. Through analysis of recent diplomatic initiatives, security cooperation, and economic engagements, the study highlights Algeria's strategic interests and the obstacles it encounters in projecting influence in these key Sahelian states. The research draws on a combination of diplomatic records, policy documents, and expert interviews to provide a comprehensive assessment of Algeria's evolving role in regional stability and development. Problem Statement: Algeria, as a major North African power, has long sought to play a stabilizing role in the Sahel region. However, the complex political landscapes, security threats, and competing international interests in Mali, Niger, and Sudan present significant challenges to Algerian diplomatic efforts. This study aims to identify and analyze these challenges, examining how they impact Algeria's ability to achieve its strategic objectives in the region and maintain its influence amidst changing geopolitical dynamics.

Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is $O(1)$ . Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.