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Dynamic Simulation of a Solar Water Heating System
No full textThis paper presents the development and application of a dynamic mathematical simulation model of a solar domestic hot water (DHW) production system designed for a 4-star hotel located in Fez, Morocco. The hotel consists of 60 rooms and accommodates approximately 120 guests, resulting in a high and continuous demand for hot water. The main objective of the study is to evaluate the energetic performance of the solar thermal system and to assess its capability to meet daily and annual DHW requirements under local climatic conditions.
The investigated system includes a flat-plate solar collector field, two hot water storage tanks, an external heat exchanger, and an auxiliary heating unit to ensure service continuity during periods of low solar availability. The proposed model enables fully dynamic simulation of system operation, taking into account local meteorological data, collector orientation and tilt angle, thermal characteristics of system components, and realistic DHW consumption profiles representative of a 4-star hotel.
Simulation results obtained for a typical meteorological year show that the solar system supplies, on average, approximately 58% of the annual DHW demand, with solar coverage reaching 70–75% during summer months. In addition, an average reduction of nearly 48% in auxiliary energy consumption is achieved, with peak reductions reaching up to 60% during summer months, together with a significant improvement in overall system efficiency and satisfactory thermal stability of the storage tanks, despite demand peaks associated with hotel occupancy.
The originality of this work lies in the adaptation of the developed dynamic model to a high-demand 4-star hotel application in Fez, representative of the Moroccan tourism sector, and in the detailed assessment of the impact of local climatic conditions on solar thermal system performance. The proposed approach is intended to serve as a decision-support tool for the design and optimization of solar thermal installations in mediumand large-scale hotel buildings
Physical and chemical characteristics of Wastewater from the Fez Industrial Sector in Morocco
No full textMaintaining the sustainability of natural ecosystems and improving the health of human populations depend strongly on forecasting risks from pollution and safeguarding aquatic resources. The growing industry, which encourages economic expansion, especially through the rejection of unprocessed industrial waste, is one of the primary causes of these worries. Businesses that release their raw effluent into rivers or discharge systems, such as tannage, metallurgy, and textiles, are the main contributors to pollution in Fez, Morocco. The need for immediate intervention to prevent pollution from all sources is highlighted by the possibility that these reactions will upset the natural equilibrium in the future. The purpose of this study is to describe the wastewater from three industrial sites: laiton production (S1), tanneries (S2), and textile manufacturing operations (S3). In order to quantify the pollution caused by these activities, measurements have been taken. Numerous parameters exceed both the OMS-recommended limits and Moroccan standards, according to the results of physico-chemical studies. The effluents’ pH ranges from 5.91 to 9.24, their turbidity can reach 880 NTU, and their conductivity ranges from 2520 to 16740 μS/cm. The chemical demand for oxygen (DCO) ranges from 322.5 to 25,500 mg O2/L, while the biochemical demand for oxygen (DBO5) ranges from 135 to 9,800 mg O2/L. Furthermore, the content of nickel (Ni) is 172.46 mg/L, whereas the concentration of chloride ranges from 354 to 2903.5 mg/L
Research trajectories in Moroccan coastal environmental science: Bibliometric insights from scopus and web of science
No full textThis study presents the first comprehensive bibliometric assessment of scientific research addressing heavy metal pollution in Moroccan coastal environments over the period 1980–2024. A dataset of 2,031 publications was extracted from Scopus and Web of Science and analyzed using the Bibliometrix package in R. The results reveal a steady and pronounced increase in scientific output, with publication volumes rising sharply after 2000 and reaching their highest levels between 2020 and 2022. Research efforts are strongly concentrated on ecologically vulnerable systems, including the Nador and Marchica lagoons, Oualidia, and the Bay of Dakhla, where the most frequently investigated metals are Pb, Cd, Zn, Cu, and Hg. Collaboration network analysis highlights the central role of Moroccan institutions, while also revealing a partial dependency on Euro-Mediterranean scientific partnerships. Despite this sustained research dynamic, several critical gaps persist, including limited interdisciplinary integration, the absence of nationwide synthesis studies, and comparatively low global visibility. Overall, this bibliometric investigation provides a strategic evidence base to guide future research agendas, reinforce international collaboration, and support science-based decision-making for the sustainable management of Moroccan coastal ecosystems
An AI-driven framework for predicting sleep quality and delivering personalized recommendations using digital wellbeing and lifestyle data
No full textSleep is essential for maintaining mental clarity, emotional stability, and physical health, as supported by extensive research in sleep science. However, modern lifestyles characterized by excessive screen exposure, irregular routines, and heightened stress levels have led to a noticeable decline in sleep quality. Existing AI and digital health tools for sleep monitoring remain expensive or heavily reliant on wearable devices, creating a gap in accessible and non-intrusive sleep assessment methods. To address this issue, the present study proposes a low-cost, software-based AI system that predicts sleep quality without specialized hardware. The system integrates behavioral factors such as exercise duration, caffeine intake, and stress levels with digital wellbeing metrics including screen time, app usage patterns, and nighttime device activity, all of which have been shown to influence sleep patterns. After preprocessing, machine learning models such as Random Forest and XGBoost classify sleep quality into Good, Average, or Poor, aligning with prior research utilizing behavioral and physiological indicators for sleep prediction. A user-friendly dashboard visualizes trends and provides personalized recommendations, such as reducing nighttime screen exposure to improve sleep hygiene. This AI-driven approach offers an accessible and actionable framework for improving sleep health
Detecting early signs of depression in twitter posts through LSTM-based sentiment analysis
No full textDepression is one of the most common mental health disorders, yet a large proportion of the cases are left undiagnosed due to associated social stigma, limited awareness, and lack of timely clinical intervention. The unprecedented growth of social media has encouraged users to frequently express their emotions on the social Web, thus opening an opportunity for the early detection of psychological distress from their digital footprints. Most of the available research works target single-language sentiment analysis or few classifier benchmarking, and thus there is a lack in multilingual and comparison-based deep learning-based studies. This work attempts to fill such a research gap by proposing a supervised machine learning framework for the identification of depression indicators from both English and Marathi tweets. The proposed approach integrates the standard NLP preprocessing—tokenization, stop-word removal, stemming, and TF-IDF and Word2Vec-based feature extraction—with training multiple classifiers, namely Logistic Regression, SVM, Random Forest, and LSTM. The experimental results demonstrate that the LSTM model yields state-of-the-art performance compared to other traditional models due to its natural ability to capture contextual dependencies of texts. The findings have important implications for the social media-driven method for early mental health screening and public health monitoring and digital psychological intervention systems
Nyay-Sahayak - integrating legal processes with Al and local languages
No full textWith 47 million pending cases and only 21 judges per million population, India is facing a serious crisis of legal access, which is disproportionately affecting the rural population. This Paper proposes an AI- powered multilingual legal aid platform called Nyay Sahayata, which generates court-compliant documents (FIRs, RTIs, affidavits) in over 22 Indian languages. Our system uses a novel Retrieval Augmented Generation (RAG) framework along with IndicBERT and AI4India's IndicTrans2 for accurate multilingual legal processing. The estimated performance analysis on the proposed framework indicates a document acceptance efficiency of 95.2% from legal authorities, reduced filing time from 15 days to an average of 12.7 minutes, and demonstrated 92.8% accuracy in multilingual legal vocabulary processing. Voice input functionality is designed to help users with low literacy independently prepare legally valid documents. Our findings suggest that AI-powered multilingual legal assistance could significantly improve access to justice for disadvantaged populations. The RAG framework ensures legal accuracy while removing barriers to voice input literacy. This paper proposes a comprehensive framework for voice-enabled multilingual legal AI tailored to developing regions, with the potential to improve global access to justice
Road Noise in Europe: The Case for Transparent and Standardised Data Presentation
No full textIt has been nearly three decades since the European Commission adopted the Green Paper on Future Noise Policy in 1996, marking a pivotal step towards developing an EU - wide noise policy, ultimately leading in 2002 to the formal adoption of Directive 2002/49/EC, known as the Environmental Noise Directive. The END mandates that the EU Member States develop strategic noise maps and action plans every five years. Since its inception, four rounds of noise mapping and action planning have been completed: the first in 2007, followed by subsequent rounds in 2012, 2017 and 2022. These efforts have systematically assessed environmental noise exposure across Europe, informing action plans aimed at reducing its impacts. A significant evolution in EU noise policy has been the requirement for all EU Member States to use Common Noise Assessment Methods in Europe for their noise mapping activities. Based on mapping results, road traffic continues to be the primary source of environmental noise in urban areas, with millions of individuals exposed to harmful noise levels. Increasing evidence underscores the health risks associated with noise exposure, with studies suggesting that reducing road traffic noise to levels recommended by the World Health Organization could lead to significant improvements in public health outcomes. Despite these concerns, obtaining accurate and comprehensive noise data from Member States remains a persistent challenge. This paper examines the data submitted from EU Member States under the END and identifies critical needs for the future
Antibacterial Activity of Endophyte Bacteria Isolation from Mangrove Leaves
No full textAntibacterial activity testing is a fundamental method for evaluating the ability of bacterial isolates to inhibit the growth of pathogenic microorganisms. This study aimed to identify and describ endophytic organisms by isolating bacteria from the mangrove leaves of Scyphiphora hydrophyllacea Gaertn, and to assess their antibacterial potential against Staphylococcus aureus and Escherichia coli. Antibacterial assays were conducted using metabolites extracted from the endophytic isolates, employing both Kirby-Bauer test and Minimum Inhibitory Concentration (MIC) methodologies. Of the 12 bacterial isolates obtained, seven exhibited inhibitory activity against S. aureus and E. coli. Notably, isolates SH1 and SH2 demonstrated the most pronounced inhibitory zones, varying from 10 mm to 12 mm. MIC analysis revealed that both SH1 and SH2 effectively suppressed E. coli growth at a concentration of 1.56 ug/ml, as indicated by the reduced absorbance levels. For S. aureus, the MIC values were determined to be 12.5 ug/ml SH1 and 3.125 ug/ml SH2. These findings suggest that endophytic bacteria isolated from S. hydrophyllacea possess promising antibacterial properties and warrant further investigation for their potential therapeutic applications. Based on the identification results, bacterial isolates SH1, SH2, and SH7 were identified as Amphibacillus sp., Sporolactobacillus sp., and Bacillus sp
Clustering Haplotypes in Native Papuans Based on Polymorphisms in the Sequence of the LDLR Gene
No full textSingle-nucleotide polymorphisms (SNPs) within the LDLR can serve as genetic markers for diagnosing susceptibility to coronary heart disease. The presence of SNPs in this gene can serve as a basis for the formation of haplotype clusters within a population. Papua exhibits significant ethnic diversity, potentially influencing genetic variation within the LDLR gene. This study aimed to cluster the haplotypes of native Papuans based on SNPs in the LDLR gene sequence. In this study, the rear end of the LDLR gene was sequenced in 20 native Papuans from tribes inhabiting different ecological zones. Sequence analysis revealed four SNPs that formed six haplotypes. Two SNPs were located at intron 17, namely IVS17- 80 G>A and VS17-42 A>G, and two SNPs were located at the 3'UTR, namely *52G>A and *504G>A, with a nucleotide diversity of 0.00185. The identified haplotypes were GAGG, GGGG, GGGA, AGGG, GAAA, and AAAA, with a diversity of 0.726 ± 0.075. Four haplotypes (GAGG, GGGG, GGGA and AGGG) were clustered into one group (Cluster A), whereas the remaining two haplotypes (GAAA and AAAA) formed another distinct cluster (Cluster B). These findings highlight the potential of haplotype clustering in characterizing the population structure of Papuan tribes across diverse ecological regions