Badji Mokhtar University

Abstract We explore direct and inverse problems for the bi-Laplace equation within a bounded, simply connected planar domain. The direct problem is recast into a system of Fredholm integral equations via Green-type identities, ensuring the existence and uniqueness through functional analysis in appropriate Sobolev spaces. For the inverse problem, we consider the reconstruction of missing Cauchy boundary data on a non accessible portion of the domain, where Riquier-Neumann-type conditions are imposed. To counteract the intrinsic ill-posedness of this data completion task, the problem is reduced to a nonlinear boundary integral system, which is stabilized through Tikhonov regularization. Numerical investigations validate the robustness and accuracy of the proposed approach, establishing its effectiveness in recovering missing boundary information for fourth-order elliptic models.

This research examines the role of entrepreneurial support in sustaining small and medium enterprises (SMEs). The case study focuses on the National Unemployment Insurance Fund (CNAC) in Annaba and El Tarf, Algeria, administratively known as wilayas. SMEs are vital for job creation, economic growth, and local innovation, yet they face persistent challenges in financial management and long-term survival. The study employs a descriptive-analytical methodology supported by empirical data and questionnaires targeting entrepreneurs engaged in CNAC’s programs. It emphasizes key forms of assistance, including financial and administrative support, training and capacity building, and follow-up processes. Findings indicate that entrepreneurial support has helped reduce unemployment by encouraging business creation. However, such support alone is insufficient to ensure long-term economic sustainability unless complemented by policies such as tax reductions, improved facilities, and a more favorable entrepreneurial climate. The study therefore recommends reconsidering current support mechanisms to help SMEs transition from aid dependency to competitiveness and sustainability, which is essential for local development and a resilient national economy.

Gastric cancer (GC) remains a significant global health challenge with high mortality rates, often due to late-stage diagnosis. We hypothesize that Raman spectroscopy (RS) (a modern minimally invasive technique that uses light to analyze the molecular composition of tissue, generating a unique "fingerprint" that reveals biochemical details, distinguishing between normal and diseased tissues.) when combined with Machine learning (ML) would provide accurate and expedite approach of detecting GC. We aim to meta-analyze the diagnostic accuracy of ML-enhanced RS in differentiating GC component from normal tissue.This study was conducted following PRISMA-DTA guidelines. We searched PubMed, Scopus, Web of Science, VHL, and Google Scholar up to the end of February 2025. with an updated search conducted on 14 July 2025. We included any peer-reviewed manuscript that assessed ML-based RS technique for detecting GC components against normal control during endoscopy and reported sufficient data to construct 2 × 2 contingency table for assessing basic diagnostic metrics such as the sensitivity and specificity were included. Methodological quality of studies deemed eligible was assessed using QUADAS-2 risk of bias tool. Data on true positives, true negatives, false positives, and false negatives were extracted to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC) using R software. Heterogeneity was assessed with I2 statistics and Deeks' funnel plot was employed to examine potential publication bias. Moreover, we further subgrouped individual study metrics based on source of sample, RS technique, AI model, and the experimental context to assess their role in solidify results by controlling several confounders for heterogeneity.A total of 28 studies were enrolled comprising 2,392 patients and 8861 gastric spectra. Twenty-one studies (75%) applied per-spectra approach to analyze the diagnostic utility for GC tissue detection from non-pathological tissue. On the other hand, seven studies (25%) approached analysis as of per-patient stratification evaluating GC patients from healthy subjects. The pooled estimates of the sensitivity and specificity of per spectra approach yielded 92% (95% CI: 88-95%) and 93% (95% CI: 89-96%), respectively, and the AUC was 0.955. On the other hand, the pooled analysis of studies implemented per patient assessment approach yielded excellent sensitivity, specificity, and AUC as well with 95% (95% CI: 87-98%), 93% (95% CI: 89-95%), 0.928, respectively. Subgroup analyses showed that studies using the KNN model demonstrated the highest diagnostic accuracy. Conventional Raman spectroscopy also achieved superior performance across most metrics. Serum-based samples yielded higher sensitivity and specificity than tissue samples, though the limited number of serum studies warrants cautious interpretation. In vitro studies showed slightly better diagnostic accuracy than in vivo studies, although the difference was not statistically significant. Substantial heterogeneity was observed across per-spectra studies (I² = 82.1% for sensitivity and 91.2% for specificity), but no significant between-study variation was observed in per-patient analyses (I² = 27.3% for sensitivity and I² = 0% for specificity). No substantial publication bias was detected based on Deeks' funnel plot asymmetry test, with p = 0.394 for the per-spectra analysis and p = 0.858 for the per-patient analysis.Our meta-analyses' results provide strong evidence that ML-enabled RS from different body sources and across various ML algorithms subtypes significantly improve the GC detection rate and is superior at differentiating GC from healthy tissue during upper GI endoscopy. This approach led to a more precise and real-time decision-making regarding biopsy and excision, given our excellent diagnostic accuracy and low between-study heterogeneity that we obtained, integrating ML-enhanced detection of significant spectra into clinical workflows as valuable diagnostic adjunct particularly during the endoscopy will optimize false negative rate and overall patient outcomes.

ABSTRACT This study investigates the diversity and ecological preferences of saproxylic beetles in the Berouaga Forest, part of the Edough Forest Massif in northeastern Algeria. Despite their key role in wood decomposition and nutrient cycling, saproxylic beetles remain under‐studied in Mediterranean and North African forests. The study assesses the influence of seasonal variation and dominant tree species on saproxylic beetle assemblages. Sampling was conducted over a full year using Polytrap interception traps, Barber pitfall traps and direct observation. A total of 434 saproxylic individuals were recorded, representing 33 species and 15 families. Results revealed pronounced seasonal dynamics, with peak abundance and diversity in spring and summer. Tree species strongly shaped community structure: Buprestidae and Melyridae were dominant in cork oak forests, while Curculionidae were more associated with mixed forests. Dead wood presence and its decomposition stage significantly influenced species distributions. These findings demonstrate the importance of forest composition and dead wood heterogeneity in shaping saproxylic beetle communities, and provide critical baseline data for conservation and sustainable forest management in Mediterranean ecosystems.

This study introduces a novel methodology aimed at minimising total harmonic distortion (THD) in grid-connected photovoltaic (PV) systems (GCPVs) through the implementation of a maximum power point tracking (MPPT) approach based on artificial neural networks (ANN). High THD levels in PV systems can lead to inefficiencies, power quality issues, and potential damage to the grid infrastructure. Although traditional MPPT methods effectively optimise the power output, they often fail to address harmonics. The proposed ANN-based MPPT algorithm improves PV power harvesting while actively minimising the harmonic distortions. The ANN was trained using a comprehensive dataset that included various environmental conditions, ensuring robust performance in diverse operational scenarios. Simulation results demonstrate that the ANN-based MPPT approach significantly reduces THD to below 1% across various irradiance levels, in contrast to the 1.18% to 2.72% observed with conventional methods such as perturb and observe (PO), while simultaneously preserving optimal power output. Reducing harmonic distortion improves the power quality, system efficiency, and lifespan of grid-connected components. This study highlights ANN-based control strategies for addressing the challenge of maximising energy harvesting and maintaining power quality in modern PV systems, offering a solution for the sustainable integration of solar energy into the grid.

Weak soils present significant challenges for highway construction in seismic regions, often leading to instability issues such as settlements and slope failures. These conditions necessitate safe, effective, and cost-efficient solutions to ensure embankment stability. Despite the critical importance of embankment stability under dynamic loading, research on soil reinforcement techniques in seismic scenarios is limited. This study evaluated the seismic performance of conventional deep cement mixing (DCM) columns versus innovative T-shaped deep mixing (TDM) columns for highway embankments on soft soil through three-dimensional finite element analysis via PLAXIS 3D. After validation, a comprehensive parametric study investigated the effects of column type, spacing (1.67m, 2.14m, and 3.0m), and seismic loading on embankment response parameters, including settlement, lateral displacement, and excess pore water pressure. The results demonstrate that both reinforcement methods significantly enhance seismic stability compared with unreinforced conditions. Compared with unreinforced embankments, TDM columns achieved superior performance, with settlement reductions of 89.95% versus 36.11% for DCM columns. Under seismic loading, the TDM columns exhibited 88.31% less settlement than the DCM columns at the embankment crest. Parametric analysis revealed that closer column spacing (1.67m) reduced settlements to 0.0032m for TDM compared with 0.023m at wider spacing (3.0m). The TDM columns also demonstrated more controlled consolidation behavior with smoother settlement time histories and reduced excess pore water pressure fluctuations. The findings indicate that TDM columns provide superior seismic performance through enhanced load transfer mechanisms and improved soil‒structure interactions, making them the preferred solution for highway embankments in seismically active regions.

L’intelligence artificielle (IA) accentue la centralisation de l’information entre les mains de quelques plateformes dominantes (les « GAFAM »). Cet article examine comment les mécanismes d’extraction et de traitement des données associés à l’IA renforcent le pouvoir informationnel de ces acteurs, quels en sont les risques pour le pluralisme et la souveraineté numérique, et quelles voies réglementaires, techniques et sociales peuvent contribuer à rééquilibrer le paysage informationnel.