Optimizing inventory management using a multi-agent LLM system

Effective inventory management requires a comprehensive capability to forecast demand and optimize stock levels, traditionally reserved for human expertise. Emerging AI methods, while providing effective solutions through deep learning models and data analytics, often lack the flexibility to incorporate dynamic market insights and real-time data. By leveraging the diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome these limitations and develop a new class of AI-driven inventory management systems. This paper presents a multi-agent framework comprising a project manager agent, a sales forecasting agent, and an inventory manager agent, which autonomously collaborate to address inventory management challenges. The agents dynamically adjust inventory plans and maintain product availability through self and mutual corrections. Simulation results demonstrate a significant increase in the inventory turnover ratio, reduced shipping costs and holding fees, and a substantial decrease in total cost, all while maintaining a zero stockout rate. Our framework showcases the potential of synergizing the intelligence of LLMs, the precision of statistical modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automating and optimizing supply chain management

Study of the catalytic wet air oxidation of p-hydroxybenzoic acid on a fresh ruthenium catalyst supported by different oxides

The catalytic wet air oxidation (CWAO) of p-hydroxybenzoic acid (p-HBA) was conducted in a batch reactor at 140 °C, and at a total air pressure of 50 bar over Ru-based catalysts. Four materials were selected as supports – TiO2, CeO2–TiO2, ZrO2–TiO2, and La2O3–TiO2 – all of which had mesopores in their texture and pollutant adsorption capacities. The supports were prepared by the sol-gel method, and then impregnated with 3 wt% of Ru precursor. Such characterization techniques as N2-sorption, XRD, XPS, H2–TPR, NH3-TPD, TEM, and HAADF-STEM were used to analyze the different solids. The correlation between catalytic activities and physicochemical properties was discussed. A significant specific surface area (SBET), a large amount of surface-active oxygen, and Lewis acidity sites were observed on cerium-containing catalysts (Ru/CeTi). Fresh Ru catalysts containing cerium showed higher activity than Ru/TiO2, Ru/ZrTi, and Ru/LaTi catalysts. It is assumed that the acidic sites and surface oxygen trap the p-HBA molecule, thus increasing the catalytic properties of the Ru particles which interact with the surface oxygen through the cerium redox process (Ce3+/Ce4+). As the presence of cerium increases surface-active oxygen, it inhibits the deposition of carbon on the surface of the Ru catalyst. The pseudo-second order (PSO) model adequately described the kinetic data of the p-HBA oxidation reaction using Ru catalysts.Peer ReviewedPostprint (published version

From underground laser scans to 3D urban geological and geotechnical models

International audienceThe near sub-surface geology, say down to 20-30-m-depth, of many cities has been massively exploited for extracting building stones and various other industrial or agricultural materials (gypsum, lime, etc…). The long-term instability of these cavities poses a significant collapse hazard conditioned by their geometry (void location, dimensions and shape) and by their surrounding rock mechanics properties. In this presentation, we show how handheld laser scanning surveys efficiently document geometric variables and can interact with 3D geological modelling of the surrounding rocks. The construction of near-surface urban geological models can then be turned into 3D geotechnical models by attributing geotechnical parameters to rock horizons and ultimately become a key subsurface knowledge component of BIM (Building Information Model). Acquiring surface and subsurface geometry is no longer a challenge thanks to handheld laser scanners. Survey loop traverses can be pieced together to link surface and subsurface geometry with accuracies better than 1 m (an accuracy level compatible with urban risk management maps at 1/5.000) (DEWEZ et al., 2017). However, the hundreds of-millions of 3D points describing the cavity surface cannot be integrated as such into geomodeling software. Too many points with not high enough information. We suggest two different scenarios to perform their integration: (i) as independent validation of geomodeling hypotheses, or (ii) as geomodel constraints. In the first integration scenario, point cloud information is passed to the geomodeling software at a minimal level. A decimated triangular meshed model can be used to intersect the geomodel. Triangulation is performed at the point cloud processing software level (e.g. GeoSLAM desktop or Cloud Compare) and intersection is handled at the geomodelling software level with a generic query concept (here GeoModeller software with a generic query API – LOISELET et al.,2016). In this instance, cavity mesh triangular faces are refined based on the geological model queries (relying on the marching triangles algorithm) and provide geotechnical attributes based on the geological formations given by the geomodel. This scenario offers a visual display of geological properties (Fig. 1) for checking that modelled layers and structures match those observed in underground outcrops. In the second scenario, which is more integrated, higher level information is passed to the geomodelling tool. Planar surfaces of marker horizons are segmented from the point cloud either manually using Compass (THIELE et al., 2017) or semi-automatically with FACETS (DEWEZ et al., 2016) and passed as structural data objects to constrain the geomodel (Fig. 2). This data integration is demonstrated on a ca. 1 ha underground building stone quarry of the eastern suburbs of Orléans, Central France. The cavity was scanned at ca. 1pt/1cm with a Zeb-Revo (90 Mpts underground and 35 Mpts above ground). A geomodel of the subsurface area (Calcaire de Beauce, Tertiary) was created with the GeoModeller software as a tabular sub-horizontal multilayer environment. The geomodel infers rock distribution over a domain of ca. 200 x 200 m with geological and geotechnical information (e.g. limit pressure for dimensioning building foundations). Both approaches leverage a generic API query tool informing which domain surrounds a point and whether a geological contact cross-cuts a triangular face

Efficient and Sustainable Treatment of Tannery Wastewater by a Sequential Electrocoagulation-UV Photolytic Process

Tannery wastewater contains large amounts of pollutants that, if directly discharged into ecosystems, can generate an environmental hazard. The present investigation has focused the attention to the remediation of wastewater originated from tanned leather in Tunisia. The analysis revealed wastewater with a high level of chemical oxygen demand (COD) of 7376 mgO2/L. The performance in reduction of COD, via electrocoagulation (EC) or UV photolysis or, finally, operating electrocoagulation and photolysis in sequence was examined. The effect of voltage and reaction time on COD reduction, as well as the phytotoxicity were determined. Treated effluents were analysed by UV spectroscopy, extracting the organic components with solvents differing in polarity. A sequential EC and UV treatment of the tannery wastewater has been proven effective in the reduction of COD. These treatments combined afforded 94.1 % of COD reduction, whereas the single EC and UV treatments afforded respectively 85.7 and 55.9 %. The final COD value of 428.7 mg/L was found largely below the limit of 1000 mg/L for admission of wastewater in public sewerage network. Germination tests of Hordeum Vulgare seeds indicated reduced toxicity for the remediated water. Energy consumptions of 33.33 kWh/m3 and 314.28 kWh/m3 were determined for the EC process and for the same followed by UV treatment. Both those technologies are yet available and ready for scale-up

Soil geochemistry, edaphic and climatic characteristics as components of Tunisian olive terroirs: Relationship with the multielemental composition of olive oils for their geographical traceability

journal articl

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models for Skin Lesion Analysis

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal hyperparameters for the proposed CNN models were determined using the Grid Search Optimization technique. This approach determines optimal values for architectural and fine-tuning hyperparameters, which is essential for learning. Rigorous evaluations of loss, accuracy, and confusion matrix thoroughly assessed the performance of the CNN models. Three datasets from the International Skin Imaging Collaboration (ISIC) Archive were utilized for the classification tasks. The primary objective of this study is to create a robust CNN system that can accurately diagnose skin lesions. Three separate CNN models were developed using the labeled ISIC Archive datasets. These models were designed to accurately detect skin lesions, assess the malignancy of the lesions, and classify the different types of lesions. The results indicate that the proposed CNN models possess robust capabilities in identifying and categorizing skin lesions, aiding healthcare professionals in making prompt and precise diagnostic judgments. This strategy presents an optimistic avenue for enhancing the diagnosis of skin cancer, which could potentially decrease avoidable fatalities and extend the lifespan of people diagnosed with skin cancer. This research enhances the discipline of biomedical image processing for skin lesion identification by utilizing the capabilities of DL algorithms

Dacryocystostomies par voieendonasale:indications et technique chirurgicale

Introduction : La dacryocystorhinostomie consiste à dériver le contenu du sac lacrymal directement vers la lumière de la fosse nasale correspondante en réalisant une large ouverture dans la paroi osseuse et muqueuse du sac, court-circuitant ainsi le canal lacrymonasal obturé. nous nous proposons, à travers cette étude rétrospective, de discuter les aspects techniques chirurgicaux de la dacryocystostomie par voie endonasale et de revoir les indications opératoires.Mots-clefs : dacryocystite ; dacryocystorhinostomie ; chirurgie endonasaleThe purpose of dacryocystorhinostomy is to derive the content of the lacrimal sac directly into the light of the corresponding nasal cavity by making a large opening in the bony wall and sac mucosa, by passing the blocked nasolacrimal duct. We intend, through this retrospective study, discuss the technical aspects of surgical dacryocystostomy by endonasal and review the indications for surgery.Keywords : dacryocystitis; dacryocystorhinostomy; endonasal surgery

Ultrasound cervical length in predicting preterm birth

BackgroundPreterm birth is a leading cause of perinatal morbidity and mortality and represents a major public health problem. It is associated with a 15–20 per cent mortality rate and remains responsible for 75 per cent of perinatal deaths in foetuses without anomalies.AimsThe aim of this study was to evaluate the importance of cervical length measured in the first trimester (11–14 Weeks of amenorrhea “WA”) and the second trimester (20–24 Weeks of amenorrhea” WA”) in an asymptomatic population of singleton pregnancies to assess the risk of spontaneous preterm birth compared to the digital assessment.Methods We conducted a prospective, longitudinal study involving 117 asymptomatic women with singleton pregnancies between January and December 2015.Results In our study, the clinical examination had a low positive predictive value and a low sensibility for screening women at risk of preterm delivery. Cervical length less than 35mm between 12–14WA and 30mm between 22–24WA predicts the occurrence of preterm birth with a high sensitivity (Se), and specificity (Sp).ConclusionWe conclude that ultrasound screening of preterm delivery is now highly recommended

Application of a standard risk assessment scheme to a North Africa contaminated site (Sfax, Tunisia) - tier 1

Phosphorus is a critical element to agriculture, consequently global phosphate rock demand will remain rising to feed a growing world population. The beneficiation of phosphorous ore gives rise to several tons of a waste by-product [phosphogypsum (PG)] which valorisation is limited, within other reasons, by the risks posed to environment and human health. Although threatening, the accumulation in stacks is the only procedure so far practiced by several countries as a means to get rid of this industrial externality. As part of a NATO Science for Peace Project (SfP 983311) this study describes the application of an environmental risk assessment (ERA) framework, to assess the risks posed by a PG stack to the surrounding soils, in Sfax, Republic of Tunisia. The ERA followed a weight of evidence approach, supported by two lines of evidence (LoE): the chemical (ChemLoE) and the ecotoxicological (EcotoxLoE). Integrated risks point for risk values greater than 0.5 in soils collected in PG stack surrounding area. Soil salinization, has likely contributed to the exacerbation of risks, as well as to the lack of consistency between both LoEs. This study highlights the need of rethinking the weight given to each LoE in ERA, in areas where soil salinization is a reality.publishe