Optimization Methodology Research Articles

EUFOREA meeting on defining disease states in allergic rhinitis: towards a unified language in AR

Allergic rhinitis (AR), the most prevalent immunological disease, affects approximately 400 million individuals globally and can significantly impact quality of life (QoL). Despite nearly 25 years of guidelines, AR remains largely under- diagnosed, suboptimally treated and poorly controlled. In the light of new knowledge and treatment options, there is a necessity to update or revise fundamental AR definitions to facilitate communication across diverse specialties engaged in its treatment and to improve patient care. The European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) convened a meeting of experts and patient representatives to deliberate the optimal methodology for measuring AR treatment responses and establishing novel treatment goals. This paper presents a consensus on revised AR definitions, including control, severe allergic rhinoconjunctivitis (SARC), refractory severe allergic rhinoconjunctivitis (R-SARC), remission, resolution, improvement, exacerbation, treatable traits (TTs), treat to target, relapse, progression, disease modification, and prevention.

Concurrent design optimization of powertrain component modules in a family of electric vehicles

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting “one-size-fits-all” problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules’ sizes and the powertrain operation of the entire family, for given driving cycles and modules’ multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5%. Moreover, our concurrent design optimization methodology can reduce the TCO by up to 17% for the market conditions considered in our sensitivity study.

Essential fatty acids as major lipid fraction in Nostoc muscorum by optimization of mixotrophic culture

Linoleic and α-linolenic acids are essential fatty acids for animals and humans that must be provided in their diet. Several sources have been studied, however production by cyanobacterium Nostoc muscorum has not been thoroughly studied. We aim to evaluate the effect of mixotrophic culture on biomass and lipid content, and subsequently to characterize and quantify the fatty acids present in N. muscorum. The individual and interactive effects of three culture conditions (glycerol concentration, acetate concentration and culture time) on biomass and lipid production by N. muscorum were investigated through a three factor, three-level Box-Behnken experimental design of response surface methodology for process optimization. Glycerol and acetate addition proved to have a synergetic effect on growth of N. muscorum which caused an increased in biomass and lipid production at tenth day of culture compared to the control culture. Moreover, the fatty acid profile showed a high percentage of polyunsaturated fatty acids, especially linoleic (52.64%) and α-linolenic (33.34%), suggesting possible applications for nutraceutical and feed industries.

Topical drug formulation for enhanced permeation: A comparison of Bayesian optimisation and response surface methodology with an ibuprofen-loaded poloxamer 407-based formulations case study.

Topical skin products aim to address aesthetic, protective, and/or therapeutic needs through interaction with the human epidermal system. Traditionally, formulation development relies on empirical knowledge and trial-and-error experiments. In this paper, we introduced the Bayesian optimisation method and compared it with the traditional response surface methodology (RSM) for topical drug formulation. The objective was to optimise the formulation composition of ibuprofen gel-like to achieve a maximum flux through in vitro permeation tests (IVPTs). As a model system, poloxamer 407, ethanol, and propylene glycol (PG) were selected as the key excipients, whose concentrations were optimised. Strat-M membrane, serving as a surrogate for human skin, and Franz cell diffusion were employed in IVPTs. Two sets of experiments were conducted under identical conditions for 30 h. Under the RSM approach, the optimised ibuprofen gel-like formulation was identified with a poloxamer 407: ethanol: PG ratio of 20:20:10, achieving a measured permeation flux of 11.28 ± 0.35 μg cm-2h-1. In comparison, Bayesian optimisation, after four iterations, yielded an optimised formulation with a ratio of 20.95:19.44:12.14, resulting in a permeation flux of 14.15 ± 0.77 μg cm-2h-1. These findings highlight the potential of Bayesian optimisation as an effective tool for improving topical drug formulations.

On the optimization of fatigue limit in additively manufactured fiber reinforced polymer composites

Abstract This study uses the Taguchi optimization methodology to optimize the fatigue performance of short carbon fiber-reinforced polyamide samples printed via fused deposition modeling (FDM). The optimal printing properties that maximize the fatigue limit were determined to be 0.075 mm layer thickness, 0.4 mm infill line distance, 50 mm/s printing speed, and 55 °C chamber temperature with layer thickness being the most critical parameter. To qualify fatigue endurance limit, the energy dissipation in uniaxial fatigue was quantified by using hysteresis energy and temperature rise at steady state. From these results, the fatigue limit for a specimen printed with optimized printing parameters was predicted to be 69 and 70 MPa from hysteresis energy and temperature rise at steady state methods, consecutively, and it was experimentally determined to be 67 MPa. This work demonstrates the effectiveness of the Taguchi optimization method when applied to additive manufacturing and the swift ability to predict the fatigue limit of a material with only one specimen to produce optimal additively manufactured components for industrial applications, as validated by experimental fatigue testing.

Review of Design Schemes and AI Optimization Algorithms for High-Efficiency Offshore Wind Farm Collection Systems

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.

Machine learning gold price forecasting

ABSTRACT Price projections for main metal commodities have been highly valued by many market players for a substantial period of time. Our research examines the daily reported price of gold in order to address the issue. The sample that is being investigated covers a period of ten years, beginning on 22 April 2014 and ending on 17 April 2024, and the price series that is being examined has significant business repercussions. When it comes to this particular scenario, Gaussian process regression models are constructed by using cross-validation approaches and Bayesian optimization methodologies. The strategies that are produced as a consequence are then used in order to supply price predictions. With a relative root mean square error of 0.8706%, our empirical prediction technique generates price projections that are relatively accurate for the out-of-sample measurement period that spans from 4 May 2022 to 17 April 2024. Investors and governments are provided with the knowledge they need to make informed decisions on the gold market due to the availability of models that anticipate prices.

Integrating novel stellarator single-stage optimization algorithms to design the Columbia stellarator experiment

Abstract The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus (CNT) experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail.

Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm

The construction of a predictive model that accurately reflects the spontaneous combustion temperature of coal in goaf is fundamental to monitoring and early warning systems for thermodynamic disasters, including coal spontaneous combustion and gas explosions. In this paper, on the basis of programming temperature experiment and industrial analysis, 381 data sets of 9 coal types are established, and feature selection was executed through the utilization of the Pearson correlation coefficient, ultimately identifying O2, CO, CO2, C2H4, C3H8, C3H8/CH4, C2H4/CH4, C2H4/C3H8, CO2/CO, and CO/O2 as input indicators for the prediction model. The chosen indicator data were divided into training and testing sets in a 4:1 ratio, the Particle Swarm Optimization (PSO) methodology was applied to optimize the parameters of the XGBoost regressor, and a universal PSO-XGBoost prediction model is proposed. A tenfold cross-validation method was employed to assess performance of PSO-XGBoost, PSO-RF, PSO-SVR, XGBoost, RF, and SVR models separately, the results underscored the superior predictive accuracy, robustness, fault tolerance, and universality of the PSO-XGBoost model.

A taxonomy of literature reviews and experimental study of deepreinforcement learning in portfolio management

Portfolio management involves choosing and actively overseeing various investment assets to meet an investor’s long-term financial goals, considering their risk tolerance and desired return potential. Traditional methods, like mean–variance analysis, often lack the flexibility needed to navigate the complexities of today’s financial markets. Recently, Deep Reinforcement Learning (DRL) has emerged as a promising approach, enabling continuous adjustments to investment strategies based on market feedback without explicit price predictions. This paper presents a comprehensive literature review of DRL applications in portfolio management, aimed at finance researchers, data scientists, AI experts, FinTech engineers, and students seeking advanced portfolio optimization methodologies. We also conducted an experimental study to evaluate five DRL algorithms—Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed DDPG (TD3)—in managing a portfolio of 30 Dow Jones Industrial Average (DJIA) stocks. Their performance is compared with the DJIA index and traditional strategies, demonstrating DRL’s potential to improve portfolio outcomes while effectively managing risk.

Efficient Frontier and Applications in Product Offering and Pricing

Problem definition: The joint assortment and pricing problem is notoriously difficult to solve, especially for large-scale issues subject to various operational constraints arising from business practices. In this paper, we delve into the joint optimization challenge under constrained logit choice models, accounting for product-differentiated price sensitivities across static, randomized, and dynamic contexts. Methodology/results: We develop a unified optimization methodology that applies efficient frontier and dimensional reduction and transforms the joint optimization problem into a single-variable one with respect to the total choice probability. The efficient frontier is employed to transform the nonconcave objective function equivalently into its concave counterpart. We show that the optimal prices are uniquely determined by the common target adjusted markup. The complexity of the mixed combinatorial optimization problem can be reduced by searching over efficient sets of polynomial size. In the randomized assortment and pricing problem in which the product offer sets and prices follow certain distribution, we show that randomization has the potential to elevate the total revenue to the exact efficient frontier, a feat that the static problem may fail to achieve (with a given total choice probability). For dynamic joint product selection and pricing, we find that the optimal policy adopts a simple time-threshold structure that can be precomputed. Furthermore, we illustrate the robustness of our methodology by extending the analysis to a variety of general settings, including the nested logit model. Managerial implications: The proposed methodology contributes to the increasingly popular topic in retail management by significantly reducing the computational complexity of joint product offering and pricing under different constraints. Our results regarding the effects of product set constraints and price bounds provide valuable insights and guidance to practitioners on product offering and pricing in various business scenarios. Funding: C. Ke acknowledges financial support from the National Natural Science Foundation of China [Grant 72101113]. L. Lu acknowledges financial support from the Hong Kong Research Grants Council [Grants 26501021 and 16502423]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0164 .

Enhanced Diagnosis of Skin Cancer from Dermoscopic Images Using Alignment Optimized Convolutional Neural Networks and Grey Wolf Optimization

Skin cancer (SC) is a highly serious kind of cancer that, if not addressed swiftly, might result in the patient’s demise. Early detection of this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an effective and efficient tool for SC detection (SCD). Several DL-based algorithms for automated SCD have been reported. However, more efficient models are needed to improve accuracy. As a result, this paper introduces a new strategy for SCD based on Grey Wolf optimization (GWO) methodologies and CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, and classification. The proposed method utilizes a Convolutional Neural Network (CNN) to extract features from Regions of Interest (ROIs). CNN is employed for feature categorization, whereas the GWO approach enhances accuracy by refining edge detection and segmentation. This technique utilizes a probabilistic model to accelerate the convergence of the GWO algorithm. Employing the GWO model to optimize the structure and weight vectors of CNNs can enhance diagnostic accuracy by a minimum of 5%, based on evaluation outcomes. The application of the proposed strategy and its performance comparison with other methods indicate that the proposed method with GWO predicted SC with an average accuracy of 95.11% and without GWO an Accuracy of 92.66%, respectively, enhancing accuracy by a minimum of 2.5% when we train our model with GWO.

2H NMR as a Practical Tool for Following MOF Formation: A Case Study of UiO-66.

Developing the mechanism for MOF formation is crucial for the rapid development of new materials. This work demonstrates that Deuterium NMR spectroscopy is the optimal inter-laboratory methodology for understanding the in situ kinetics of metal-organic framework (MOF) formation. This method is facile, affordable, and allows for the isolation and monitoring of individual reagents by using one deuterated component while the remaining components are protonated. This study utilizes 2H NMR, via the spectrometer's lock channel, to investigate the formation of UiO-66 as influenced by different modulators: acetic acid, benzoic acid, and hydrochloric acid. By monitoring the concentration of the deuterated linker and observing the chemical shift and peak width of deuterated water over time, key elements of the mechanism are unraveled. Paradoxically, conditions that cause the ligand to be consumed more slowly result in MOFs forming more quickly and with fewer defects. This phenomenon is attributed to the dissociative mechanism associated with the Zr(IV)-containing node.

Optimizing global risk-conscious portfolios: the strategic role of Sharia-compliant and ESG investments

Purpose This study aims to explore the strategic integration of Sharia-compliant and environmental, social and governance (ESG)-focused investments within global equity portfolio optimization frameworks, with a particular emphasis on variance minimization and dynamic rebalancing techniques. Design/methodology/approach The research uses historical data from Sharia-compliant, ESG-focused and conventional equity exchange-traded funds (ETFs). Advanced mean-variance optimization methodologies via quadratic programming are employed, encompassing static optimization with and without a 50% cap on individual asset weights, dynamic optimization with monthly rebalancing and rolling window optimization. Findings Portfolios integrating Sharia-compliant investments frequently outperform those composed solely of conventional equity ETFs. Dynamic optimization with monthly rebalancing achieved the highest Sharpe ratio (1.3708) and demonstrated enhanced portfolio resilience during market turbulence, such as the COVID-19 pandemic. Sharia-compliant investments showed substantial allocations during key periods, with weights reaching up to 100% in the first half of 2020. In contrast, ESG-focused investments exhibited more limited and sporadic allocations, reflecting a more opportunistic role in the portfolio. Practical implications The findings reaffirm the critical role of Sharia-compliant investments in well-diversified, risk-conscious portfolios while also providing nuanced insights into the more selective integration of ESG-focused assets. The results offer practical guidance for portfolio managers seeking to integrate ethical and sustainable investment principles within advanced portfolio optimization frameworks, particularly when focusing on minimizing variance and dynamically responding to evolving market conditions. Social implications The study contributes to the growing body of literature on ethical and sustainable investments, demonstrating that it is possible to balance ethical considerations with robust financial performance. The research underscores the potential for Sharia-compliant investments to play a significant role in global portfolios, potentially fostering greater financial inclusion and cross-cultural understanding in the investment community. Originality/value This research provides novel insights by focusing on Sharia-compliant investments within non-Muslim countries, an area that has been relatively underexplored. It also compares the outcomes of static, dynamic and rolling optimizations, highlighting the dynamic interplay between ethical investment principles and financial performance.

Advanced control parameter optimization in DC motors and liquid level systems

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO’s superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.

Optimizing the Construction and Activation of 3D-Printed Electrochemical Sensors: An Experimental Design Approach for Simultaneous Electroanalysis of Paracetamol and Caffeine.

This work presents an optimization of the construction, treatment, and activation of 3D-printed electrochemical sensors (E-3D). For this, was used a 23-full factorial design examining three key variables at two levels: electrode height, electrode diameter, and printing speed. Moreover, it evaluates various physical, chemical, and electrochemical methods to treat and activate the E-3D surface. The techniques of electrochemical impedance spectroscopy and cyclic voltammetry (CV) shows that the sequential physical, chemical, and electrochemical treatments lead to the highest treatment efficiency and activation. Raman spectroscopy and atomic force microscopy characterize untreated and treated E-3D sensor surfaces. The optimal treatment and activation methodology was applied to the electroanalysis of paracetamol (PAR) and caffeine (CAF) simultaneously using CV and differential pulse anodic stripping voltammetry (DPASV). DPASV measurements reveal limits of detection of 0.44 and 0.58 μmol L-1 in a 0.5 mol L-1 H2SO4 medium for PAR and CAF, respectively, with the treated and activated E-3D sensor. The principal achievement of this work was emphasizing the critical role of surface treatment and activation in enhancing the performance of the developed electrodes, thereby advancing technological applications of 3D-printed electrochemical sensors.

Waste PET bottle-derived carbon for defluorination of fluoride-polluted water

ABSTRACT This study synthesises expanded graphite (EG) from graphitised carbon from waste polyethylene terephthalate (PET) bottles. The adsorbent material was characterised using FTIR, XRF, XRD, SEM, Raman Spectroscopy, and BET surface area analysis. The synthesised EG defluorinated wastewater, utilising response surface methodology (RSM) for experimental design and optimisation. XRD patterns confirmed the successful synthesis of graphite and EG, demonstrating structural modifications. Raman spectra indicated higher crystalline order in EG, with D and G band shifts and an increased ID/IG intensity ratio from 0.89–1.04. BET analysis revealed a specific surface area of 247.1 m²/g. . FTIR analysis showed abundant functional groups, particularly hydroxyl (-OH) and alkene (C = C). Batch adsorption experiments revealed that fluoride adsorption onto EG depended on pH, time, and initial fluoride concentration. Optimal conditions for fluoride removal, determined using RSM with central composite design (CCD), demonstrated a maximum fluoride removal rate of 97%. Isotherm data fitted both Langmuir and Freundlich model, and kinetics data aligned well with the pseudo-first-order model. ANOVA showed significant effects of contact time, pH, adsorbent dose, and initial fluoride concentration on removal efficiency. The model's R² value of 0.98 and lack of fit value of 0.1554 confirmed the quality of the second-order polynomial model. Optimal conditions for maximum fluoride removal efficiency of 97% were validated at 5 mg/L fluoride concentration, pH 4, adsorbent dose of 5 g/L, and a contact time of 30 min. Therefore, the present study demonstrated efficient fluoride-polluted water treatment using waste-derived EG.

Information model of the general methodology as a system for building any activity

The research offers an information model of the general methodology as a system for building any activity. Modeling has long been an effective tool in building various processes. And since almost all processes have an information basis, information models are widely used, which are often brought to algorithmic and computer implementation. Computer technologies allow not only to quickly calculate process parameters and expected results, but also to visually display the course of the process itself with varying degrees of visualization. The greatest difficulty is modeling social processes, as they are directly related to the human factor, mostly due to the unpredictability of behavior and decisions made, often contrary to conventional logic. This is due to the lack of study of the spiritual sphere of man, his consciousness. The authors rely on the following definitions: methodology is a system of knowledge about the structure, effective organization, principles, methods and means of conscious activity; knowledge is a feature of a certain amount of information that determines its status and separates it from all other information by the criterion of its ability to solve the task; beliefs - generalizations that a person has made about cause-and-effect relationships, values, about himself/herself, other people, actions, identity, etc., what he/she considers “true” at the moment, including the conscious needs of the individual that motivate him/her to act in accordance with the accepted value orientations. The methodology as a system of activity involves the following components: knowledge - elements of the system (worldview, values, goals, principles, methods, ways and forms); human potential (according to their personal qualities and abilities, people form an organizational and functional structure and the corresponding hierarchy); interaction organized in a certain structure (management, technology, provision, execution, control); results obtained (indicators, criteria, evaluation) and feedback. Thus, the model covers all the components of any activity and allows determining the optimal methodology for building the entire process. The model is based on the quality of knowledge, which is an element of the methodological system and allows to build any activity with a high degree of adequacy and efficiency.

Application of response surface methodology (RSM) for experimental optimization in biogenic silica extraction from rice husk and straw ash

This work aimed to extract silica from combination of rice husk (RH and Rice straw (RS) by optimizing the ash digesting process parameters with the aid of response surface methodology (RSM). The effects of three independent ash digestion process factors like sodium hydroxide concentration (1–3 M), temperature (60–120 °C) and time (1–3 h), for silica production from the mixture of rice husk (RH) and rice straw (RS) were studied. A quadratic model was used to correlate the interaction effects of the independent variables for maximum silica production at the optimum process parameters by employing central composite design (CCD) with RSM. The work indicates that the temperature is the most significant parameter among the model terms, followed by NaOH concentration, then time for digestion of ash for silica production. It may be because of the larger F-value for temperature, which influences to high extent of ash digestion for silica production. The proximate analysis discovered that RH/RS possessed compositions of high ash, volatile matter and fixed carbon content whereas low moisture content, i.e., 20.5 ± 0.46, 67.1 ± 0.78, 15.8 ± 0.35 and, 6.6 ± 0.37 wt.%, correspondingly. The characterization of RH/RS and silica were performed by employing thermogravimetric (TG) and differential thermal (DT), Fourier transforms infrared spectroscopy (FTIR), X-ray fluorescence spectroscopy (XRF) and Brunner–Emmet–Teller (BET) for confirmation of the silica production. Hence, this current study concludes that silica materials obtained from RH/RS (with purity > 97.35 wt.%) may be new materials in the Si–C–O system that may be used in construction, ceramics and silica gel that helps to absorb moisture.

Measuring the impact and contribution of industrial and agricultural output and tax revenues on Libya’s GDP during the period (1990-2020)

The Libyan economy is a rentier economy dominated by oil output as the main source of GDP, which undoubtedly represents a fundamental and pivotal flaw in this economy, making it more volatile and less capable of achieving economic stability due to the oil sector's lack of adherence to internal economic control. From this perspective, the study aimed to measure the contribution of both the manufacturing and agricultural sectors, in addition to tax revenues, and to identify the strength and direction of the impact of these outputs on the Libyan GDP. To achieve this goal, the researchers relied on the inductive approach and the standard method, which, after conducting stability tests, indicated that the optimal methodology is the autoregressive distributed lag model. The results indicated a statistically significant positive impact of both agricultural and industrial outputs on the dependent variable represented by gross national output, with their elasticity being (0.66 and 0.34) respectively, while the impact of tax revenues was negative and significant, with its elasticity estimated at about (0.41). One of the main recommendations of the study is the necessity to adopt plans and programs for the development and integration of the industrial and agricultural sectors to achieve higher contribution rates to the formation of GDP. Keywords: GDP, industrial output, agricultural output, tax revenues, Libyan economy.