Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems.

A well-known traditional GARCH model assumes normal innovations that do not satisfactorily model sudden variations usually caused by economic tremors or disturbances. This has necessitated the need to develop non-linear, distributional and robust models. In this study, a new set of GARCH models with smooth transition non-linearities and novel innovation distributions are developed to improve the modeling and forecasting of stock returns volatilities in the Nigeria /US stock markets’ Daily data on Heating Oil, Crude Oil, and Gasoline regular spot prices (Naira/US per Dollar) from 1985 to 2025 were obtained from the U.S. Energy Information Administration (EIA) website (https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm). This study was carried out using copula-based regime switching GARCH Generalized Error distribution (GED) model and a hidden Markov model. The copula switching GARCH (CoS GARCH) framework showed that the spot prices of crude oil, heating oil and gasoline demonstrated distinct patterns of volatility clustering and distributions with heavy and notable interdependence among different regimes. The estimated transition probability matrix indicated that the Markov chain associated with the volatility states displayed significant persistence. The equations for the conditional means indicated that returns were marginally different across the regimes, which aligns with the established observation that energy price returns have small means compared with their variances. The findings of the study therefore established the presence of heavy tails, clustering of volatility, structural changes, and significant interdependence in energy markets.

A well-known traditional GARCH model assumes normal innovations which do not adequately capture sudden variations typically caused by economic shocks or disturbances. This has necessitated the need to develop non-linear, distributional and robust models. In this study, a new set of GARCH models with smooth transition non-linearities and novel innovation distributions are developed to improve the modeling and forecasting of stock returns volatilities in the Nigeria /US stock markets’ Daily data on Heating Oil, Crude Oil, and Gasoline regular spot prices (Naira/US per Dollar) from 1985 to 2025 were obtained from the U.S. Energy Information Administration (EIA) website (https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm). This study was carried out using copula-based regime switching GARCH Generalized Error distribution (GED) model and a hidden Markov model. The copula switching GARCH (CoS GARCH) framework showed that the spot prices of crude oil, heating oil and gasoline demonstrated distinct patterns of volatility clustering and distributions with heavy and notable interdependence among different regimes. The estimated transition probability matrix indicated that the Markov chain associated with the volatility states displayed significant persistence. The equations for the conditional means indicated that returns were marginally different across the regimes, which aligns with the established observation that energy price returns have small means compared with their variances. The findings of the study therefore established the presence of heavy tails, clustering of volatility, structural changes, and significant interdependence in energy markets.

Indonesia faces increasing pressure on its natural forest resources due to deforestation and rising demand for wood, encouraging the utilization of fast-growing species such as sengon (Falcataria moluccana) and underutilized invasive species such as African tulip (Spathodea campanulata). However, both species are characterized by naturally light surface colors, which are often perceived as less attractive for decorative and interior applications. Oil heat treatment (OHT) has been used to modify wood color and enhance visual appearance. This study evaluated the effects of OHT at 160°C, 180°C, 200°C, and 220°C for 2 hours on color changes and public color preferences of sengon and African tulip woods. Color changes were visually observed and quantitatively analyzed using the CIE-LAB color system, while preference data were collected through an online survey involving male and female respondents. The results showed that OHT caused significant darkening of wood surfaces, with African tulipwood exhibiting greater color changes than sengon. Preference analysis indicated that female respondents tended to favor wood colors heat-treated at moderate temperatures (180°C), whereas male respondents preferred darker colors produced at higher temperatures (200–220°C). For African tulip wood, treatment at 200°C was the most preferred overall by both genders. These findings demonstrate that oil heat treatment effectively modifies wood color and that consumer color preferences vary by gender, which may be considered in visually oriented wood applications.

Purpose This paper aims to study the relationships between a set of energy commodities (carbon emissions, Brent, heating oil and natural gas) and the geopolitical risk (GPR) indices of each of the G7 countries (USA, Japan, Germany, United Kingdom, France, Italy and Canada), before and after the beginning of the recent armed conflicts in Russia and Israel. Design/methodology/approach The authors applied a time-varying parameter-vector autoregression methodology, using the monthly GPR index data and the monthly commodities returns, which covers the period between July 2019 and August 2024. This time frame includes the events of the Russian–Ukrainian War and the Israel–Palestinian War, allowing this study to have two periods: one before the wars started and another after their beginning. Findings The authors concluded that natural gas is a transmitter of shocks in both periods to GPR_JP and GPR_CAN after the wars began. The other commodities in the prewar period were essentially net receivers of shocks. After the wars started, they underwent a complete transformation in their behavior related to the GPR indexes and became transmitters of shocks. Originality/value This significant shift in behavior is a key finding of the research, providing a new perspective on the dynamics between GPR and energy commodities. Practical and social implications are discussed from the perspective of business sustainability, thereby offering a novel contribution to the literature, while also analyzing recent crisis episodes using an appropriate technique.

Abstract Laboratories are major contributors to institutional carbon emissions due to their high energy and material demands. This study presents the first carbon impact assessment of daily laboratory heating practices, specifically evaluating the energy efficiency and lifecycle emissions of common lab heating methods—oil baths, bead baths, and heating blocks—used to heat water to 80 °C. Each method was evaluated over a cradle-to-grave lifecycle, including manufacturing, 2400 use cycles, and end-of-life scenarios (disposal or recycling), with and without foil insulation. Global warming potential (GWP) was calculated using Simapro and Ecoinvent, applying the IPCC 2021 GWP100 V1.03 method. Foil insulation reduced energy use by up to 66%, significantly lowering operational GWP. However, the embodied carbon of foil was substantial when treated as hazardous waste. The carbon impact was significantly reduced when foil was reused at least 4–10 times or recycled at the end of life, highlighting the importance of material reuse and sustainable end-of-life strategies. Among the tested methods, oil baths consistently exhibited the lowest carbon impact in most scenarios. Sensitivity analysis, presented as a calculator tool, showed a dependence on the reaction time, material lifetime, and block weight. These findings underscore the importance of energy-efficient setups, material reuse, and recycling in promoting sustainable labs and responsible consumption, aligning with SDG 12: Responsible Consumption and Production.

Tailoring corrosion resistance and wettability of AZ31 Mg alloy via laser, hot water, and silicone oil heat treatments

The relationship between the heat flow, the quantity and quality of oil in the Pripyat Trough and the geodynamics of its development is investigated. It has been established that the overwhelming amount of oil deposits and its high quality are confined to the Northern Structural District, within which the maximum heat flow is recorded in the area of the Pripyat paleorift. It is shown that the high heat flow in this area is caused, firstly, by increased convective heat and mass transfer in the crustal segment of the detachment (main extention fault) of the Pripyat Trough located here. Secondly, by conductive cooling of the effusive rocks of the cover and consolidated crust, containing a large volume of intrusive bodies of Late Devonian magmatism. A significant manifestation of magmatism in the northeastern part of the trough was due to active plume-tectonic processes generated by the head part of the West Dnieper rift pillow of the Dnieper graben during the period of maximum stretching of the lithosphere. Tectonically, this led to the formation of a deep Pripyat-Dnieper sedimentary paleostrait here. Pripyat riftogenesis developed due to plastic stretching of the lower crust with simultaneous thinning of its upper and middle crust as a result of brittle stretching. The strong resistance to stretching of the lower acid crust of the Korosten pluton, which underlies the Southern structural region, and the increased plasticity of the basic-ultrabasic lower crust of the Central and Northern structural regions caused the asymmetric development of the Pripyat rift with the formation of a tension neck, a listric fault system, and a translithospheric detachment.The upper mantle segment of the detachment zone and its branches in the crust of the Northern structural region in the form of listric faults served as the main channels of intensive heat and mass transfer and migration of mantle hydrocarbon-containing fluids into the sedimentary cover, favoring the formation of high heat flow and oil deposits here.

This work presents a polarization-maintaining K-type side-hole fiber (SHF) optimized for simultaneous distributed measurements of hydrostatic pressure and temperature using optical frequency-domain reflectometry (OFDR) based on Rayleigh backscattering. The specific orientation of the elliptical core relative to the pair of side holes ensures record-high differential pressure sensitivity between the two orthogonal polarization modes (-1.9 GHz/MPa and 0.7 GHz/MPa), while maintaining nearly identical temperature responses. The fiber's design prevents pressure-induced birefringence compensation, thus preserving stable polarization separation over the entire pressure range. A two-channel polynomial model was applied to decouple the effects of temperature and pressure on the spectral shift. Simultaneous measurements of both parameters were demonstrated for the first time in a distributed Rayleigh-based configuration, achieving an accuracy close to 0.1 ° and 0.1 MPa. The proposed sensing concept combines high-pressure sensitivity, thermal stability, and strong polarization selectivity, enabling reliable operation under varying thermo-mechanical conditions. These findings pave the way for advanced multiparameter distributed fiber-optic sensors applicable in energy-storage systems, pulsating heat pipes, oil and gas exploration, and structural health monitoring of pressure-critical components.

Nowadays, renewable energy sources are gaining importance, yet global energy demand is primarily met by burning fossil fuels. Fluctuations in fossil fuel availability, driven by geopolitical tensions, supply–demand changes, and natural disasters, can lead to sudden energy price spikes or supply shortages, adversely affecting the global economy. Despite its negative impact on carbon emissions and climate change, Heating Oil (HO) offers advantages over other fossil fuels in efficiency, reliability, and availability. Accurate time series prediction models for HO are crucial for stakeholders. This study proposes a novel hybrid model, integrating the Chaotic Adaptive Fitness-Distance Balance-based Stochastic Fractal Search (AFDB-SFS) algorithm with a Bidirectional Long-Short Term Memory (Bi-LSTM) network, for HO close price prediction. The dataset comprises daily observations of five financial time series (close, open, high, low, and volume) over 4260 trading days, yielding a total of 21,300 data points (4260 days × 5 variables). During the feature extraction stage, financial signal processing methods such as Demand Concentration Curve (DCC) and traditional technical indicators are utilized. A total of 189 features are extracted at appropriate intervals for each indicator. Due to the large number of features, the AFDB-SFS algorithm then efficiently identifies the most compatible feature subsets, optimizing the Bi-LSTM model based on three criteria: maximizing R2, minimizing RMSE, and minimizing feature count. Experimental results demonstrate the proposed hybrid model’s superior performance, achieving high accuracy (R2 of 0.9959 and RMSE of 0.0364), outperforming contemporary models in the literature. Furthermore, the model is successfully implemented on the Jetson Orin Nano Developer Platform, enabling real-time, high-frequency HO price predictions with ultra-low latency (1.01 ms for Bi-LSTM), showcasing its practical utility for edge computing applications in commodity markets.

Per-and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals extensively utilized in various industrial and consumer applications, owing to their distinctive characteristics such as heat resistance, oil and water repellency, and chemical stability. PFAS are commonly called "forever chemicals" as they exhibit extraordinary persistence in the environment, leading to their accumulation in soil, water, air, wildlife, and human tissues. As a result, there has been a recent surge of interest in the development of effective degradation methods to address this environmental challenge. PFAS can be classified based on their terminal functional groups (Head-groups) with carboxylic and sulfonic head groups being the most common. In this study we employ a theoretical approach to study the PFAS degradation mechanism due to the application of ultrasound and the rate law governing the degradation of various PFAS compounds, including Perfluorooctane sulfonate (PFOS), Perfluorooctanoic acid (PFOA) using Reactive Molecular Dynamics (ReaxFF) simulation. The initial steps of quantum level understanding of the reaction mechanism of PFAS molecules were performed using Perflurobutanoic acid (PFBA) as a model molecule using Density Functional Theory (DFT). We studied two mechanisms of PFAS molecular destruction via ultrasound (1) head-tail bond breaking and (2) tail-tail bond breaking mechanism, thereby addressing gaps in the current understanding of reaction pathways and degradation rates associated with PFAS compounds.

THE RELEVANCE. The issues of efficient use of fuel and energy resources in the Russian industry remain extremely important, which is confirmed by the adoption of a number of legislative and regulatory documents at the federal and regional levels. Historically, the structure of energy complexes of enterprises, including production using oil systems, was formed in conditions of low energy prices, which led to insufficient energy efficiency of technological processes. In this regard, the modernization of existing components, in particular, oil heating systems, using modern methods of technological modeling, becomes an urgent task. THE PURPOSE. The study of oil heating unit in order to optimize its thermal regime, reduce energy losses and develop measures to improve energy efficiency using technological modeling tools is the purpose of this study. METHODS. To achieve the set objectives the following methods were used: system analysis of thermal and technological processes, mathematical and computer modeling of heat exchange in the oil heating unit, methods of energy-technological combination to identify energy saving reserves. RESULTS. Within the framework of the research there were carried out: analysis of heat losses in the oil heating unit, modeling of heat flows taking into account changes in viscosity and heat capacity of oil, evaluation of efficiency of heat exchange equipment and identification of “bottlenecks”. Proposed solutions: introduction of an additional heat exchanger for waste gas heat recovery, optimization of heating modes by means of automation of temperature parameters control, use of recuperative schemes to increase system efficiency. CONCLUSION. Implementation of the proposed measures will result in savings of up to 6.55 million rubles per year. Application of technological modeling tools in modernization of oil heating unit allows to optimize thermal processes, reduce energy losses and increase economic efficiency of production. Implementation of the proposed solutions will provide significant energy savings with a relatively short payback period. The implementation of this project will contribute to the digital transformation of heat transfer processes and energy efficiency in the petrochemical industry through the application of artificial intelligence and machine learning technologies. This corresponds to the key directions of the Strategy for Scientific and Technological Development of the Russian Federation, including the transition to intelligent production systems, big data processing and the introduction of automated control methods. Thus, the proposed approach opens up new opportunities for the digitalization of petrochemical industries, increasing their efficiency, environmental friendliness and competitiveness in accordance with the priorities of scientific and technological development of the Russian Federation.

RELEVANCE. Recently, power transformers with mineral biodegradable insulating oil have been increasingly used, since it is environmentally friendly and biodegradable. Power transformers are the main and extremely important equipment in electric power systems. METHODS. Diagnostic methods are of particular importance for the stable operation of the system. Gas analysis (GA) of mineral oil has its own diagnostic methods that cannot be directly applied to other oils. RESULTS. The relationship between gas evolution and destructive processes occurring in mineral oil is considered. In this study, tests were carried out for local heating, partial discharge and arc discharge in oil to study gas formation under these conditions. Based on the results obtained, gas formation mechanisms were discussed depending on the heating temperature, discharge energy and other factors. CONCLUSION. The methods proposed in this paper, based on experimental data, are important, since they indicate promising areas for diagnosing mineral oils. The accuracy of these methods can be improved by optimization using data from operating transformers.

Nature-inspired surfaces with hybrid wettability hold significant promise for water harvesting, dropwise condensation, and biomedical liquid arrays. However, current fabrication methods are hampered by restricted pattern complexity, reliance on toxic fluorides, mask alignment inaccuracies, and poor scalability. Here, we introduce a fluorine- and mask-free, implant-grade process to create superhydrophilic-superhydrophobic (SHPi-SHPo) patterns on titanium via sequential laser machining, silicone oil heat treatment, and ultraviolet (UV) irradiation treatment. Through parametric optimization of laser parameters and UV exposure, we establish ideal fabrication conditions for achieving micron-scale accuracy, enhanced stability in SHPi micropatterns, and long-term durability of the SHPo substrate. The underlying mechanisms governing wettability transitions and stability were elucidated through surface morphology and surface chemistry analyses. Additionally, the SHPi regions within hybrid architectures exhibit switching between extreme wettability states (SHPi and SHPo) via UV irradiation and thermal annealing cycles while maintaining adjacent SHPo regions' integrity without cross-contamination. Moreover, additional silicone oil heat treatment fully erases prior patterns and enables micron-scale rewriting of arbitrary designs. This scalable, eco-friendly fabrication strategy opens new avenues for dynamic fluid management, efficient heat transfer, and reconfigurable biomedical interfaces.

This study investigated the hydrophobic and mechanical enhancement of silica sands treated with tung oil, a natural hydrophobic stabilizer, for road embankment applications. Through laboratory testing including apparent contact angle (ACA) and water drop penetration time (WDPT) measurements, uniaxial compression tests, and direct shear box tests, the effects of mean particle size (0.24–0.99 mm), tung oil concentration (0.01–5%), and heating duration (1–14 days at 60°C) were quantified. Key findings revealed: (1) peak hydrophobicity (ACA=95.1-128.7°, WDPT>3600 s) occurred at 0.05% tung oil concentration, beyond which a reduced water repellency was observed; (2) both peak and near-constant-volume shear strength, along with their corresponding parameters (friction angle and cohesion), increased with tung oil concentration and heating duration but decreased with larger mean particle size, due to the decreased interparticle bonding demonstrated by scanning electron microscopy (SEM) analysis; (3) stabilization efficiency showed a similar trend to shear strength but decreased with higher normal stresses. The results demonstrated tung oil’s potential dual functionality for cost-effective, zone-specific subgrade treatment, with low concentrations (0.05%) serving as impervious barriers and higher concentrations (3%) forming bonded load-bearing layers.

This study pioneers the application of regularized vector autoregressive (VAR) models in return forecasting research and explores their effectiveness in predicting the spot and futures returns of crude oil and heating oil. Our findings demonstrate the efficacy of regularized VAR models, especially the superior predictive performance of the VAR-elastic model with a small lag order and the VARX-elastic model (the model that incorporates predictors as exogenous variables into the VAR-elastic framework) with a large lag order, in predicting the futures and spot returns of crude oil and heating oil. Furthermore, the predictive power of the regularized VAR models for crude oil and heating oil returns is primarily concentrated during periods of economic recessions. Finally, mean-variance investors operating within crude oil and heating oil markets can achieve considerable utility gains by employing regularized VAR models, particularly the VAR-elastic model with a small lag order and the VARX-elastic model with a large lag order.

Deep-sea residual oil has been recognized as a significant environmental hazard due to its poor low-temperature fluidity and complex recovery processes necessitating urgent solutions. A segmented regulated two stage heating transport method is proposed, with a high-precision thermo-fluid coupling model that integrates thermophysical properties to characterize oil thermal coupling characteristics. Dimensional analysis further identifies multi-properties correlations for convective heat transfer coefficient and oil temperature that govern the thermo-fluid dynamics interaction. Numerical simulations reveal that transport distance, heat flux density, and mass flux collectively regulate convective heat transfer efficiency and temperature distribution. It is shown that oil temperatures reduce oil viscosity, thereby enhancing convective heat transfer between the thermal boundary layer oil and the pipe wall; as transport distance increases, accumulated heat and thermophysical properties stabilize simultaneously. A dual-properties regulation mechanism is identified between heat flux density and mass flux: within 180–540 kg/(m2·s), outlet thermal boundary layer oil temperature linearly responds to heat flux density (K = 0.04); exceeding 720 kg/(m2·s), temperature regulation efficiency decreases by 30%, accompanied by a downstream shift of temperature peak region under high mass flux conditions. This method provides reliable theoretical foundations for deep-sea low-temperature residual oil recovery.

As more and more technology advancement of transportation and electrification of heating system, residents nowadays usually had the less demand for using the fossil fuel which causes the carbon emission. For instance, most peoples vehicles had converted from ICE (internal combustion engine) vehicles to the BEVs (Battery electric vehicles). Another example, domestic heating pumps, use electricity instead of using fossil fuel (natural gas, propane, heating oil, charcoal etc.) nowadays to transfer heat from a cool space to a warm space and reverse this process if in summer. Electrification refers to the source of generating the electricity and their usage are renewable such as photovoltaic, wind electricity, hydrogen-powered electricity etc. These source of electricity can supply the electric vehicles and the electric heat pumps sufficiently. However, the cost of source of generating the electricity and technology impede the electrification. For materials perspective, like battery, electromotor, conductors & insulators, heat exchange, and refrigeration materials. These materials can determine the efficiency, energy density and the life span. Nevertheless, these materials are also limited and difficult to manufacture. Thus, this paper will provide the clear and executable ideas for the strategies and material selection of small cars and heat pump renovations in electrification and explain the limitation in mechanism perspective.

Energy efficiency is a critical factor in the transition toward sustainable energy systems and the decarbonization of industrial processes. In this context, the recovery of residual process energy represents a key strategy. This study presents a case analysis of a Brazilian carbo-chemical plant, where calcination furnaces release exhaust gases containing both thermal and chemical energy. These gases, generated by six furnaces, have a total flow rate of 1.36 kg/s at 800 °C and a volumetric composition of 26% H 2 , 4.2% CH 4 , and 5% CO, among other components, resulting in a total energy potential of 8.30 MW—comprising 1.63 MW of thermal and 6.67 MW of chemical energy. The main objective of this study is to assess the potential for recovering this energy through various cogeneration system configurations based on steam cycles, aimed at process thermal oil heating and electricity generation. Simulations were conducted using IPSEpro 8.0, and system performance was evaluated according to the First and Second Laws of Thermodynamics to identify opportunities for optimization. The results show that, in addition to providing 70 kW of useful heat for oil heating, the system can deliver up to 2.65 MW of electrical power. The energy and exergy efficiencies of the steam cycles reach 43.35% and 80.45%, respectively, while the overall system achieves energy and exergy efficiencies of 32.8% and 32.03%. Exergy analysis highlights areas for improvement, particularly in combustion and heat exchange, due to high irreversibilities in combustion chambers and boilers (up to 821.50 kW and 3384.29 kW, respectively) and recoverable heat present in boiler exhaust gases. Environmental analysis indicates a significant reduction in stack gas temperatures (66%–77% relative to the initial 800 °C) and the combustion of residual fuel components, especially CH 4 , which markedly decreases thermal and chemical pollution. Quantitatively, electricity generation reduces grid dependency, preventing up to 3234 tons of CO 2 emissions per year. These findings demonstrate a considerable theoretical estimable potential for residual energy recovery, yielding substantial improvements in efficiency and environmental impact mitigation. Furthermore, an optimized technological approach could achieve energy efficiencies of up to 50%, producing 40% more electricity. These results highlight the importance of further studies, particularly to evaluate economic feasibility and potential integration into carbon markets.

Guava pickle, a widely consumed condiment in various cuisines, was formulated and developed to evaluate its nutritional and quality attributes. The pickle was prepared using ripe guavas (Psidium guajava L.) and a blend of spices, including Capsicum annuum (red chilli powder), Curcuma longa (turmeric powder), sodium chloride (salt), Ferula asafoetida (asafoetida), Brassica juncea (mustard seeds), and various seasonings. The guava pickle exhibits a complex flavour profile, characterized by a harmonious balance of sweet, tangy, and spicy notes. This multifaceted flavour experience is attributed to the synergistic interaction of various biochemical compounds present in the guava fruit, spices, and seasonings. Sensory evaluation using a 9-point hedonic scale indicated that the third trial of guava pickle was the most acceptable. The effects of various processing steps on the nutritional and sensory properties of the pickle were evaluated. The optimized process involved the selection of guavas, cutting, salting, and turmeric powder application, followed by drying, oil heating, spice addition, cooling, and mixing. The pickle was preserved in sunflower oil and stored in a cool, dark place. Proximate analysis revealed a protein content of 3.2g, fat content of 21.75%, moisture content of 51.92%, and ash content of 15.65%.