Environmental Conditions Research Articles

Study on Infrared (IR) Camouflage Using Mylar in the Defense Sector

Abstract: This project investigates the feasibility of utilizing Mylar, a highly reflective polyester film, for infrared (IR) camouflage in the defense sector. Through a comprehensive analysis of Mylar's optical properties, including its reflectivity and reflectance, this study aims to assess the material's potential for reducing thermal signatures and enhancing stealth capabilities of military assets. The research involves theoretical and numerical analysis to evaluate Mylar's performance under various environmental conditions, such as temperature extremes, humidity, and exposure to ultraviolet (UV) radiation. Furthermore, the project explores the integration of Mylar-based IR camouflage into existing defense systems, including vehicle coatings, personnel gear, and tactical equipment. Recommendations for the deployment and optimization of Mylar-based camouflage solutions are provided, addressing factors such as material selection, camouflage design, compatibility with sensor systems, and logistical support. The findings demonstrate Mylar's efficacy in decreasing IR detectability, providing a cost-effective and versatile solution in IR camouflage. This research contributes significantly to the field of defense technology, offering practical insights and paving the way for further advancements in stealth and counter-detection measures in military applications.

Анализ работы современных водолазных судов, оснащенных системой динамического позиционирования (DP)

. The principle of operation of modern diving vessels equipped with dynamic positioning (DP) systems is analyzed. The classification of diving operations on such vessels is considered. To perform certain construction work or maintenance of underwater facilities, a diving team is needed – the most important component in an integral part of the most difficult work, where the ROV (remote-controlled uninhabited underwater vehicle) of the working class, equipped with tools for certain purposes, is not adapted for complex tasks. The topic of limiting the length of the hose cable (a technology that ensures the delivery of vital consumables, signals and energy via cable to the diver) for underwater work is touched upon. An assessment of the safe working distance of divers near the bow thrusters and propeller columns of the DP vessel is carried out using the example of the most modern vessel in the Caspian Sea to support diving operations, as well as for the installation of underwater structures of the deep-sea bases of the Khankendi. Conclusions are drawn about the widespread use of DP systems on modern ships, in particular those involved in the oil and gas industry, as well as other areas of the marine industry. A study has been conducted to assess the safety of diving operations of a DP vessel operating in shallow water. An assessment is given to limit the movement of the vessel during diving operations. The specifics of the universal vessel involved in connection with the risks of human life, the working environment of divers, various environmental conditions, the proximity of the vessel's propulsion systems and many other additional risks that pose a danger are indicated. The thesis on the need for careful planning and management of diving operations has been confirmed.

Crack Detection of Structures using Artificial Intelligence System

Abstract: Structural Health Monitoring (SHM) having wider scope of operations such as structural audit, rehabilitation of structures and along with it a major scope of crack detection is also included. Effective crack detection is vital for ensuring the safety and longevity of civil engineering structures. Crack Detection in existing methods is tedious process, it requires costly equipment’s as well as time consuming, and inaccuracy in mechanical equipment’s. The technological advancements especially in the field of Artificial Intelligence (AI) maybe an acceptable solution for crack identification through mobile app using Image processing techniques. The mobile app can be used to accurately identify crack pattern and measure the size of cracks in structures (concrete and steel). The app needed to capture and process data from diverse sources, such as images, in real time. It allows for real-time monitoring of structures and ensures safety and integrity of it. The image processing algorithm is used for to accurately identifying various types of cracks in different lighting, environmental conditions, effective and optimized solution. This project explained about real-time data processing for crack detection and its challenges. This demanded robust backend infrastructure and efficient data processing techniques to deliver prompt and accurate results. User Interface and Experience developing an intuitive and user-friendly interface for the app while maintaining high performance.

Fuzzy Logic-Based Dynamic Thermal Rating Assessment for Adaptive Grid Operations

Abstract: In recent times, there has been a notable increase in electricity demand, which is expected to continue rising. This uptick in demand has led to transmission lines operating at higher capacities, exposing them to greater thermal and mechanical stress and affecting the reliability of the transmission network. This paper presents an in-depth analysis of the static thermal rating (STR) and dynamic thermal rating (DTR) of power lines across various climatic conditions. The study investigates the thermal behavior of power lines during both summer and winter seasons over a 24-hour period. Through computational simulations and empirical data collection, the study determines the STR and DTR profiles to evaluate the real-time capacity of power lines under changing environmental conditions. Additionally, the research introduces a novel approach using fuzzy logic to model the dynamic thermal rating (FDTR) based on the IEEE 738 standard, particularly in regions with diverse climatic patterns. By integrating fuzzy logic with meteorological data, the FDTR model improves accuracy in predicting the thermal performance of power lines, considering factors like ambient temperature, wind speed, and solar radiation. This methodology aims to offer utilities and grid operators a reliable tool for optimizing power transmission capacity, reducing the risk of overheating, and ensuring grid reliability in areas with complex and fluctuating weather conditions. The study contributes to the advancement of smart grid technology and facilitates efficient energy management in diverse climate zones.

Evaluation of Optimum MPPT Controllers for PV Cells using Matlab Simulink Platform

Abstract: Nowadays, the Photovoltaic cell is one of the most essential parts in the electrical field to convert photo light to voltage and current, at the desired output voltage and frequency by using various control techniques The Solar panel can produce maximum power at a particular operating point called Maximum Power Point (MPP).To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus to extract maximum available power from a PV module, MPPT algorithms are implemented. The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement MPPT controllers to obtain the maximum power of a PV system regardless of variations in climatic conditions. This project reviews the most used MPPT algorithms, which are Perturb and observe (P&O), Incremental conductance method (ICM), and Fuzzy logic control (FLC).

Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.

Geological survey techniques and carbon storage: Optimizing renewable energy site selection and carbon sequestration

Geological survey techniques play a crucial role in optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage to mitigate climate change. This abstract provides an overview of how geological survey techniques can be used to achieve these objectives. Renewable energy development, particularly solar and wind power, requires careful site selection to maximize energy generation efficiency and minimize environmental impacts. Geological surveys are instrumental in assessing factors such as subsurface geology, topography, soil composition, and hydrological conditions. These surveys help identify suitable locations with optimal wind or solar resources and geologic conditions for infrastructure development. Additionally, geological surveys are essential for identifying suitable sites for carbon storage, a critical component of carbon capture and storage (CCS) technologies aimed at reducing greenhouse gas emissions. Geological formations, such as deep saline aquifers, depleted oil and gas reservoirs, and unmineable coal seams, can serve as storage reservoirs for captured carbon dioxide (CO2). Geological surveys help characterize these formations to assess their suitability for long-term CO2 storage, considering factors such as porosity, permeability, and sealing integrity. Optimizing site selection for renewable energy projects and carbon storage requires a comprehensive understanding of subsurface geology and environmental conditions. Advanced geological survey techniques, such as seismic imaging, remote sensing, and geophysical surveys, are essential for acquiring detailed subsurface data. These techniques enable scientists and engineers to assess site suitability, evaluate risks, and design effective mitigation measures. In conclusion, geological survey techniques are invaluable tools for optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage. By leveraging these techniques, stakeholders can make informed decisions that promote sustainable energy development and mitigate the impacts of climate change.

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

As the oil and gas industry increasingly explores deeper and more remote offshore sites, the maintenance of subsea infrastructure becomes paramount. The use of Artificial Intelligence (AI) in subsea maintenance offers promising solutions to enhance safety and efficiency in these challenging environments. This review explores strategic approaches to integrating AI into subsea maintenance operations. AI facilitates predictive maintenance by analyzing vast amounts of data collected from sensors and historical maintenance records. Machine learning algorithms can detect patterns and predict equipment failures before they occur, enabling proactive maintenance scheduling. This predictive capability reduces downtime and minimizes the risk of accidents by addressing potential issues before they escalate. AI-enabled autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) play a crucial role in subsea inspections and repairs. These AI-enhanced robots can navigate complex subsea environments, perform inspections, and execute maintenance tasks with greater precision and efficiency than human divers. By reducing the need for human intervention in hazardous environments, AI-driven AUVs and ROVs significantly improve safety. Furthermore, AI algorithms can optimize maintenance schedules based on factors such as equipment condition, environmental conditions, and operational requirements. By dynamically adjusting maintenance plans, operators can maximize equipment uptime while minimizing costs and risks. This proactive approach ensures that maintenance activities are conducted at the most opportune times, reducing the likelihood of unplanned downtime and improving overall efficiency. Moreover, AI facilitates condition-based maintenance strategies, where equipment health is continuously monitored in real-time. Sensors installed on subsea infrastructure collect data on factors such as temperature, pressure, and vibration, which is then analyzed by AI algorithms to assess equipment condition. By detecting early signs of degradation or malfunction, AI enables timely interventions, preventing costly breakdowns and ensuring optimal performance. In addition to predictive and condition-based maintenance, AI-driven analytics offer insights into operational performance and asset integrity. By analyzing data from various sources, including sensors, historical records, and operational logs, AI can identify trends, anomalies, and optimization opportunities. These insights enable operators to make data-driven decisions that enhance overall system reliability and efficiency. Strategic approaches to implementing AI in subsea maintenance require collaboration between technology providers, operators, and regulatory bodies. Establishing industry standards and guidelines for AI applications in subsea operations is crucial to ensure safety, reliability, and interoperability. Furthermore, investing in research and development to enhance AI algorithms and robotics technology is essential to unlock the full potential of AI in subsea maintenance. AI-enhanced subsea maintenance offers significant benefits in terms of safety and efficiency. By leveraging predictive analytics, autonomous robotics, and real-time monitoring, operators can optimize maintenance activities, reduce downtime, and minimize risks. Strategic approaches to integrating AI into subsea operations require collaboration, investment, and a commitment to advancing technology to meet the challenges of offshore environments.

Neuroanatomical alterations in brain disorder: A magnetic resonance imaging analysis

Background: Bipolar disorder is a serious mental disorder caused by strong mood fluctuations, affecting 2% of the world's population. People with bipolar disorder experience both manic and depressive episodes, which can lead to suicidal thoughts and changes in appetite, activity, and focus. There are different subtypes of bipolar disorder, with cyclothymic disorder being the milder of the two. Bipolar I is characterized by manic periods, while bipolar II is marked by hypomanic and significant depressive episodes. Bipolar disorder is a biological, genetic, and environmental condition that can affect anyone at any age. Methods: In this study, we enrolled a total of 49 bipolar disorder (BD) participants to 44 healthy control (HC) subjects. Bipolar disorder patients were enlisted with their consent using web portals, medical facilities, and local marketing. MRI contraindications, medical history, self-reported using a mood-altering medication, inadequate vision, and left-handedness were among the exclusion criteria. The various tissue structures which are: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined using voxel-based morphometry (VBM) to ascertain these changes. We employed statistical parametric mapping (SPM) for both image processing and statistical analysis. Results: Our findings demonstrate that individuals with bipolar disorder showed a substantial volume decrease in both gray matter (GM) and white matter (WM), with gray matter (GM) and white matter (WM) volume decreases being more noticeable in the frontal, temporal, parietal, and occipital lobes. Additionally, individuals with bipolar disorder showed no CSF volume in any of the brain regions. Conclusion: Our VBM research backs up earlier studies that link changes in brain structure, particularly gray matter and white matter, to bipolar disorder. These discoveries shed more information on the neurology of bipolar disorder and could guide the creation of more potent diagnostic and treatment modalities.

Computational modelling of tyre behavior on concrete and sandy soil surfaces using solid works software

Tyres are integral components of vehicles, crucial for controlling movement, supporting vehicle weight, and providing traction. Consequently, there's a growing need for accurate tyre modelling to optimize performance under varying conditions. While extensive literature exists on tyre-soil interaction, information is scarce regarding tyre behaviour across different soil types. This study aims to address this gap by employing computational modelling techniques to analyse tyre behaviour on two distinct surfaces: concrete and sandy soil. Utilizing SolidWorks software, a comprehensive investigation was conducted to evaluate tyre performance under varying road conditions. The study focused on analysing shear forces, soil resistances, and partial soil resistant forces exerted on the tyres. The findings indicate that tyres exhibit superior performance on concrete surfaces compared to sandy soil. The computational simulations predicted shear forces of 0.6 kN, soil resistances of 0.2 kN, and partial soil resistant forces of 0.25 kN for the concrete surfaces. These results were notably higher than those observed on sandy soil surfaces. This study sheds light on the intricate dynamics of tyre behaviour under different road conditions and underscores the importance of computational modelling in understanding and optimizing tyre performance. The insights gained from this research have significant implications for vehicle design, road engineering, and the development of more efficient and durable tyres tailored to specific environmental conditions.

Fluorescence spectra as an indicator of the influence of environmental conditions on fruit trees

Molecular fluorescence spectra determine possible structural changes of these molecules and provide data (by comparing them with absorption ones) on the efficiency of solar energy utilization. Fluorescence emission spectra provide information on the functionality of the photosynthetic apparatus by means of two bands red F690 and beyond red F735. The objective of the study is to highlight possible changes in the organization and operation of the photosynthetic apparatus, induced by different radiations, in the leaves, for a variety of pear (Santa Maria), based on the characteristics of the two bands of emitted fluorescence. The Santa Maria variety was studied in the period of June.

Projected future climatic forcing on the global distribution of vegetation types.

Most emissions scenarios suggest temperature and precipitation regimes will change dramatically across the globe over the next 500 years. These changes will have large impacts on the biosphere, with species forced to migrate to follow their preferred environmental conditions, therefore moving and fragmenting ecosystems. However, most projections of the impacts of climate change only reach 2100, limiting our understanding of the temporal scope of climate impacts, and potentially impeding suitable adaptive action. To address this data gap, we model future climate change every 20 years from 2000 to 2500 CE, under different CO2 emissions scenarios, using a general circulation model. We then apply a biome model to these modelled climate futures, to investigate shifts in climatic forcing on vegetation worldwide, the feasibility of the migration required to enact these modelled vegetation changes, and potential overlap with human land use based on modern-day anthromes. Under a business-as-usual scenario, up to 40% of terrestrial area is expected to be suited to a different biome by 2500. Cold-adapted biomes, particularly boreal forest and dry tundra, are predicted to experience the greatest losses of suitable area. Without mitigation, these changes could have severe consequences both for global biodiversity and the provision of ecosystem services. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Studies of the Crystallization and Dissolution of Individual Suspended Sodium Chloride Aerosol Particles.

Aerosols transform between physical phases, as they respond to variations in environmental conditions. There are many industries that depend on these dynamic processes of crystallization and dissolution. Here, a single particle technique (an electrodynamic balance) is used to explore the crystallization and dissolution dynamics of a model system, sodium chloride. The physical and environmental factors that influence the dynamics of crystal formation from a saline droplet (whose initial radius is ∼25 μm) and the kinetics of water adsorption onto dried particles are examined. The drying relative humidity (RH) is shown to impact the physical properties of the dried particle. When a saline droplet is injected into an airflow at an RH close to the efflorescence RH (ERH, 45%), an individual single crystal forms. By contrast, when a compositionally equivalent saline droplet is injected into dry air (RH ∼ 0%), a salt crystal made of multiple crystalline particles is formed. Subsequent to crystallization, the crystal shape, morphology, and surface area were all found to affect the dissolution dynamics of the dried particle. Additionally, we report that the difference between the deliquesce RH and environmental RH significantly impacts the dissolution time scale.

Utilizing Disease Prediction Models to Time Fungicide Applications for Controlling Ripe Rot, Caused by Colletotrichum spp., in Maryland Vineyards

Two previously published ripe rot prediction models, DF2-NN and GH2-DT, were evaluated for fungicide application timing efficacy in Maryland vineyards. Both models utilize leaf wetness duration (LWD), temperature, and grape cluster phenological stages as model parameters. These three parameters were tracked throughout the 2021 to 2023 seasons in three vineyards. The fungicide efficacy trials started at the veraison phenological stage and included a nontreated control, a 12-day interval treatment, and two model-triggered treatments when risk predicted by the models crossed a threshold. The severity of ripe rot on the clusters in each treatment was assessed when the fruit were mature. Ripe rot severity in the nontreated controls was higher during seasons with more LWD and more precipitation. Days in which the models were triggered by the environmental conditions primarily coincided with precipitation events and lengthy LWDs. The model-triggered treatments never had significantly higher ripe rot severity than the 12-day interval treatment but had significantly lower severities than the nontreated control in most trials which had high ripe rot pressure. Furthermore, the model-triggered treatments resulted in fewer fungicide applications than the 12-day interval treatment on average. The DF2-NN model appeared to be more accurate and useful for ripe rot prediction and treatment than the GH2-DT model because it triggered fewer fungicide applications while reducing ripe rot. This model may be useful for improving or maintaining ripe rot control with fewer fungicide inputs, which may be beneficial for the environment and the reduction of fungicide resistance selection.

A meta-analysis of photosynthetic efficiency and stress mitigation by melatonin in enhancing wheat tolerance

BackgroundOur meta-analysis examines the effects of melatonin on wheat under varying abiotic stress conditions, focusing on photosynthetic parameters, chlorophyll fluorescence, leaf water status, and photosynthetic pigments. We initially collected 177 publications addressing the impact of melatonin on wheat. After meticulous screening, 31 published studies were selected, encompassing 170 observations on photosynthetic parameters, 73 on chlorophyll fluorescence, 65 on leaf water status, 240 on photosynthetic pigments.ResultsThe analysis revealed significant heterogeneity across studies (I² > 99.90%) for the aforementioned parameters and evidence of publication bias, emphasizing the complex interaction between melatonin application and plant physiological responses. Melatonin enhanced the overall response ratio (lnRR) for photosynthetic rates, stomatal conductance, transpiration rates, and fluorescence yields by 20.49, 22.39, 30.96, and 1.09%, respectively, compared to the control (no melatonin). The most notable effects were under controlled environmental conditions. Moreover, melatonin significantly improved leaf water content and reduced water potential, particularly under hydroponic conditions and varied abiotic stresses, highlighting its role in mitigating water stress. The analysis also revealed increases in chlorophyll pigments with soil drenching and foliar spray, and these were considered the effective application methods. Furthermore, melatonin influenced chlorophyll SPAD and intercellular CO2 concentrations, suggesting its capacity to optimize photosynthetic efficiency.ConclusionsThis synthesis of meta-analysis confirms that melatonin significantly enhances wheat’s resilience to abiotic stress by improving photosynthetic parameters, chlorophyll fluorescence, leaf water status, and photosynthetic pigments. Despite observed heterogeneity and publication bias, the consistent beneficial effects of melatonin, particularly under controlled conditions with specific application methods e.g. soil drenching and foliar spray, demonstrate its utility as a plant growth regulator for stress management. These findings encourage focused research and application strategies to maximize the benefits of melatonin in wheat farming, and thus contributing to sustainable agricultural practices.

Room Temperature Reduction of Nitrogen Oxide on Iron Metal-Organic Frameworks.

Nitrogen oxides represent one of the main threats for the environment. Despite decades of intensive research efforts, a sustainable solution for NOx removal under environmental conditions is still undefined. Using theoretical modelling, material design, state-of-the-art investigation methods and mimicking enzymes, we have found that selected porous hybrid iron(II/III) based MOF material are able to decompose NOx, at room temperature, in the presence of water and oxygen, into N2 and O2 and without reducing agents. This paves the way to the development of new highly sustainable heterogeneous catalysts to improve air quality. This article is protected by copyright. All rights reserved.

Effect of Platform Configurations and Environmental Conditions on the Performance of Floating Solar Photovoltaic Structures

The growth and development of floating solar photovoltaic (FPV) power plants is a prominent topic within renewable energy technology. One reason contributing to this desired technology design concept is the possibility of land acquisition issues, whereas the usage of the ocean provides a greater technical alternative area. The objective of the research is to present an innovative design for a floating structure, focusing on investigating and comparing the seakeeping performance of several hull configurations: catamaran, trimaran, quadrimaran and pentamaran. The final computational simulation results indicate a linear negative trend in the motion response graphs, particularly in specific significant response values for heave (Global Z), roll (Global RX), and pitch (Global RY), as the hull configuration increases.

Numerical Modeling and Dynamic Response Analysis of an End-Anchored Floating Bridge With a Damaged Pontoon Under Repair Operation

Abstract Floating bridges face potential hazards due to ship collisions throughout their operational lifetime. In a situation where a pontoon is significantly damaged from an accident, a floating drydock may be used to compensate for the lost buoyancy and provide a dry atmosphere for operations. As the repair might take months, a primary concern is whether the repair can be in-site conducted without shutting down the road traffic. This study aims to investigate the feasibility of using a drydock for the repair. The numerical model of the in-operation damaged bridge is established for a comparative dynamic analysis with the intact end-anchored bridge. Eigenvalue analysis is conducted, and pendulum modes of oscillation are found with an eigen-period of around 15 s. The dynamic responses are analyzed through a series of fully coupled time-domain simulations under various environmental conditions. The results indicate that the standard deviation of the moment about the girder weak axis increases significantly at the damaged pontoon axis due to the excitation of low-frequency resonant response. Swell wave loads might induce dynamic amplification to the damaged bridge, even with a relatively small wave height. In addition, the internal stress of the bridge girder is investigated and found to be larger, especially, at the lower locations of the cross section. It is suggested that the responses can be managed by limiting the excitation of pendulum modes or providing special damping devices in practical engineering.

Late Triassic to Early Jurassic carbon isotope chemostratigraphy and organo-facies evolution in a distal to proximal transect of the North German Basin

AbstractThroughout the Latest Triassic and the Early Jurassic, major changes in paleogeography, climate and eustatic sea-level impacted on the development of shelf depositional environments. Secular trends in environmental conditions were punctuated by transient perturbations that occurred in relation to large-scale volcanic events, such as the emplacement of the Central Atlantic Magmatic Province at the Triassic/Jurassic boundary and the Karoo–Ferrar Large Igneous Province in the early Toarcian. We here present bulk organic (HAWK programmed pyrolysis) and organic carbon isotope (δ13Corg) data for three drill cores recovering Latest Triassic and Early Jurassic strata (Rhaetian to Toarcian). Study sites are located in the northeastern part of the Central European Epicontinental Sea and were positioned along a distal–proximal transect of the North German Basin. This allows discussing the differential response of depositional settings and organo-facies toward secular and transient environmental change. Biostratigraphically anchored trends in δ13Corg values allow the precise correlation along the transect, as well as with distant sites. At all North German locations, diagnostic secular trends in δ13Corg are punctuated by transient negative carbon isotope excursions, reflecting perturbations of the global carbon cycle at the Triassic/Jurassic boundary and in the early Toarcian. Stratigraphic gaps occurred during sea-level lowstands and are most pronounced at shallow proximal sites. Programmed pyrolysis data indicate spatiotemporal organo-facies trends that on a temporal scale occurred in response to sea-level and climate trends, while spatial patterns were governed by basin morphology and paleobathymetry. Substantial marine organic matter accumulations occurred at high sea level during the Toarcian only, and were most continuous at distal sites. Graphical Abstract

The prospect of social medicine in South Africa

The term “médecine sociale” (social medicine in English) was first coined in 1848 during the French Revolution by Dr. Jules Guérin. It is a branch of medicine that focuses on the impact of social and economic conditions on health, disease, and the practice of medicine, with the intention of creating a healthier society. It is an interdisciplinary program between medicine and social sciences, expected to equip medicine with the knowledge and skills needed for analyzing the social causes of health and illness, akin to how the alliance between medicine and laboratory sciences provided insights into the biological, chemical, and physical bases of diseases. Social medicine is also interconnected with social determinants of health. While social determinants of health specifically target social and environmental conditions that affect health, social medicine has a broader scope, encompassing the entire social context of health, including societal structures and cultural factors. In the post-apartheid era, social medicine could play an important role in transforming the healthcare system to ensure equity and inclusivity, leading to improved health outcomes. This would require re-engineering the education of health professionals in South Africa at both undergraduate and postgraduate levels, as practiced in many parts of the world. This will assist South Africa in joining the global movement of social medicine for the promotion of human rights and social justice in medicine, and a better understanding of the impact of social factors on the aetiology and management of medical disorders in the 21st century.