The President of India is the Supreme Commander of the Indian Army, and its professional head is the Chief of Army Staff (COAS), who is a four-star general. Two officers have been conferred with the rank of field marshal, a five-star rank, which is a ceremonial position of great honour. The Indian Army originated from the armies of the East India Company, which eventually became the British Indian Army, and the armies of the princely states, which were merged into the national army after independence. The units and regiments of the Indian Army have diverse histories and have participated in several battles and campaigns around the world, earning many battle and theatre honours before and after Independence.The primary mission of the Indian Army is to ensure national security and national unity, to defend the nation from external aggression and internal threats, and to maintain peace and security within its borders. It conducts humanitarian rescue operations during natural calamities and other disturbances, such as Operation Surya Hope, and can also be requisitioned by the government to cope with internal threats. It is a major component of national power, alongside the Indian Navy and the Indian Air Force. The army has been involved in four wars with neighbouring Pakistan and one with China. Other major operations undertaken by the army include Operation Vijay, Operation Meghdoot, and Operation Cactus. The army has conducted large peacetime exercises such as Operation Brasstacks and Exercise Shoorveer, and it has also been an active participant in numerous United Nations peacekeeping missions, including those in Cyprus, Lebanon, Congo, Angola, Cambodia, Vietnam, Namibia, El Salvador, Liberia, Mozambique, South Sudan, and Somalia.The Indian Army is operationally and geographically divided into seven commands, with the basic field formation being a division. Below the division level are permanent regiments that are responsible for their own recruiting and training. The army is an all-volunteer force and comprises more than 80% of the country's active defence personnel. It is the largest standing army in the world, with 1,237,117 active troops and 960,000 reserve troops. The army has embarked on an infantry modernisation program known as Futuristic Infantry Soldier As a System (F-INSAS), and is also upgrading and acquiring new assets for its armoured, artillery, and aviation branches.
Objectives: Night vision goggles (NVG) display brightness can disrupt visual adaptation to low ambient light levels at night. Visual acuity (VA) and contrast sensitivity (CS) are established, quantifiable indicators for assessing visual adaptation time (VAT). This study estimated and compared VAT under ambient night-time illumination conditions following the discontinuation of Generation 2++ and Generation 3 NVG use. Material and Methods: Aviator’s Night Vision Imaging System Gen 2++ and F4949 Gen 3 NVGs were used. VA and CS were measured using the USAF 1951 tri-bar resolution chart and the Pelli–Robson chart, respectively. VA and CS with Gen 2++ NVGs were assessed in 22 and 8 participants, respectively, whereas both VA and CS with Gen 3 NVGs were evaluated in 11 participants. Baseline unaided VA and CS were recorded under four ambient illumination conditions: full moon (0.1 lx), half-moon (0.05 lx), quarter moon (0.01 lx), and starlight (0.001 lx). Following 10 min of NVG display light exposure, the time required to recover baseline VA and CS under each lighting condition was measured. Results: The mean VAT for VA following Gen 2++ NVG use was 6 min 9 s, 2 min 53 s, and 1 min 41 s under quarter-moon, half-moon, and full-moon illumination conditions, respectively. The corresponding mean VAT values for CS were 3 min 36 s, 2 min 19 s, and 1 min 14 s. Among Gen 3 NVG users, the mean VAT for VA was 5 min 0 s, 4 min 43 s, 2 min 16 s, and 1 min 31 s under starlight, quarter-moon, half-moon, and full-moon conditions, respectively. The mean VAT for CS under the same illumination levels was 3 min 58 s, 2 min 52 s, 2 min 2 s, and 1 min 10 s, respectively. The findings suggest that, on post-de-goggling, lower ambient illumination has increased VAT irrespective of NVG type. Target detection may precede target recognition under quarter-moon conditions, whereas both occur almost simultaneously at higher illumination levels following removal of the Gen 2++ NVG; removal of Gen 3 NVG showed differences in target detection and target recognition times only under starlight conditions. Conclusion: The study findings provide an estimate of the time required for optimal visual identification and recognition of surrounding cues after de-goggling during NVG-assisted flight operations. The study highlights the superior performance of Gen 3 NVGs over Gen 2++ NVGs in facilitating faster visual adaptation under low-light conditions.
Non-Hodgkin lymphoma (NHL) comprises a category of malignant neoplasms that arise from lymphoid tissues, which primarily affect the lymph nodes. Histologically, diffuse large B-cell lymphoma (DLBCL) is the most common subtype of NHL. The various clinical manifestations depend on the site, stage, and extent of disease and are generally treated with Chemo-immunotherapy. A 39-year-old civil aircrew, diagnosed as a case of DLBCL, treated with chemo-immunotherapy (rituximab, cyclophosphamide, hydroxydaunorubicin, oncovin, and prednisolone regimen), after complete remission and drug-free status for 25-month duration, reported to an Indian Air Force boarding center for aeromedical evaluation and disposal. The survival rate post-treatment for DLBCL is 94% as per the International Prognostic Index. However, in the first 1–2 years, the recurrence rate is between 30 and 40%. Aeromedical concerns include treatment efficacy, recurrence/relapse, post-chemotherapeutic effects, aviation risk on disability, prognosis and follow-up duration, and investigations to be carried out. The risk assessment matrix was carried out in this case using 5-year prognosis survival, and the risk of incapacitation fell between 0.1% and 1%. The fitness for flying was considered on the basis of the incapacitation risk of <1% and upgraded to the restricted flying category. The certification assessment for malignancy can be based on the survival rate post-treatment due to the lack of evidence on the risk of recurrence. Hence, the certification for aircrew with any malignancy can be assessed by treatment efficacy, risk of recurrence, and risk of incapacitation. The survival curve can be used as a tool for calculating the risk of recurrence matrix and risk of incapacitation.
Hospital emergency departments (EDs) are a major source of noise pollution which has a negative impact on health outcomes, clinical communication, staff performance and health service delivery. Though becoming more widely recognised, most existing mitigation strategies are reactive interventions applied to surfaces or behaviour, and do not address the role of architectural design in shaping acoustic performance before construction is complete. The authors in this study suggest a proactive design-based approach that combines quantitative spatial indicators to screen and compare noise-related layout characteristics during the planning phase of a health care facility. The study involved four functional areas of a large tertiary care ED in a South Asian urban health care environment: the Triage Area, the Waiting Hall, the Nurses’ Station, and a Treatment Corridor, employing a comprehensive mixed methods approach. Objective acoustic monitoring was conducted over 20 consecutive weekdays (480 elapsed hours) using calibrated Class-1 sound level meters, which recorded sound pressure levels (SPL) of Leq, L10, L90 and peak. A structured survey was conducted, with 312 respondents (174 health care professionals and 138 patients/visitors), to record perceptions of noise sources and impacts. Architectural layout analysis was used to develop three new spatial indicators: Openness Index (OI), Flow Density (FD), and Zoning Index (ZI), while the propagation of noise was simulated using COMSOL Multiphysics® finite-element software and different design interventions were assessed. The results showed measured equivalent sound levels between 62.2 dB(A) and 67.0 dB(A), exceeding the study reference thresholds used for comparison. The largest noise sources identified were patient/visitor conversations (37.9
Suspension faults in defence vehicles (either crewed or uncrewed ground vehicles), such as damper leakage, loose mountings, and bushing degradation, can significantly impair stability, mobility, and control performance under severe off road terrain conditions. This paper presents an explainable machine learning framework for embedded suspension fault detection using multi domain vibration and acoustic sensing. The approach integrates eight physically interpretable time/frequency features with a Random Under Sampling Boosted (RUSBoost) ensemble classifier to discriminate four suspension health states. Full scale experimental validation was carried out on a Land Rover Defender 110 mounted on a four-post hydraulic vibration rig capable of reproducing broadband, defence-relevant excitation. The proposed method achieved 100% accuracy (AUC = 1) in a holdout evaluation and maintained a mean accuracy of 74% (AUC = 0.87) under tenfold cross validation, demonstrating strong generalisation. Feature importance analysis identified peak-to-peak amplitude, standard deviation, and RMS as the most influential indicators, confirming that suspension degradation is primarily characterised by increased vibration excursion and dispersion rather than spectral redistribution. Principal component analysis further showed partial overlap among fault classes, highlighting the necessity of nonlinear ensemble decision boundaries. Despite higher training cost, the ensemble classifier achieved prediction times that indicate computational feasibility for future real time embedded implementation. Overall, the framework provides a transparent, robust, and control compatible diagnostic layer, supporting future integration with predictive maintenance and fault tolerant suspension control in defence mobility systems.
Objectives: Oral health significantly influences functional performance and psychological well-being. Military aviators operate under unique physiological and operational stresses where even minor dental issues can compromise in-flight performance and safety. As evidence regarding oral health-related quality of life (OHRQoL) among military aviators is scarce, this study aimed to assess the perception of oral health and OHRQoL using the Oral Health Impact Profile-14 (OHIP-14) and to evaluate its association with demographic, occupational, and behavioral factors. Material and Methods: A cross-sectional study was conducted among 430 military aviators using a structured, self-administered online questionnaire comprising flying attributes, demographic details, oral health awareness and hygiene habits, smoking habits, and the validated OHIP-14 instrument. Descriptive statistics, unpaired t -tests, one-way analysis of variance, and Spearman’s correlation were performed using IBM Statistical Package for the Social Sciences version 26.0, with significance set at p < 0.05. Results: Overall OHRQoL among aviators was favorable. Functional limitation (mean = 4.10 ± 1.89) and physical pain (mean = 2.22 ± 1.59) were the most affected domains. Advancing age and longer service duration were significantly associated with higher OHIP scores ( p < 0.001). Smokers demonstrated significantly poorer OHRQoL compared to non-smokers ( p = 0.047). Strong positive correlations were observed among physical pain, functional limitation, physical disability, and handicap domains. Conclusion: Aviators demonstrated favorable OHRQoL; however, functional limitation and physical pain remain key concerns. Age, duration of service, and smoking were major determinants of reduced OHRQoL. Incorporating periodic OHRQoL assessments, smoking cessation counseling, and preventive oral health programs into aeromedical evaluations can enhance dental readiness and operational efficiency in the Armed Forces.
