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Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Multiple slip effects on MHD non-Newtonian nanofluid flow over a nonlinear permeable elongated sheet: Numerical and statistical analysis
Purpose: The purpose of this paper is to examine the interaction effects of a transverse magnetic field and slip effects of Casson fluid with suspended nanoparticles over a nonlinear stretching surface. Mathematical modeling for the law of conservation of mass, momentum, heat and concentration of nanoparticles is executed. Design/methodology/approach: Governing nonlinear partial differential equations are reduced into nonlinear ordinary differential equations and then shooting method is employed for its solution. The slope of the linear regression line of the data points is calculated to measure the rate of increase/decrease in the reduced Nusselt number. Findings: The effects of magnetic parameter (0=M=4), Casson parameter (0.1=?<8), nonlinear stretching parameter (0=n=3) and porosity parameter (0=P=6) on axial velocity are shown graphically. Numerical results were compared with another numerical approach and an excellent agreement was observed. This study reveals the fact that the Brownian motion parameter and boundary layer thickness have a direct relationship with temperature. Also, Brownian motion and thermophoresis contribute to an increase in the thermal boundary layer thickness. Originality/value: Despite the immense significance and repeated employment of non-Newtonian fluids in industry and science, no attempt has been made up till now to inspect the Casson nanofluid flow with a permeable nonlinear stretching surface. 2019, Emerald Publishing Limited. -
Multiple solutions and stability analysis in MHD non-Newtonian nanofluid slip flow with convective and passive boundary condition: Heat transfer optimization using RSM-CCD
This study explores the effect of Williamson nanofluid in the presence of radiation and chemical reaction caused by stretching or shrinking a surface with convective boundary conditions. After implementing two-component model and Lie group theory, the transformed ODEs are solved using the RungeKutta DormandPrince (RKDP) shooting approach technique. The dual solutions are predicted for certain range of physical nanofluid parameters, such as Williamson parameter ((Formula presented.)), stretching/shrinking parameter ((Formula presented.)), and suction parameter ((Formula presented.)) with different slip (Formula presented.) and magnetic M parameters. Contour plots are generated for the stable branch of the Nusselt number ((Formula presented.)) for different combinations, providing insights into the heat transfer characteristics. The eigenvalue problem is solved in order to predict flow stability. The optimization of heat transfer in nanoliquid is conducted by RSM-CCD. The resulting quadratic correlation enables the prediction of the optimal Nusselt number for (Formula presented.), (Formula presented.), and (Formula presented.). This investigation is motivated by various applications including manufacturing processes, thermal management systems, energy conversion devices, and other engineering systems where efficient heat transfer iscrucial. 2023 Wiley-VCH GmbH. -
Multiplier-free Realization of High throughout Transpose Form FIR Filter
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE. -
Multitask EfficientNet affective computing for student engagement detection
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multitask EfficientNet affective computing for student engagement detection
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption
The study presents a machine learning approach to predict carbon dioxide (CO2) emissions by analysing key factors such as energy consumption, renewable energy adoption, and economic growth (GDP). Traditional forecasting methods struggle to capture the complex and nonlinear patterns of emissions. To overcome the limitations and improve the accuracy, research combines classical statistical models like ARIMA and VAR with advance techniques, including deep learning (LSTM) and ensemble methods (XGBoost, stacking). The models are trained on a global dataset of energy and economic records. The results shows that the hybrid models, particularly the LSTM + XGBoost and stacked approaches, have outperformed the traditional methods by obtaining a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R2). Apart from advancing environmental data science, the research offers a solid predictive framework to support policy initiatives related to the Sustainable Development Goals, specifically SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). 2025 IEEE. -
Multivariate optimization of electrochemical sensing parameters for bisphenol a detection using an AgNPs/g-C3N4/IL@GCE via box-behnken design
We developed an electrochemical sensor for detecting BPA using AgNPs/g-C3N4/IL@GCE. Graphitic carbon nitride was synthesized by the calcination of melamine at 550 C. In contrast AgNPs were synthesized via a green tea extract method, and the nanocomposite was dropcast onto the GCE surface along with 1-butyl-3-methylimidazolium methyl sulfate (BMIM-MeSO4) ionic liquid as a binder. Morphology was characterized, and response surface methodology (RSM) using Box-Behnken Design (BBD) was used as a concurrent strategy to optimize pH, scan rate, and deposition time. Independent validation experiments conducted at multiple interior points within the design space showed good agreement between predicted and experimental responses, with prediction errors of 6.29%-10.11% for oxidation peak current and 1.63%-5.24% for oxidation potential. Applying the optimized conditions, the sensor linearized BPA detection over the concentration range of 1-10 ?M, with a limit of detection of 0.66 ?M, and a limit of quantification of 2.20 ?M. The sensor showed excellent recovery (99.16%-102.26%) of BPA present in real water samples. The improved electrocatalytic activity of the sensor interfaces was due to the synergistic effects of AgNPs and g-C3N4. The novelty of this research was the use of an RSM-BBD approach to systematically optimize a green synthesized AgNPs/g-C3N4 nanocomposite electrode, enabling predictive modelling to show reasonable electrochemical sensitivity towards BPA detection. 2026 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. All rights. -
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Multiwavelength spectral modelling of the candidate neutrino blazar PKS 0735+178
The BL Lac object PKS 0735+178 was in its historic ?-ray brightness state during 2021 December. This period also coincides with the detection of a neutrino event IC 211208A, which was localized close to the vicinity of PKS 0735+178. We carried out detailed ?-ray timing and spectral analysis of the source in three epochs: (a) quiescent state (E1), (b) moderate-activity state (E2), and (c) high-activity state (E3) coincident with the epoch of neutrino detection. During the epoch of neutrino detection (E3), we found the largest variability amplitude of 95 per cent. The ?-ray spectra corresponding to these three epochs are well fit by the power-law model and the source is found to show spectral variations with a softer when brighter trend. In epoch E3, we found the shortest flux doubling/halving time of 5.75 h. Even though the spectral energy distribution in the moderate-activity state and in the high-activity state could be modelled by the one-zone leptonic emission model, the spectral energy distribution in the quiescent state required an additional component of radiation over and above the leptonic component. Here, we show that a photomeson process was needed to explain the excess ?-ray emission in the hundreds of GeV that could not be accounted for by the synchrotron self-Compton process. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved. -
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V. -
Musculoskeletal Disorders and Psychological Well-being among Indian Nurses: A Narrative Review of Impacts and Interventions (2024)
Background: A prevalent occupational health issue that may have a detrimental effect on nurses' mental health and general well-being is musculoskeletal problems. This narrative review aimed to explore the social, economic, and personal implications of Musculoskeletal Disorder on nurses in India, and examine support, and intervention strategies available for them. Material & Methods: A comprehensive literature search was conducted in electronic databases, including PubMed, Scopus, and Google Scholar, using relevant keywords related to Musculoskeletal Disorder, mental health, nurses, social, personal, support, and intervention. The inclusion criteria were articles published in English and focused the nursing workforce in India. Results: A total of 15 articles were selected for review synthesis. According to the summary, nurses in India who suffer from musculoskeletal disorders deal with serious social and personal repercussions that impact their everyday life and general well-being. Musculoskeletal Disorder can lead to decreased social connections, reduced job satisfaction, and physical and emotional distress. However, limited interventions are available that address Musculoskeletal Disorder and the mental health of nurses in India. Conclusion: There is a significant effect of Musculoskeletal Disorder on the mental health, quality of life, and economic well-being of nurses in India. However, limited scientific research exists exploring the prevalence and psychosocial implications of Musculoskeletal Disorder in the Indian nursing population. Consequently, additional research is essential to comprehend the scope and ramifications of this occupational health concern. To create interventions and support systems that are effective in the unique cultural and occupational context of nursing in India, it is imperative to engage in interdisciplinary collaboration. 2024 The Author(s); Published by Rafsanjan University of Medical Sciences. -
Museum visit as an academic engagement activity: teachers perspective
The educational value of museums are immense as they are the storehouse of artifacts depicting history, culture, and science. Teachers must tap this resource to provide students the experiential learning. Bangalore is one of the metropolitan cities of India, hosting variety of museums where artifacts related to various school subjects such as history, geography, science, art, culture, technology, and music are found. Therefore, the present study aims to find out the teachers' perspective on museum visits as an academic engagement activity and a resource for experiential learning. The study employed a descriptive survey design to understand school teachers' perspectives on museum visits in Bangalore. A convenient sampling method yielded 200 complete responses from school teachers. The study revealed that despite knowing about the field trip to museum as a source of learning, teachers revealed several constraints in organization and implementation of such visits. Having understood teachers perspective, study suggests future research may delve into students' learning engagement on a field trip. 2025, Intelektual Pustaka Media Utama. All rights reserved. -
Museum visit intervention in K-12 education: a scoping review
This scoping review aims to provide an overview of empirical studies on worldwide museum visit intervention in K-12 education. The study employed Mendeley citation software to identify the articles in the database. A metaanalysis PRISMA statement is used for reporting the items. Out of 135 possibly rich articles, the present study reviewed 18 studies that met the inclusion criteria and were subjected to descriptive and content analyses published between 2017 and 2021. Most of the studies are experimental and from primary school contexts. It is revealed that science is the subject matter context majority of the studies, but philosophy, disaster management, language, and environmental science are also represented. The content analysis resulted in the following learning and social outcomes. It states that social outcome is explored chiefly, followed by learning outcome. The findings indicate that museum visit intervention positively impacts students learning and social outcome. The review also identifies the need for further research on museum visit intervention in the Asia Pacific region. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Mushroom Farming and Rural Youth: Analyzing a Sustainable Livelihoods Framework
We examine mushroom farming as an alternative livelihood for rural youth, utilizing the Sustainable Livelihoods Framework (SLF) to assess its impact on economic resilience and rural development. A qualitative study was conducted in Uttarakhand, India, involving 20 participants aged 1835 who were engaged in mushroom cultivation. Data from semi-structured interviews were analyzed thematically, revealing that rural youth are motivated by their hopes for financial independence and a growing interest in sustainable agriculture. Mushroom farming enhances human capital through skill development, cultivation, and business management, which in turn boosts self-esteem. The income they earn provides financial capital to support their families and reinvestment in their businesses. In addition to empowering marginalized groups, mushroom farming bolsters all five capitals of the SLF: human capital through skills and education; financial capital through income generation; natural capital via the use of agricultural waste and biological resources; physical capital through infrastructure and tools; and social capital through peer networks and community support. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Mushroom-Derived Carbon Nanosheets for Efficient Photothermal De-Icing Applications
The green synthesis of nanomaterials has emerged as a viable alternative to traditional techniques that reduce environmental risks and the production of harmful byproducts. In this work, biomaterial derived from wild mushrooms was used to synthesize psilocybin-derived carbon nanosheets (P-CNSs). The bioactive substance psilocybin serves as a sustainable precursor that ensures an environmentally friendly synthesis procedure. Spectroscopic measurements confirm the structural and functional properties of the P-CNSs. The naturally extracted P-CNSs demonstrated substantial photothermal conversion efficiency under both visible and infrared light. Their adaptability for thermal applications was shown by their medium-specific response. Furthermore, in photothermal de-icing, P-CNSs effectively melted ice under visible light exposure, making it a crucial application. Additionally, density functional theory (DFT) and time-dependent DFT calculations were performed to optimize the structures of psilocin, baeocystin, and norbaeocystin, which show the electronic transitions responsible for the appearance of absorption and fluorescence behavior. This work draws attention to the inclusion of psilocybin in green synthesis to produce an affordable and sustainable solution for environmental issues brought to the forefront by the advantages of environmentally benign manufacture and multipurpose use, especially in thermal control and environmental remediation. 2026 American Chemical Society -
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining
Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved. -
MVTamperBench: Evaluating Robustness of Vision-Language Models
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerability to adversarial tampering and manipulations remains under-explored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping; based on real-world visual tampering scenarios such as surveillance interference, social media content edits, and misinformation injection. MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks. This benchmark challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families. We reveal substantial variability in resilience across tampering types and show that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code, data, and benchmark to foster open research in trustworthy video understanding. 2025 Association for Computational Linguistics.