Intricate Environment Research Articles

The True Cost-to-Serve in Nursing Homes: A Methodological Issue in Applying Activity-Based Costing

The healthcare sector is marked by intense competition, escalating costs, service personalization, quality standards, financial constraints, and increased demand. Healthcare organizations must implement efficient cost management systems to navigate this intricate environment. The debate persists in the literature regarding the optimal cost accounting system for healthcare: traditional cost-center-based or activity-based costing (ABC). This article delves into the factors influencing the selection of a suitable cost accounting system, examining the implementation of ABC in an Italian nursing home. A methodological challenge emerged during the ABC implementation, relevant to all healthcare facilities serving diverse elderly populations. The article explores this issue, evaluates potential solutions and their impacts, and determines the approach enabling managers to accurately assess service costs and uphold service quality.

Environmental hazard assessment of forest fire sites to firefighting aircraft—part I: Canyon wind and temperature distribution

Wildfires have caused immense damage to the environment, property and human safety in recent years. Fortunately, the deployment of firefighting aircraft, particularly water bombers, has emerged as one of the most effective strategies for combating wildfires. However, the intricate environment of forest fire sites, characterized by thermal turbulence and canyon winds, presents a formidable flight risk for firefighting aircraft. To address this issue, this study conducted a comprehensive risk assessment of firefighting aircraft operating in a wildfire environment, analyzing the impact of thermal turbulence and canyon winds using a finite element simulation method. This approach not only bridges the research gap in the field of flight safety but also evaluates the environmental risks encountered by firefighting aircraft when entering forest fire sites. Our findings underscore thermal turbulence and canyon winds as potential hazards for aircraft flying over mountainous terrain, elucidating the effect of thermal turbulence on aircraft engine intakes and quantifying the total lift loss incurred during encounters with canyon winds featuring non-uniform airflow velocities. Specifically, thermal turbulence can induce instability and vibration in aircraft engines, whereas canyon winds can generate updrafts and downdrafts that may compromise aircraft structures or lead to lift loss. Furthermore, we cite several references to emphasize the multifaceted risks associated with the forest fire site environment, encompassing temperature gradients, thunderstorms, and air pollution. Such comprehensive wildfire data can be invaluable in assessing the flight safety of firefighting aircraft during water-dropping missions in forest fire scenarios.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Plant-microbe Interactions and their Potential Application in Improving Disease Resistance in Vegetable Crops

Microorganisms have a substantial impact on the environment and have been observed to either immunize or infect organisms from all kingdoms. Plants and microorganisms have undergone co-evolution and engage in interactions within their natural environment. Plant microbiota, often known as plant-associated microorganisms, are essential components of plant existence. Similar to other living systems, the plant body interacts with a range of living and non-living factors as it develops in an intricate environment. The plant microbiome can sustain disease suppression by providing essential tasks to their host, such as stress tolerance, vitality, and development. Plants and microorganisms have evolved molecular systems to mutually interact and derive advantages from their interaction. This symbiotic relationship is advantageous for both the microorganisms and the plants. Various pathways have been explored and are currently being utilized to achieve agricultural, environmental, and health benefits, based on this molecular interaction. Plant-associated microorganisms serve a vital role in the growth and development of plants. Microorganisms stimulate plant growth by directly increasing nutrient uptake and stimulating hormone production. The potential benefits of these mutually beneficial interactions between plants and microbes could be connected to develop microbial inoculants for application in agricultural biotechnology. In general, the utilization of microorganisms and the establishment of beneficial plant-microbe interactions offer promising and environmentally favourable approaches for sustainable and organic agriculture worldwide.

A novel optimized pre-embedding antibody-labelling correlative light electron microscopy technique.

In the intricate environment of a cell, many studies seek to discover the location of specific events or objects of interest. Advances in microscopy in recent years have allowed for high detail views of specific areas of cells of interest using correlative light electron microscopy (CLEM). While this powerful technique allows for the correlation of a specific area of fluorescence on a confocal microscope with that same area in an electron microscope, it is most often used to study tagged proteins of interest. This method adapts the correlative method for use with antibody labelling. We have shown that some cellular structures are more sensitive than others to this process and that this can be a useful technique for laboratories where tagged proteins or viruses, or dedicated CLEM instruments are not available.

In Situ Single-Cell Bacterial Imaging Provides Mechanistic Insight into the Photodynamic Action of Photosensitizer-Loaded Hydrogels.

Hydrogels, three-dimensional hydrophilic polymeric networks with high water retaining capacity, have gained prominence in wound management and drug delivery due to their tunability, softness, permeability, and biocompatibility. Electron-beam polymerized poly(ethylene glycol) diacrylate (PEGDA) hydrogels are particularly useful for phototherapies such as antimicrobial photodynamic therapy (aPDT) due to their excellent optical properties. This work takes advantage of the transparency of PEGDA hydrogels to investigate bacterial responses to aPDT at the single-cell level, in real-time and in situ. The photosensitizer methylene blue (MB) was loaded in PEGDA hydrogels by using two methods: reversible loading and irreversible immobilization within the 3D polymer network. MB release kinetics and singlet oxygen generation were studied, revealing the distinct behaviors of both hydrogels. Real-time imaging of Escherichia coli was conducted during aPDT in both hydrogel types, using the Min protein system to report changes in bacterial physiology. Min oscillation patterns provided mechanistic insights into bacterial photoinactivation, revealing a dependence on the hydrogel preparation method. This difference was attributed to the mobility of MB within the hydrogel, affecting its direct interaction with bacterial membranes. These findings shed light on the complex interplay between hydrogel properties and the bacterial response during aPDT, offering valuable insights for the development of antibacterial wound dressing materials. The study demonstrates the capability of real-time, single-cell fluorescence microscopy to unravel dynamic bacterial behaviors in the intricate environment of hydrogel surfaces during aPDT.

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection.

Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion process, significantly compromising the network's detection performance on traffic targets. This paper presents a novel methodology by which to overcome the concerns above, namely HRYNet. Firstly, a dual fusion gradual pyramid structure (DFGPN) is introduced, which employs a two-stage gradient fusion strategy to enhance the generation of more comprehensive multi-scale high-level semantic information, strengthen the interconnection between non-adjacent feature layers, and reduce the information gap that exists between them. HRYNet introduces an anti-interference feature extraction module, the residual multi-head self-attention mechanism (RMA). RMA enhances the target information by implementing a characteristic channel weighting policy, thereby reducing background interference and improving the attention capability of the network. Finally, the detection performance of HRYNet was evaluated by utilizing three datasets: the horizontally collected dataset BDD1000K, the UAV high-altitude dataset Visdrone, and a custom dataset. Experimental results demonstrate that HRYNet achieves a higher mAP_0.5 compared with YOLOv8s on the three datasets, with increases of 10.8%, 16.7%, and 5.5%, respectively. To optimize HRYNet for mobile devices, this study presents Lightweight HRYNet (LHRYNet), which effectively reduces the number of model parameters by 2 million. The results demonstrate that LHRYNet outperforms YOLOv8s in terms of mAP_0.5, with improvements of 6.7%, 10.9%, and 2.5% observed on the three datasets, respectively.

Pressure-Sensitive Capability of AgNPs Self-Sensing Cementitious Sensors.

Intelligent monitoring approaches for long-term, real-time digitalization in structural health monitoring (SHM) are currently attracting significant interest. Among these, self-sensing cementitious composites stand out due to their easy preparation, cost-effectiveness, and excellent compatibility with concrete structures. However, the current research faces challenges, such as excessive conductive filler, difficulties in filler dispersion, and insufficient stress sensitivity and instability. This study presents a novel approach to these challenges by fabricating self-sensing cementitious sensors using silver nanoparticles (AgNPs), a new type of conductive filler. The percolation threshold of AgNPs in these materials was determined to be 0.0066 wt%, marking a reduction of approximately 90% compared to traditional conductive fillers. Moreover, the absorbance test with a UV spectrophotometer showed that AgNPs were well dispersed in an aqueous solution, which is beneficial for the construction of conductive pathways. Through various cyclic loading tests, it was observed that the self-sensing cementitious sensors with AgNPs exhibited robust pressure-sensitive stability. Additionally, their stress sensitivity reached 11.736, a value significantly surpassing that of conventional fillers. Regarding the conductive mechanism, when encountering the intricate environment within the cementitious material, AgNPs can establish numerous conductive pathways, ensuring a stable response to stress due to their ample quantity. This study provides a significant contribution to addressing the existing challenges in self-sensing cementitious materials and offers a novel reference for further research in this domain.

Study on Brand Management of Fashion Brands

Amidst the backdrop of swift economic progression and surging consumption rates, the global goods market is witnessing heightened brand competition. This evolving scenario has rendered domestic fashion brands particularly susceptible to various external adversities. Prominent among these challenges are misconceptions in brand management characterized by inadequate market positioning, diminished market share, nascent brand imagery, evolving marketing channels, and stagnation in social marketing innovation. To traverse this intricate environment, businesses must champion a forward-leaning stance, assimilate insights from seasoned brand management practices, and perpetually refine their branding techniques. This deliberate nurturing aims to bolster and expand their brand equity. In light of these observations, this paper articulates multiple strategies to amplify brand management, encompassing the amplification of consumer desires, recalibration of fashion marketing paradigms, in-depth consumer behavior insights, nuanced focus on marketing touchpoints, adoption of holistic multimedia marketing avenues, and harnessing the potential of fashion communication ecosystems. This synthesis is envisioned to serve as a compass for marketing aficionados, equipping enterprises to carve a distinctive niche in a saturated market, thereby charting a trajectory for resilient growth.

Unraveling the lymphatic system in the spinal cord meninges: a critical element in protecting the central nervous system.

The lymphatic vasculature plays a crucial role in fluid clearance and immune responses in peripheral organs by connecting them to distal lymph nodes. Recently, attention has been drawn to the lymphatic vessel network surrounding the brain's border tissue (Aspelund et al. in J Exp Med 212:991-999, 2015. https://doi.org/10.1084/jem.20142290 ; Louveau et al. in Nat Neurosci 21:1380-1391, 2018. https://doi.org/10.1038/s41593-018-0227-9 ), which guides immune cells in mediating protection against tumors (Song et al. in Nature 577:689-694, 2020. https://doi.org/10.1038/s41586-019-1912-x ) and pathogens Li et al. (Nat Neurosci 25:577-587, 2022. https://doi.org/10.1038/s41593-022-01063-z ) while also contributing to autoimmunity (Louveau et al. 2018) and neurodegeneration (Da Mesquita et al. in Nature 560:185-191, 2018. https://doi.org/10.1038/s41586-018-0368-8 ). New studies have highlighted the integral involvement of meningeal lymphatic vessels in neuropathology. However, our limited understanding of spinal cord meningeal lymphatics and immunity hinders efforts to protect and heal the spinal cord from infections, injury, and other immune-mediated diseases. This review aims to provide a comprehensive overview of the state of spinal cord meningeal immunity, highlighting its unique immunologically relevant anatomy, discussing immune cells and lymphatic vasculature, and exploring the potential impact of injuries and inflammatory disorders on this intricate environment.

UAVPNet: A balanced and enhanced UAV object detection and pose recognition network

With the increasing popularity of unmanned aerial vehicles (UAVs), accurate positioning and pose recognition of UAVs by target images based on photoelectric detection become a research hotspot. To solve this issue, a multi-scale UAV-Pose dataset consisting of 1400 UAV images is contributed in this paper. In addition, a balanced and enhanced network (UAVPNet) is proposed. UAVPNet has two major features: (1) balanced feature pyramid (BFP) feature fusion structure to improve unbalanced multi-scale features; (2) VarifocalNet detection head to alleviate the foreground-background imbalance. A comparative study demonstrates that UAVPNet is superior to some state-of-the-art object detection models (such as Faster R-CNN-CARAFE, and Yolov8, etc.) in terms of detection accuracy and robustness. Specifically, UAVPNet achieves state-of-the-art 0.885 mAP on the newly-created UAV-Pose dataset, together with nearly 33.49 M parameters, 139.9G FLOPs, and 9.8 FPS. It could fully fulfill the requirements of UAV positioning and pose recognition in the intricate environment.

Shining Light on Multidrug‐Resistant Bacterial Infections: Rational Design of Multifunctional Organosilver‐Based AIEgen Probes as Light‐Activated Theranostics for Combating Biofilms and Liver Abscesses

AbstractThe intricate environment of biofilms provides a heaven for bacteria to escape antibiotic eradication, leading to persistent chronic infections. Therefore, it is urgently needed to develop effective therapies to combat biofilm‐associated infections. To address this problem, a series of antimicrobial agents are designed and synthesized utilizing triphenylamine imidazole silver complexes (TPIMS). Due to the photoactivated release of Ag+ coupled with aggregation‐induced emission (AIE) properties and efficient 1O2 generation, TPIMS exhibits excellent visual diagnostic capabilities and potent broad‐spectrum antimicrobial activity, showing antimicrobial efficacy against both Gram (+) and Gram (−) bacteria. Additionally, TPIMS shows extraordinary antibacterial performance and biofilm resistance against methicillin‐resistant Staphylococcus aureus (MRSA), with reduced potential for resistance thanks to the synergistic effect of phototoxicity and dark toxicity. Notably, among the TPIMS variants tested, TPIMS‐8 has demonstrated exceptional curative ability against resistant bacterial biofilm infections in vivo with minimal side effects. Furthermore, it is applied to clinical samples from infected patients and the results indicated that TPIMS‐8 is able to achieve excellent bacterial‐specific detection and superior killing of drug‐resistant bacteria even in complex systems, demonstrating its great potential for clinical applications. This study presents a promising foundation for the development of advanced antimicrobial therapeutics targeting multidrug‐resistant bacteria and biofilm‐associated infections.

Trace analysis using Wi-Fi probe positioning and virtual reality for commercial building complex design

The effective management of human flow is critical to the success of complex projects in urban centers, such as shopping malls. However, conventional methods of flow organization, which rely on observation and empirical knowledge, face challenges in fully comprehending the intricate environment. This study proposes an analytical approach to manage human flow in shopping malls in urban centers. A Wi-Fi probe positioning system recorded real-time human flow to assess current movement patterns, while virtual reality (VR) identified issues and optimized the space layout. The Maoye shopping mall in Nanjing experienced a decline in human flow, prompting the need for a new design. The study demonstrates that the Wi-Fi probe positioning system supports the setting of entrances and main corridors during the design phase, while VR tracing evaluates space layout and aids optimization. The study contributes to data-informed design by integrating analytical approaches into the conventional design process.

Aerodynamic Identification and Control Law Design of a Missile Using Machine Learning

The new generation of air vehicle is confronted with a more intricate environment and challenging missions, which puts forward higher requirements for the flight control system. In this study, the aerodynamic identification and control law design based on machine learning for a missile configuration is investigated through numerical simulations. The missile pitch and elevator deflection are realized via the combination of a rigid dynamic grid method and an overlapping grid technology, while the computational fluid dynamics/rigid body dynamics (CFD/RBD) strong coupling method is implemented to simulate the unsteady flows associated with the motion of the missile. Firstly, the aerodynamic data of the missile are gathered through forced pitching motion involving elevator deflection, and an aerodynamic model is constructed using a deep neural network to identify the aerodynamic moment with only a small number of unsteady aerodynamic data. Then, the accuracy and fidelity of the model are checked with the open-loop control law. Afterward, a missile pitch control law is generated through deep reinforcement learning based on the aerodynamic model, which enables the realization of a robust and exact angle-of-attack control process. Finally, the control law is transferred to a numerical environment and numerical virtual flight based on CFD is conducted, which demonstrates that stable control can be maintained even in continuous maneuvering. This study verifies the possibility of applying a deep neural network to air vehicle aerodynamic identification and deep reinforcement learning to a complicated flight control law design with excellent generalization ability. Machine learning is expected to play a significant role in the design and research for the novel generation of air vehicle.

Reinforcement Learning Inspired by Psychology and Neuroscience

Decision-making is a crucial intelligence shared by animals and humans and it helps them to act in an intricate environment to seek rewards and avoid punishments. Psychologists first became interested in this ability and studied the conditional behavioral problems associated with it, then these studies have further led to the need for unified quantitative explanation models, among which Reinforcement learning is still the most convincing and data-backed model today. The model itself, in turn, facilitates research in neuroscience. In this paper, the researcher first introduces the original framework of reinforcement learning and the potential neural correlates to it. Then the paper reviews new developments in reinforcement learning algorithms that address the limitations of the original model as well as variants further inspired by neuroscience. Finally, the study highlights some new directions for future research. This study focuses on the evolution of reinforcement learning algorithms inspired by neuroscience, shows the relationship of mutual promotion and common development between reinforcement Learning and neuroscience, and clarifies some concerns for future exploration.

Data-Driven Malware Detection for 6G Networks: A Survey From the Perspective of Continuous Learning and Explainability via Visualisation

5G is inherently prone to security vulnerabilities. We witness that many today's networks contain 5G security flaws due to their reliance on the existing 4G network core. A lack of security standards for 5G IoT devices means network breaches and malware threats might run uncontrolled. The future 6G network is predicted to be implemented with artificial intelligence-driven communication via machine learning, enhanced edge computing, post-quantum cryptography and so forth. With the activation of edge computing, the computing power available at supercomputing servers is to be integrated directly into the devices at the entry point of a network in a distributed manner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , antennas, routers, IoT sensors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> ). This feature brings an equal quality of service everywhere including remote regions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a.k.a</i> service everywhere) which will trigger an exponential growth of associated applications. In this intricate environment, malware attacks are becoming more challenging to detect. This paper thus reviews the theoretical and experimental data-driven malware detection literature, in the large-scale data-intensive field, relating to: (1) continuous learning, including new concepts in multi-domain to multi-target learning and the challenges associated with unseen/unknown data, imbalance data and data scarcity, and (2) new explainability via visualisation concepts with a multi-labelling approach which allows identifying malware by their recipes while improving the interpretability of its decision process.

Effect of rake face surface of cutting tool on tool crater wear

Tool wear is complex to predict due to the intricate environment of a machining process. However, there are a confrontational effect of tool wear on the machining process in terms of deprived surface finish, reduced dimensional accuracy and increased power consumption. In this study an attempt is made to examine the effect of rake face surface of the cutting tool on tool crater wear. For this, three different types; uncoated, coated and structured rake face cutting tools were used and cutting test were performed of plain carbon steel (AISI/SAE 4140). The cutting speed of 283 m/min, feed rate of 0.1 mm/min and depth of cut of 0.1 were used. Results show, structured rake face of the cutting tool benefitted most in supressing the tool crater wear. Energy-Dispersive X-Ray analysis (EDXA) analysis confirms reduction in iron transfer on tool rake face in case of coated and structured cutting tool. Micro − hardness test was also performed and the values in case of coated and structured cutting tool was found to be suffice. This study can be a benefit for cutting difficult to cut material where crater wear formation is unavoidable.

The systemic-level repercussions of cancer-associated inflammation mediators produced in the tumor microenvironment.

The tumor microenvironment is a dynamic, complex, and redundant network of interactions between tumor, immune, and stromal cells. In this intricate environment, cells communicate through membrane–membrane, ligand–receptor, exosome, soluble factors, and transporter interactions that govern cell fate. These interactions activate the diverse and superfluous signaling pathways involved in tumor promotion and progression and induce subtle changes in the functional activity of infiltrating immune cells.The immune response participates as a selective pressure in tumor development. In the early stages of tumor development, the immune response exerts anti-tumor activity, whereas during the advanced stages, the tumor establishes mechanisms to evade the immune response, eliciting a chronic inflammation process that shows a pro-tumor effect.The deregulated inflammatory state, in addition to acting locally, also triggers systemic inflammation that has repercussions in various organs and tissues that are distant from the tumor site, causing the emergence of various symptoms designated as paraneoplastic syndromes, which compromise the response to treatment, quality of life, and survival of cancer patients. Considering the tumor–host relationship as an integral and dynamic biological system, the chronic inflammation generated by the tumor is a communication mechanism among tissues and organs that is primarily orchestrated through different signals, such as cytokines, chemokines, growth factors, and exosomes, to provide the tumor with energetic components that allow it to continue proliferating. In this review, we aim to provide a succinct overview of the involvement of cancer-related inflammation at the local and systemic level throughout tumor development and the emergence of some paraneoplastic syndromes and their main clinical manifestations. In addition, the involvement of these signals throughout tumor development will be discussed based on the physiological/biological activities of innate and adaptive immune cells. These cellular interactions require a metabolic reprogramming program for the full activation of the various cells; thus, these requirements and the by-products released into the microenvironment will be considered. In addition, the systemic impact of cancer-related proinflammatory cytokines on the liver—as a critical organ that produces the leading inflammatory markers described to date—will be summarized. Finally, the contribution of cancer-related inflammation to the development of two paraneoplastic syndromes, myelopoiesis and cachexia, will be discussed.

The Application of Human Factors Engineering to Reduce Operating Room Turnover in Robotic Surgery.

Challenges associated with turnover time are magnified in robotic surgery. The introduction of advanced technology increases the complexity of an already intricate perioperative environment. We applied a human factors approach to develop systematic, data-driven interventions to reduce robotic surgery turnover time. Researchers observed 40 robotic surgery turnovers at a tertiary hospital [20 pre-intervention (Jan 2018 to Apr 2018), 20 post-intervention (Jan 2019 to Jun 2019)]. Components of turnover time, including cleaning, instrument and room set-up, robot preparation, flow disruptions, and major delays, were documented and analyzed. Surveys and focus groups were used to investigate staff perceptions of robotic surgery turnover time. A multidisciplinary team of human factors experts and physicians developed targeted interventions. Pre- and post-intervention turnovers were compared. Median turnover time was 67min (mean: 72, SD: 24) and 22 major delays were noted (1.1/case). The largest contributors were instrument setup (25.5min) and cleaning (25min). Interventions included an electronic dashboard for turnover time reporting, clear designation of roles and simultaneous completion of tasks, process standardization of operating room cleaning, and data transparency through monthly reporting. Post-intervention turnovers were significantly shorter (U = 57.5, p = .000) and ten major delays were noted. Human factors analysis generated interventions to improve turnover time. Significant improvements were seen post-intervention with a reduction in turnover time by a 26min and decrease in major delays by over 50%. Future opportunities to intervene and further improve turnover time include targeting pre- and post-operative care phases.

Nano Drug Delivery Platforms for Dental Application: Infection Control and TMJ Management-A Review.

The oral cavity is an intricate environment subjected to various chemical, physical, and thermal injuries. The effectiveness of the local and systemically administered drugs is limited mainly due to their toxicities and poor oral bioavailability that leads to the limited effectiveness of the drugs in the target tissues. To address these issues, nanoparticle drug delivery systems based on metals, liposomes, polymeric particles, and core shells have been developed in recent years. Nano drug delivery systems have applications in the treatment of patients suffering from temporomandibular joint disorders such as preventing degeneration of cartilage in patients suffering from rheumatoid arthritis and osteoarthritis and alleviating the pain along with it. The antibacterial dental applications of nano-drug delivery systems such as silver and copper-based nanoparticles include these agents used to arrest dental caries, multiple steps in root canal treatment, and patients suffering from periodontitis. Nanoparticles have been used in adjunct with antifungals to treat oral fungal infections such as candida albicans in denture wearers. Acyclovir being the most commonly used antiviral has been used in combination with nanoparticles against an array of viral infections such as the herpes simplex virus. Nanoparticles based combination agents offer more favorable drug release in a controlled manner along with efficient delivery at the site of action. This review presents an updated overview of the recently developed nanoparticles delivery systems for the management of temporomandibular joint disorders along with the treatment of different oral infections.