Search Results (74)

Traditional firefighting protective clothing materials, such as meta- and para-aramid fibers, provide significant thermal protection but often fail to adequately manage heat stress and moisture, especially due to the incorporation of semi-permeable membranes within the three-layer garment structure known as turnout gear. Integrating hydrogels into textiles for firefighting personal protective clothing (PPC) could enhance thermoregulation and moisture management, providing firefighters with improved comfort and safety. Hydrogels are three-dimensional, hydrophilic polymer networks capable of holding substantial amounts of water. Their high water content and excellent thermal properties make them ideal for cooling applications. Therefore, this review focuses on the potential of hydrogel-infused textiles to improve firefighters’ PPC by enhancing thermal comfort and moisture management. Specifically, hydrogel structures and engineered properties for enhanced performance are presented, including smart hydrogels and hydration customization mechanisms. Hydrogel integration into firefighting PPC for moisture management and improved thermoregulation is explored, including current and future market projections and state-of-the-art clinical trial findings. Overall, the future of hydrogel-integrated textiles for firefighting PPC is bright, with numerous advancements and trends poised to enhance the safety, comfort, and performance of protective gear. Full article

The rapid advancement of wearable technology has created an increasing demand for efficient, high-performance energy storage systems that also offer key characteristics such as flexibility, lightweight, and durability. Among the emerging materials, polymer hydrogels have garnered significant attention due to their unique combination of viscoelasticity, low density, and tunable porous nanostructures. These materials exhibit adaptable surface and structural properties, making them promising candidates for next-generation flexible and wearable energy storage devices. This work provides an overview of recent progress and innovations in the application of polymer hydrogels for the development of flexible energy storage systems. The intrinsic three-dimensional architecture and porous morphology of these hydrogels offer a versatile platform for constructing high-performance supercapacitors, rechargeable batteries, and personal thermal management devices. Various types of polymer hydrogels and their principal fabrication methods are discussed in detail, along with the structural factors that influence their electrochemical and mechanical performance. Furthermore, recent advancements in integrating polymer hydrogel materials into wearable and flexible technologies—such as energy storage devices, thermal regulation systems, and multifunctional energy platforms—are comprehensively reviewed and analyzed. Full article

Background: Delayed Onset Muscle Soreness (DOMS) is a transient, exercise-induced condition characterized by muscle pain, stiffness, and functional impairment, particularly following eccentric or high-intensity physical activity. Recent advances in diagnostic imaging, neurophysiology, and therapeutic techniques have led to a reassessment of DOMS pathophysiology and management. Objective: This scoping review aims to critically evaluate non-pharmacological strategies for DOMS management, focusing on clinical studies published between 2020 and 2025. Emphasis is placed on physical, thermal, neurophysiological, and nutritional interventions in athletic populations. Methods: A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science. Included studies were randomized controlled trials, systematic reviews, meta-analyses, and high-quality scoping reviews. Methodological quality was assessed using PEDro, AMSTAR 2, and ROBIS tools. Key outcome measures included pain (VAS), functional recovery (ROM, performance), biochemical markers (CK, IL-6), and neuromuscular activation (iEMG). Results: Twenty-five studies met the inclusion criteria. Emerging strategies such as cryosauna, vibration therapy, percussive massage, and polyphenol supplementation demonstrated significant benefits in reducing DOMS-related symptoms and enhancing recovery. Evidence supports the integration of multimodal, personalized interventions over monotherapies. Imaging techniques (7T MRI, ultrasound) confirmed microstructural muscle changes consistent with DOMS, strengthening diagnostic precision. Conclusions: Non-pharmacological approaches to DOMS have evolved considerably, highlighting the importance of combining mechanical, thermal, and nutritional modalities. Personalized, multimodal recovery strategies appear most effective for symptom relief and performance restoration. Future studies should aim to standardize treatment protocols and outcome measures to improve clinical applicability. Full article

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions. Full article

Urban underground spaces, including tunnels, subways, and underground commercial buildings, have grown quickly as urbanization has progressed. Fires frequently break out following industrial accidents and multi-hazard natural disasters, and they can severely damage human health. Fire smoke is a major contributor and a major hazard to public safety. The flow patterns of fire smoke in underground spaces, the risks to human casualties, and engineering and personal protective technologies are all thoroughly reviewed in this work. First, it analyzes the diffusion characteristics of fire smoke in underground spaces and summarizes the coupling effects between human behavior and smoke spread. Then, it examines the risks of casualties caused by toxic gases, particulate matter, and thermal effects in fire smoke from both macroscopic case studies and microscopic toxicological viewpoints. It summarizes engineering protection strategies, such as optimizing ventilation systems, intelligent monitoring and early warning systems, and advances in the application of new materials in personal respiratory protective equipment. Future studies should concentrate on interdisciplinary collaboration, creating more precise models of the interactions between people and fire smoke and putting life-cycle management of underground fires into practice. This review aims to provide theoretical and technical support for improving human safety in urban underground space fires, thereby promoting sustainable urban development. Full article

In the context of thermal injury, local tissue integrity and systemic homeostasis are compromised, often resulting in delayed healing, infections, and disturbances of the skin and intestinal microbial balance. Despite several reviews addressing probiotics in wound healing, none has specifically focused on their role in thermal injuries and burn-associated pathophysiology. This review uniquely integrates evidence on the gut–skin axis, postbiotic innovations, and regenerative perspectives tailored to burn care. We conducted a critical synthesis of recent preclinical and clinical trials evaluating the use of probiotics and their derivatives to promote tissue regeneration following burn injury. Previous reviews have addressed probiotics in general wound repair, but the present synthesis advances the field by bridging mechanistic insights (immune modulation, angiogenesis, microbiome restoration) with translational evidence in burn patients, offering a framework for personalized regenerative approaches. Based on a structured review of the literature—including in vitro models, animal experiments, and randomized trials with topical, enteral, and systemic administration of probiotic—we identified four main mechanisms of action: modulation of the immune response by balancing cytokines and polarization of T lymphocytes; stimulation of tissue repair by increasing the proliferation of keratinocytes and fibroblasts, increased collagen synthesis, and induction of angiogenesis; direct antimicrobial activity against biofilms and multiresistant pathogens; and the restoration of eubiosis with the improvement of the function of epithelial barriers. While these findings endorse the adjunctive use of probiotics in burn management, large multicenter trials are required to standardize strains, dosages, and formulations before their routine clinical adoption. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This system innovatively integrates a patented evaporator-pump module and an optimized miniature rotary compressor, achieving a 151 W cooling capacity at 55 °C ambient temperature, surpassing existing portable systems in compactness and performance. Human trials with eight male participants at 35 °C (walking) and 40 °C (sitting) demonstrated that the liquid cooling garment system significantly improved thermal comfort. The mean thermal comfort vote decreased from 2.63 (uncomfortable) to 1.13 (slightly uncomfortable) while walking and from 3.88 (very uncomfortable) to 1.25 (slightly uncomfortable) while sitting. The mean skin temperature in the final stable state was reduced by 0.34 °C in walking trials and 1.09 °C in sitting trials, and heart rate decreased by up to 10.2 bpm in sedentary conditions. Comprehensive human trials under extreme heat further validate this system’s efficacy. This lightweight, efficient system offers a practical solution for personal thermal management in extreme high-temperature environments, with potential applications in industrial safety, military operations, and emergency response. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

Polylactic acid (PLA) fabrics exhibit significant sunlight reflectivity and high emissivity within the atmospheric window, making them suitable as the foundational material for this study. This research involves the modification of one side of the fabric with hydrophilic agents and titanium dioxide (TiO₂), while the opposite side is treated with MXene and subsequently coated with polydimethylsiloxane (PDMS) to inhibit oxidation of the MXene. Through these surface modifications, a thermal management fabric based on PLA was successfully developed, capable of passively regulating temperature in response to environmental conditions and user requirements. The study discusses the optimal concentrations of TiO₂ and MXene for the fabric, and characterizes and evaluates the functional surface of the PLA. Surface morphology analyses and tests indicate that the resulting functional PLA fabrics possess excellent ultraviolet (UV) resistance, favorable air permeability, high sunlight reflectivity on the TiO₂-treated side, and superior photothermal conversion capabilities on the MXene-treated side. Furthermore, photothermal effect tests conducted under a light intensity of 1000 W/m² reveal that the MXene-treated fabric exhibits a heating effect of approximately 25 °C, while the TiO₂-treated side demonstrates a cooling effect exceeding 5 °C. This study developed PLA functional fabrics with heating and cooling capabilities. Full article

Background/Objectives: Dry eye disease (DED) is a multifactorial condition with complex pathophysiology involving tear film instability, ocular surface inflammation, and nerve dysfunction. This review summarizes current evidence on the different available therapies targeting these mechanisms. Methods: A review of clinical studies evaluating treatment outcomes for therapies targeting aqueous tear deficiency, Meibomian gland dysfunction, ocular surface inflammation, and ocular pain was conducted, with an emphasis on randomized controlled trials and meta-analyses where available. Results: Artificial tears provide symptomatic relief with limited impact on tear film stability. Punctal plugs improve tear retention but show variable efficacy across studies. Treatments targeting MGD—such as lipid-based lubricants, eyelid hygiene, thermal pulsation (LipiFlow, iLux), and intense pulsed light (IPL)—demonstrate improvements in gland function, though outcomes vary. Anti-inflammatory agents including cyclosporine, lifitegrast, and short-term corticosteroids improve ocular surface signs, with mixed symptom relief. Biologic therapies like autologous serum tears and platelet-rich plasma show promise for both signs and symptoms, but data remain inconsistent. Nerve-targeted therapies, including oral neuromodulators (gabapentin, antidepressants), botulinum toxin, and transcutaneous nerve stimulation, have shown potential for managing neuropathic ocular pain, although randomized data are limited. Overall, variability in study designs, patient populations, and outcome measures highlights the need for more rigorous research. Conclusions: Personalized, mechanism-based treatment strategies are essential for optimizing outcomes in DED. Future research should prioritize well-designed, controlled studies to clarify the role of emerging therapies and guide the individualized management of this heterogeneous condition. Full article

Antimicrobial resistance arises from treatment non-adherence and ineffective delivery systems. Optimal wound dressings combine localized drug release, exudate management, and bacterial encapsulation through hydrogel-forming nanofibers for enhanced therapy. In this work, polylactic acid (PLA) and polyvinylpyrrolidone (PVP) fibers loaded with chlorhexidine (CHX) were developed using Solution Blow Spinning (SBS), a scalable electrospinning alternative that enables in situ deposition. Molecular interactions between CHX and polymers in solution (by UV-Vis and fluorescence spectroscopy) and in solid state (by FTIR, XRD and thermal analysis) were studied. The morphology of the polymeric fibers was determined by optical microscopy, showing that PVP fibers are thinner (1625 nm) and more uniform than those of PLA (2237 nm). Finally, drug release from single-polymer fibers discs, overlapping fibers discs (PLA/PVP/PLA and PVP/PLA/PVP), and solid dispersions was determined by UV-Vis spectrometry. PVP-based fibers exhibited faster CHX release due to their hydrophilic nature, while PLA fibers proved sustained release, attributed to their hydrophobic matrix. This study highlights the potential of PLA/PVP-CHX fibers made from SBS as advanced wound dressings, combining biocompatibility and personalized drug delivery, offering a promising platform for localized and controlled antibiotic delivery. Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

Textile structures with ohmic (Joule) heating capability are frequently used for personal thermal management by tuning fluctuations in human body temperature that arise due to climatic changes or for medical applications as electrotherapy. They are constructed from electrically conductive textile structures prepared in different ways, e.g., from metallic yarns, conductive polymers, conductive coatings, etc. In comparison with other types of flexible ohmic heaters, these structures should be corrosion resistant, air permeable, and comfortable. They should not loose ohmic heating efficiency due to frequent intensive washing and maintenance. In this study, the basic electrical properties of a conductive fabric composed of a polyester/cotton fiber mixture and a small amount of fine stainless-steel staple fibers (SS) were evaluated and predicted. Even though the basic conductive component of SS fibers is iron and its electrical characteristics obey Ohm’s law, the electrical behavior of the prepared fabric was highly nonlinear, resembling a more complex response than that of a classical conductor. The non-linear behavior was probably due to non-ideal, poorly defined random interfaces between individual short SS fibers. A significant time–dynamics relationship was also shown. Using the Stefan–Boltzmann law describing radiation power, we demonstrated that it is possible to predict surface temperature due to the ohmic heating of a fabric related to the input electrical power. Significant local temperature variations in the heated hybrid fabric in both main directions (warp and weft) were identified. Full article

Thermal and moisture properties of the textile materials worn in close contact with the skin greatly contribute to the comfort of the workwear and of the personal protective clothing (PPC) assemblies they are part of. This study examines in depth the thermoregulatory properties of eighteen knitted fabrics used in polo shirts and T-shirts, which function as thermal underwear, standard workwear compliant with various regulations, or as base layers in PPC systems. Most of the fabrics specifically engineered for heat protection demonstrated superior air permeability (ranging from 700 to 1200 mm/s) and efficient moisture management (OMMC 0.5–0.7). Their drying time varied between 12 and 18 min, comparable to most commodity fibre blend fabrics investigated. Generally, the heat-protective fabrics were heavier and exhibited greater thermal and vapour resistance. However, despite minor variations in predicted thermal comfort, seventeen of the fabrics were classified in the same cluster. These findings offer valuable insights into the thermal and moisture management properties of knitted fabrics with various levels of protection, and the correlations found between their thermoregulatory and physical properties, such as mass and thickness, provide guidance for the development of innovative knitted materials for workwear that enhance wearer comfort. Full article