Search Results (319)

Energy consumption in hotels is strongly influenced by HVAC operation, lighting systems, and highly variable occupancy patterns. Internet of Things (IOT) technologies have been widely proposed to improve energy efficiency in building interiors; however, the maturity and practical applicability of this research remain unclear. This study presents a PRISMA-based systematic literature review of IOT-driven energy efficiency research in hospitality environments. A total of 1709 records were initially identified across Web of Science, Scopus, and Google Scholar, from which 60 peer-reviewed articles were selected for detailed analysis. Each study was evaluated using a three-dimensional research maturity assessment framework and a four-level ordinal scoring scale. The results indicated a moderate research maturity (average score 2.65/4), limited real-world implementation, and insufficient reporting of technological architectures and operational details required for replicability. These findings highlight the need for more rigorous empirical validation and clearer reporting standards to enable scalable adoption of IOT-based energy management in hospitality. Full article

Hydrogels have garnered significant attention due to their tunable structures and broad applicability in biomedical and smart materials. However, achieving a balance between excellent mechanical performance and multifunctionality remains a major challenge. In this study, a series of multifunctional polyurethane hydrogels (PUGs) was developed by integrating dynamic disulfide bonds and pendant tertiary amine groups into poly(ethylene glycol)-based networks using a solvent-exchange method. Structural characterization confirmed the successful formation of a crosslinked porous network. The hydrogels demonstrated remarkable mechanical properties, with PUG–II exhibiting a tensile strength of 448 kPa and an elongation at break of 489%, as well as exceptional compressibility (371 kPa at 90% strain) and fatigue resistance. Meanwhile, the PUGs displayed efficient room-temperature self-healing with a healing efficiency of up to 94.5%. The reversible protonation of tertiary amine groups imparted pronounced pH-responsive swelling behavior, with the equilibrium swelling ratio of PUG–I at pH 2.0 being 5.8 times higher than that at pH 12.0. This study provides a promising strategy for developing PU-based hydrogels that combine robust mechanical performance and multifunctionality, offering potential for advanced smart material applications. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

Reverberation and background noise remain persistent obstacles to achieving clear and intelligible speech in enclosed environments. Conventional data-driven or purely empirical dereverberation systems often perform well only under training conditions but lack robustness and physical interpretability when exposed to new acoustic spaces. To address these limitations, this paper proposes a physics-informed speech enhancement algorithm that integrates analytical room acoustics modeling with a descriptor-guided optimization framework. The method employs virtual field simulations based on the Helmholtz equation to estimate key acoustic descriptors, reverberation time (RT60), direct-to-reverberant ratio (DRR), and clarity index (C50), which are then used to adaptively control a model-informed dereverberation filter. This hybrid formulation bridges physical modeling and signal processing, allowing the algorithm to minimize late reverberation energy while maintaining spectral fidelity. Experimental results across multiple simulated and real-room conditions demonstrate measurable improvements over baseline methods, achieving average gains of +6.4 dB in SNR, +1.2 in PESQ, and +0.13 in STOI, along with reduced RT60 and enhanced clarity. The proposed approach offers both computational efficiency and interpretability, making it suitable for real-time deployment in teleconferencing, hearing-assistive, and smart audio applications. Full article

This study proposes a reproducible methodology for optimizing occupancy sensor placement and assessing the sustainable energy performance of smart lighting systems in university classrooms. The research was conducted in Block H of the South Campus of the Universidad Politécnica Salesiana, Quito, using one representative classroom for detailed geometric analysis and extending the optimization to eight classrooms with different dimensions, areas, and installed lighting configurations. The proposed framework integrates Voronoi-based spatial analysis, genetic algorithm optimization, and dynamic occupancy-based lighting control simulation as a retrofit-oriented strategy for existing educational buildings. For the representative classroom, the optimized sensor position was located near the geometric center of the room and achieved an estimated spatial coverage of 94.7% under the adopted sampling-based geometric model and an effective detection radius of 6 m. The multi-classroom analysis showed that the required number of sensors depends on classroom geometry and the adopted sensing radius; at R = 6 m, most classrooms satisfied the 90% coverage criterion with one sensor, while the largest classroom required two sensors. Based on occupancy schedules and automatic control rules, the dynamic simulation showed reductions in lighting operating time of 48% and 52% for 10 h and 12 h daily scenarios, respectively. These reductions were translated into lower daily and monthly energy consumption across different lighting configurations. The results indicate that optimized occupancy-based control can support sustainability-oriented energy management in university buildings by reducing unnecessary electricity use while preserving the existing lighting infrastructure. However, the results are limited to occupancy-based control and do not include daylight harvesting, photometric validation, or a complete economic payback assessment. Full article

Wormlike micelles (WLMs) of surfactants are widely used as smart thickeners in various applications, including enhanced oil recovery. However, their thickening ability needs to be improved both at ambient and elevated temperatures. In the present paper, we propose to enhance the viscoelastic properties of surfactant solutions by incorporating carboxymethylated cellulose nanocrystals (CNCs). Upon addition of CNCs, dilute solutions of short WLMs acquire viscoelasticity and then transition into a viscoelastic solid state. This process is accompanied by an increase in the viscosity and storage modulus by up to five and four orders of magnitude, respectively. The observed effect of CNCs on the storage modulus and viscosity is greater than that of any of the previously studied WLM-CNC systems. It is attributed to the formation of a network of fibrillar-like aggregates composed of WLMs and CNCs, which was confirmed by cryo-TEM data. To our knowledge, such kind of aggregates have not been observed before. When CNCs are added to a transient network of long entangled WLMs, the viscoelastic solution transitions into a viscoelastic solid state, which results in an increase in the viscosity and storage modulus by up to two orders of magnitude. CNCs provide the WLM solution with greater resistance to heating, such that the storage modulus remains almost unchanged when the temperature increases from 20 to 70 °C. Moreover, a heat-induced gelation was observed. It was shown that higher concentrations of nanocrystals lower the critical gel temperature, indicating that they promote the gelation of the mixture. SANS data revealed that the local structures of both micelles and nanocrystals are preserved in the mixed system upon heating. According to ITC data, at room temperature, the interaction between surfactant ions and similarly charged nanocrystals is governed by both enthalpy and entropy, which suggests that hydrogen bonding plays a major role in this process, although hydrophobic interactions may also be involved. When the temperature increases to 60 °C, the aggregation becomes entropy-driven, indicating that hydrophobic interactions begin to dominate. The results obtained can expand the range of practical applications of WLMs as thickening agents, in particular, to higher-temperature conditions in deeper oil wells. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

Photothermal superhydrophobic surfaces represent a promising solution for passive anti-icing; however, the persistent high solar absorption of static black coatings often leads to undesirable overheating under non-icing conditions. To address this limitation, we developed a smart superhydrophobic polydimethylsiloxane (PDMS) surface embedded with thermochromic capsules (TC) (S-PDMS/TC) featuring reversible thermochromic capability via a facile combination of spin-coating and femtosecond laser ablation. The resulting hierarchical micro-grid structure acts as a sacrificial layer, shielding fragile nanostructures against mechanical abrasion, while endowing the surface with robust superhydrophobicity (contact angle > 155°). Uniquely, S-PDMS/TC exhibits an adaptive color transition from pale yellow to deep black when the temperature drops below 5 °C. This response enables on-demand photothermal enhancement, significantly boosting solar absorption in freezing environments while minimizing heat absorption at room temperature. Consequently, S-PDMS/TC demonstrates superior anti-icing performance, extending the freezing time to 310 s and reducing ice adhesion strength to 40.4 kPa. Notably, during photothermal de-icing, the meltwater exhibits spontaneous dewetting behavior driven by the replenishment of the air cushion, effectively preventing secondary icing. This work presents a mechanically durable and intelligent strategy for ice protection, successfully balancing efficient de-icing with thermal management. Full article

While the global surgeon deficit continues to demand urgent action, traditional “over-the-shoulder” teaching is increasingly constrained by infection-control demands and crowded operating rooms. Over the past four years, we integrated head-mounted smart cameras into reconstructive-surgery workshops across East Africa. Utilizing voice-controlled, stabilized video technology, we provided trainees with a high-definition, wearer’s-perspective view that enhanced visualization without compromising the sterile field. Following remarkably high acceptance in Africa, we have initiated a pilot study at the National Burn Centre in Sweden to apply these lessons to a high-income setting. Our findings suggest that this technology improves surgical education while supporting infection-control stewardship through reduced overcrowding. This experience illustrates a reverse innovation, where tools refined under the logistical constraints of African operating theatres offer scalable solutions for universal challenges in surgical training and patient safety. Full article

Cold thermal energy storage (CTES) has emerged as a critical clean-energy technology for enhancing postharvest management in rural agricultural supply chains, where losses often exceed 20–40% due to inadequate cooling infrastructure and unreliable electricity. This review synthesizes the recent literature on CTES systems, including ice-, chilled-water-, and phase-change material (PCM)-based storage, with a focus on smart-farm integration, IoT-based monitoring, predictive control, and solar photovoltaic (PV) energy coupling. Trends in village-level cold rooms, micro-dairy milk cooling, and fruit–vegetable storage are critically examined, highlighting efficiency, resilience, and scalability relative to battery-dominant and conventional refrigeration systems. Current research gaps are identified in multi-scale modeling, PCM stability, state-of-charge estimation, techno-economic optimization, and AI-based operational strategies. Addressing these gaps is essential to realizing sustainable, low-carbon, and energy-efficient rural cold chains. Full article

The paper proposes an intelligent energy management system designed for a university laboratory room, centered on the user and based on the integration of smart sensors and fuzzy logic for the simultaneous optimization of thermal comfort and energy efficiency. The system architecture integrates three control methods, On/Off controller, Proportional Integral Derivative (PID) controller, and Fuzzy Logic, within a hybrid structure capable of managing multiple factors such as thermal comfort, energy consumption, and the availability of renewable energy sources. The system is implemented and tested using Zigbee 3.0 sensors, smart relays, and photovoltaic panels, while variables such as temperature, humidity, energy consumption, and user feedback are monitored. The simulation results, obtained in the MATLAB/Simulink development environment, demonstrate that the fuzzy algorithm reduces thermal oscillations, optimizes energy costs, and maintains perceived comfort within an optimal range. The main contribution of the study lies in the development of a user-centered, interpretable, and scalable architecture, along with a PowerApps application that records occupants’ feedback in real time, which can be implemented in smart buildings with limited computational resources. Two operating scenarios with different time periods were developed for the proposed system. The fuzzy controller maintained a mean temperature deviation below ±0.2 °C, reduced oscillatory behavior compared to PID controller, and enabled photovoltaic coverage of up to 29.97% during peak intervals, with an average daily contribution of 8.77%. The total simulated energy cost was 8.49 RON for the one-day scenario and 48.12 RON for the five-day interval. Full article

Smart mobility is a vital part of a smart city. Autonomous public transport buses are becoming an increasingly noticeable and significant component of smart mobility. This study examines and compares trials of autonomous public transport buses in Baltic countries (Estonia, Latvia, and Lithuania). This research covers the period from 2017 to 2024 and is based on qualitative research methods: case studies, secondary source analysis, conventional content analysis, and comparative analysis. This study found that Estonia was the first among the Baltic countries to begin testing autonomous public transport buses and was the most active, conducting as many as 11 trials. Moreover, Estonia tested autonomous buses at the highest speeds and over the longest distances. Despite relatively promising trials, autonomous public transport buses have encountered certain challenges and disruptions in all three countries. These results suggest that the Baltic States still have room for improvement in the field of smart mobility and autonomous public transport. Full article

The accurate positioning of location in indoor environment has become crucial in many location-based services, mainly where global positioning systems (GPSs) are unavailable or fail to navigate correctly. Conventional fingerprint-based approaches face challenges with instability, low accuracy, and being sensitive to changes in the environment. This study proposes a robust fingerprint-based machine learning (ML) model for dynamic environment indoor navigation in real time. The proposed model uses link quality indicator (LQI) values from IEEE 802.15.4 as fingerprints and supervised learning algorithms, showing high accuracy and a strong ability to adapt to changes in the environment. A room within a building floor has been regarded as the unit of location identification instead of the user’s exact coordinates to make the suggested model more relevant under practical conditions. The model was trained and tested using a real LQI dataset collected from varied indoor conditions to ensure the system can adapt effectively and operate consistently in dynamic environments and signal conditions. The results show that the proposed model surpasses fingerprinting indoor navigation in room detection accuracy and flexibility to environmental changes. An implemented prototype proved the real-time capability of the proposal in smart buildings, hospitals, and industrial IoT settings. Full article