Search Results (18,024)

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

Soil respiration (Rs) plays a pivotal role in carbon cycling within semi-arid ecosystems. In our millet field experiment, we measured Rs, autotrophic respiration (Ra), heterotrophic respiration (Rh), water consumption (ET), yield (Y), water use efficiency (WUE), and key soil environmental properties to examine the effects of supplemental irrigation and selenium application on Rs dynamics and to clarify the controlling factors. The experiment was conducted from 2023 to 2024 with four treatments and three replicates per treatment each year. These treatments comprised conventional rainfed (CK), supplemental irrigation (SI, 50 mm), rainfed with Se addition (CS, 67.84 g·hm⁻²), and supplemental irrigation with Se addition (SIS). SI increased CO₂ emissions in the millet field, whereas selenium application (CS) suppressed them. Ra was the dominant component of Rs and was 1.03–4.01 times greater than Rh. SI and CS significantly affected cumulative CO₂ emissions through Ra (p < 0.05), whereas their effects on Rh were minor. The CS treatment resulted in the lowest cumulative CO₂ emissions at 4233 and 4009 g·m⁻² in 2023 and 2024, respectively. Diurnal variation patterns of Rs, Ra, and Rh differed across millet growth stages. Both supplemental irrigation and selenium application improved soil water retention, soil enzyme activity, and soil organic matter (SOM), and moderated soil temperature. Classification and Regression Tree (CART) algorithm analysis revealed that Ra was primarily driven by soil temperature, with a feature weight of 86.95% determined by CART based on machine learning, whereas Rh was mainly influenced by soil enzyme activity, with a feature weight of 76.11%. The CS treatment enhanced production while promoting emission mitigation. The combined SIS treatment achieved the highest WUE and maintained a lower Rs than SI. These findings suggest an environmentally sustainable management strategy for millet production in semi-arid regions. However, due to the limited number of parcels in this study, further field-scale validation and additional experimental research involving multiple levels of supplemental irrigation and Se addition are necessary. Full article

Yamakagashi (Rhabdophis tigrinus) is a widely distributed snake species in Japan. Yamakagashi causes venom-induced consumption coagulopathy (VICC) when the amount of infused venom is high, and bites can be fatal if antivenom treatment is delayed. However, yamakagashi antivenom is an unapproved treatment, and its storage capacity is limited, preventing its prompt administration. Therefore, we investigated the application of commercially available drugs, namely tranexamic acid and antithrombin III, in the treatment of VICC caused by yamakagashi venom in a rat model. Furthermore, we investigated the combination of each drug with recombinant thrombomodulin α. Administration of tranexamic acid or antithrombin III alone failed to extend rat survival or correct changes in blood coagulation markers, such as prothrombin time, fibrinogen concentrations, and D-dimer levels, in yamakagashi venom-treated rats. However, combined administration of recombinant thrombomodulin α and tranexamic acid extended rat survival and partially restored blood coagulation markers. Therefore, the combination of recombinant thrombomodulin α and tranexamic acid might represent a useful therapeutic regimen for yamakagashi venom exposure. Full article

The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow geothermal energy is emerging as an efficient and renewable solution for sustainable heating and cooling. To address these gaps, this study examines the potential of GeoBIM, the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), as a unified framework for multiscale energy analysis and for supporting shallow geothermal applications. A systematic literature review was conducted based on the PRISMA framework, combining a systematic literature review using the Scopus database with the critical examination of representative case studies. The results show that GeoBIM-based modeling improves data quality, enhances thermal performance assessments, and supports the implementation of shallow geothermal systems, including energy piles and district-scale ground-coupled networks. Reported applications demonstrate energy consumption reductions exceeding 40% in certain urban contexts. Several research gaps and challenges were identified, particularly data interoperability issues, lack of standardization, computational complexity, and the need for specialized training. Overall, the review indicates that GeoBIM offers a promising pathway for optimizing resources, supporting informed decision-making, and advancing resilient and sustainable smart buildings and cities. 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

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

Background: Overweight and obesity are emerging public health concerns among young adults. However, evidence on associated sociodemographic, dietary, and lifestyle factors among young industrial workers in low- and middle-income countries remains limited. This study aimed to identify factors associated with overweight and obesity among Vietnamese young industrial workers aged 18–30 years. Methods: A cross-sectional study was conducted among 2295 young industrial workers (55.6% men and 44.4% women) recruited from factories and industrial zones in three geographic regions of Vietnam. Sociodemographic characteristics, dietary habits, lifestyle behaviors, and physical activity were assessed using a structured questionnaire. Body mass index (BMI) was calculated from self-reported height and weight and classified using WHO Western Pacific Region (WPRO) cut-offs; overweight/obesity was defined as BMI ≥ 23.0 kg/m². Physical activity was assessed using the International Physical Activity Questionnaire—Long Form (IPAQ-LF) and categorized by total MET-min/week according to IPAQ scoring guidelines. Logistic regression analyses were performed to estimate crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Results: Overall, 10.4% of participants were overweight (BMI 23.0–24.9 kg/m²) and 7.0% were obese (BMI ≥ 25.0 kg/m²), yielding a combined prevalence of 17.4%. After multivariable adjustment, increasing age (aOR = 1.15; 95% CI: 1.10–1.20), male sex (aOR = 2.10; 95% CI: 1.59–2.76), and regular alcohol consumption (aOR = 1.37; 95% CI: 1.04–1.81) were independently associated with higher odds of overweight/obesity, while residence in the Southern region was inversely associated (aOR = 0.57; 95% CI: 0.42–0.76). High total physical activity (vs. low activity) was positively associated with overweight/obesity, whereas moderate physical activity was not independently associated. Other dietary behaviors were not significantly associated after adjustment. Conclusions: Among Vietnamese young industrial workers, overweight and obesity were associated with age, sex, alcohol consumption, and geographic region. The observed association with high total physical activity likely reflects the occupational context of physical activity in this population, highlighting the importance of distinguishing between occupational and leisure time physical activity when interpreting physical activity obesity relationships. These findings underscore the relevance of early, workplace relevant prevention strategies targeting modifiable behaviors, particularly alcohol use. Full article

Background/Objectives: Recent guidelines have emphasized the importance of early mobilization and rehabilitation of patients following cardiac surgery. However, studies on the optimal targets and prescription methods for phase I cardiac rehabilitation (CR) are lacking. This study aimed to evaluate the feasibility and utility of an early phase 1 submaximal cardiopulmonary exercise test (CPET) using a recumbent ergometer in patients who have undergone cardiac surgery. Methods: Twenty ambulatory patients who underwent cardiac surgery between December 2021 and February 2023 were referred to the CR department on the fifth postoperative day, and a CR program was initiated. The program was conducted five times a week, with hour-long sessions consisting of warm-up exercises, resistance training, aerobic exercises, and a cool-down period. A recumbent ergometer-based submaximal CPET was performed approximately nine days after the surgery, prior to discharge. Participants initiated the test at 0 W, and the workload was increased by 20 W after 2 min. During the test, researchers evaluated parameters including submaximal peak values of oxygen consumption (VO₂), metabolic equivalents of task, respiratory exchange ratio (RER), blood pressure, heart rate (HR), and rating of perceived exertion (RPE). The grip strength test, 6 min walk test (6MWT), Korean Activity Scale/Index (KASI), EuroQol-5 dimension (EQ-5D), and short-form 36-item health survey (SF-36) values were also measured prior to discharge. Results: Twenty patients (75% male, average age 62.50 ± 1.99 years) underwent CPET at a median of 9.0 (8.0; 12.5) days postoperative. The average exercise duration of the CPET was 411.75 ± 168.25 s. During the test, their submaximal peak VO₂ was 12.32 ± 0.75 mL/kg/min (corresponding to 46.65 ± 2.08% of VO₂ max). The submaximal peak RER was 1.01 (0.98–1.12), and the submaximal peak RPE was 15.00 ± 0.51. Furthermore, the submaximal peak HR was 111.8 ± 3.76 beats/min (equivalent to 70.95 ± 2.09% of age-predicted maximal HR). After adjustment for age and sex, statistically significant positive correlations were observed between the submaximal peak VO₂ and 6MWT, squat endurance test, KASI, EQ-5D, and the physical component summary (PCS) of the SF-36 questionnaire. The 6MWT, squat endurance test, KASI, and PCS of SF-36 showed a correlation coefficient (r) of 0.522 (p = 0.026), 0.628 (p = 0.005), 0.586 (p = 0.011), and 0.546 (p = 0.019), respectively. No significant cardiac events, such as ST elevation/depression or hemodynamic instability, were observed during the test. Conclusions: Our findings suggest that performing recumbent ergometer-based CPET during early phase 1 CR is safe and feasible. These results highlight the potential of recumbent ergometer-based CPET as a valuable tool for guiding the appropriate prescription of early CR programs following hospital discharge in patients undergoing cardiac surgery. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and material consumption. To address this challenge, this study proposes an artificial neural network (ANN) predictive model with 5-fold cross-validation to estimate compressive strength performance and to develop mix design recommendations based on actual and predicted results. A total of 55 experimental samples were prepared and grouped into 11 batches, with the EPS volume replacement levels ranging from 0% to 50% at 5% increments. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R²), and scatter index (SI), with graphical representations like predicted vs. actual plots, response plots, and residual plots, and the results were benchmarked against a multiple linear regression (MLR) model. Among the tested configurations, the 4-5-1 ANN model demonstrated the highest predictive accuracy. Furthermore, a Shapley (SHAP) analysis was conducted to interpret the model behavior and determine the relative importance of the input variables. The findings reveal that EPS content had the greatest influence on compressive strength prediction, followed by slump value, then gravel content, and finally concrete density. Full article

Objective: This study compared the clinical and patient-reported outcomes of Er: YAG laser-assisted versus conventional scalpel frenectomy, while evaluating the adjunctive impact of postoperative antimicrobial mouthwashes on wound healing and periodontal parameters. Methods: A total of 102 patients who underwent labial frenectomy were included in this retrospective longitudinal cohort study. Participants were allocated into four groups based on the surgical approach (Er: YAG laser or conventional scalpel) and the postoperative mouthwash protocol (Kloroben^® or Klorhex Plus^®). Clinical assessments were performed at baseline and at 7, 14, and 28 days postoperatively. Wound healing, evaluated using the Wound Healing Index, was defined as the primary outcome. Secondary outcomes included periodontal clinical parameters, epithelialization status, postoperative pain, bleeding, and analgesic consumption. To control potential confounders, multivariable regression analysis was performed alongside standard parametric and nonparametric tests, with p < 0.05 considered statistically significant. Results: All treatment protocols resulted in significant improvements over time (p < 0.001). However, Er: YAG laser–assisted frenectomy was associated with significantly better periodontal indices, superior wound-healing scores, and more favorable patient-reported outcomes than the conventional scalpel technique at all postoperative evaluations (p < 0.001). On day 7, ‘Very Good’ healing was observed in 70.2% of the laser groups, compared with 14.4% in the CS groups (p = 0.001). Group 4 showed the lowest mean VAS scores (0.04 ± 0.20) and the lowest analgesic consumption by day 7. Multivariable analysis confirmed that the surgical technique was the strongest independent predictor of superior wound healing (p < 0.05), regardless of age, gender, smoking, or systemic disease. Notably, frenulum type was not significantly associated with wound healing or pain outcomes (p > 0.05). Conclusions: Within the limitations of this study, Er: YAG laser-assisted frenectomy was observed to provide favorable wound healing outcomes compared to the conventional technique. Furthermore, our findings show that anatomical variations in frenulum type do not significantly influence the quality or speed of recovery. These findings suggest that the choice of surgical modality and postoperative chemical support are more critical determinants of early clinical success than the anatomical variations of the frenulum. Full article

With the widespread adoption of electric vehicles, predicting user charging consumption can enhance the operational efficiency of charging infrastructure. However, differences in user charging habits result in charging station operators obtaining data that is non-continuous and event-driven, lacking internal battery state information. This makes traditional methods difficult to apply directly. This paper explores how to accurately predict user charging consumption based on non-continuous observation data from charging stations. To this end, we propose a three-stage solution: (1) Design a method for segmenting the temporal sequence of users’ internal charging behavior based on statistical significance testing, enabling unsupervised recognition of homogeneous sequences of user behavior patterns; (2) establish a continuous-time reconstruction mechanism based on a physics-inspired power decay model to convert discrete homogenous sequences into equidistant daily sequences of charging consumption; (3) utilize seasonal and trend decomposition using Loess (STL) time-series decomposition to extract the component from the reconstructed sequence and input it as a feature into the Long Short-Term Memory (LSTM) prediction model. Through experimental validation using real charging data, the proposed method significantly enhances prediction performance, providing an effective solution for forecasting user charging consumption in actual charging stations. Full article

This work presents the real-world deployment of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent energy control in resource-constrained IoT devices. The proposed system employs a first-order Takagi–Sugeno fuzzy model with three Gaussian membership functions per input: ambient temperature, light intensity, and battery voltage. The model was trained offline using augmented environmental datasets and subsequently translated into optimized embedded C code for execution on an ESP32 microcontroller. The controller dynamically adjusts the node’s deep sleep duration according to environmental conditions, enabling adaptive behavior based solely on local environmental conditions without requiring external connectivity. A 10-day field deployment compared the ANFIS controller with conventional fixed and rule-based strategies. Results show that the ANFIS-based strategy reduced energy consumption by 31.1% relative to the fixed approach while maintaining accurate adaptation to environmental conditions (RMSE = 9.6 s). The inference process required less than 2.5 ms and used under 30 KB of RAM, confirming the feasibility of real-time fuzzy inference on resource-constrained embedded platforms. Full article

In developing countries, sustainable development strategies are increasingly shifting toward a green economy that integrates economic, social, and environmental dimensions. Despite the growing importance of green economic growth, comparative empirical studies examining its determinants in Turkey and Romania remain limited. This study investigates the dynamic relationships between environmentally sustainable growth, carbon emissions, life expectancy, renewable energy consumption, education, and technological innovation in Turkey and Romania over the period 1980–2023. Using annual time series data, the analysis applies the Augmented Dickey–Fuller and Zivot–Andrews unit root tests to examine stationarity and potential structural breaks. The empirical framework is based on the Autoregressive Distributed Lag (ARDL) bounds testing approach, which allows the estimation of both long-run equilibrium relationships and short-run dynamics. The results provide partial evidence of long-run relationships among the variables. Although the ARDL bounds test results fall within the inconclusive region, the negative and statistically significant error correction terms indicate that deviations from long-run equilibrium are corrected over time. The findings also reveal heterogeneous short-run causal interactions across the two countries, suggesting that the drivers of environmentally sustainable growth differ between Turkey and Romania. Overall, the results highlight the importance of country-specific policy frameworks, institutional structures, and energy transition pathways in promoting green economic growth. Full article

This study investigates how key planning variables—the number of wood assortments, the geometric shape of clear-cut areas, and the extraction (forwarding) distance—influence greenhouse gas (GHG) emissions. Twelve plots formed a heterogeneous sample with similar site type and soil moisture conditions. A Komatsu 931 harvester and a 855 forwarder, driven by the experienced operators, were used to ensure consistency in operator skill. For each plot, the isoperimetric quotient was computed to quantify how plot shape correlated with labor hours, fuel consumption, and the resulting volume of GHG emitted. The number of assortments extracted per plot ranged from three to fourteen product groups. The results show that plots with more complex shapes require significantly more operator time and fuel. Increasing the number of assortments amplifies handling time and fuel use. Longer extraction distances further exacerbate the emissions. These findings underscore the importance of integrating spatial geometry and wood assortment planning into harvest scheduling to enhance productivity and reduce the carbon footprint of forest operations. Recommendations for practitioners include prioritizing more compact treatment units, optimizing assortment grouping, and minimizing extraction distances as key strategies for precision forestry. Full article