Search Results (3,995)

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Background/Objectives: Radial head arthroplasty is a commonly preferred treatment for complex, unreconstructable radial head fractures. Recent studies have raised the question of whether factors such as bone resorption may be related to failure. This observational, retrospective, multicenter, spontaneous, and non-profit study aims to assess radiological outcomes, focusing on bone resorption around the stem, for radial head replacement using a modular, cementless radial head prosthesis with interconnected porosity. Methods: A series of 42 cases was available for review. Patients underwent radial head arthroplasty using a three-dimensional-printed radial head prosthesis. Patients were eligible for inclusion if they had undergone at least one follow-up between 6 and 15 months post-operatively. A scoring system to detect bone resorption was developed and administered by two independent evaluators. Results: Forty-two patients (14 males, 28 females), with an average age of 59 ± 11 years (range: 39–80 years), were analyzed with a minimum of six months’ and a maximum of 32 months’ follow-up. At follow-up, 50 radiological evaluations were collected, with 29 showing ≤3 mm and 12 showing 3–6 mm resorption around the stem. The average resorption was 3.5 mm ± 2.3. No correlation was found between the extent of resorption and the time of follow-up. The developed scoring system allowed for a high level of correlation between the evaluators’ measurements of bone resorption. Conclusions: Radial head prosthesis with interconnected porosity provided a low stem resorption rate for patients after a radial head fracture at short-to-mid-term follow-up after the definition of a reliable and easy-to-use radiological-based classification approach. (Level of Evidence: Level IV). Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

Refractory full-thickness macular holes (rFTMHs) present a significant challenge in vitreoretinal surgery, with reported incidence rates of 4.2–11.2% following standard vitrectomy with internal limiting membrane (ILM) peeling and gas tamponade. Risk factors include large hole size (>400 µm), chronicity (>6 months), high myopia, incomplete ILM peeling, and post-operative noncompliance. Multiple surgical techniques exist, though comparative evidence remains limited. Current options include the inverted ILM flap technique, autologous ILM transplantation (free flap or plug), lens capsular flap transplantation (autologous or allogenic), preserved human amniotic membrane transplantation, macular subretinal fluid injection, macular fibrin plug with autologous platelet concentrates, and autologous retinal transplantation. Closure rates range from 57.1% to 100%, with selection depending on hole size, residual ILM, patient posturing ability, etc. For non-posturing patients, fibrin plugs are preferred. Residual ILM cases may benefit from extended peeling or flap techniques, while large holes often require scaffold-based (lens capsule, amniotic membrane) or fibrin plug approaches. Pseudophakic patients should avoid posterior capsular flaps due to lower success rates. Despite promising outcomes, the lack of randomized trials necessitates further research to establish evidence-based guidelines. Personalized surgical planning, considering anatomical and functional goals, remains crucial in optimizing visual recovery in rFTMHs. Full article

As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not direct attacks—to evaluate and compare explainable, interpretable, and opaque machine learning models. Through advanced preprocessing and feature engineering, we examine the trade-off between model performance and transparency in the early detection of suspicious connections. We evaluate explainable ML-based models such as k-nearest neighbours, fuzzy algorithms, decision trees, and random forests, alongside interpretable models like naïve Bayes, support vector machines, and non-interpretable algorithms such as neural networks. Results show that neural networks achieve the highest performance, with a macro F1-score of 0.8786, but explainable models like HFER offer strong performance (macro F1-score = 0.6106) with greater interpretability. The choice of algorithm depends on project-specific needs: neural networks excel in accuracy, while explainable algorithms are preferred for resource efficiency and transparency, as stated in this work. This work underscores the importance of aligning cybersecurity strategies with operational requirements, providing insights into balancing performance with interpretability. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Background/Objectives: Excessive sugar intake through sugar-sweetened beverages (SSBs) has raised global concerns due to its association with various health risks. This study evaluates the effectiveness of nudges—in the form of order placement, variety expansion, and a combination of both—in promoting non-SSB purchases at self-service kiosks, a key environment for SSB consumption. Methods: This study was conducted using a real-world kiosk at food and beverage outlets in South Korea from 28 May to 12 July, 2024. A total of 183 consumers aged 19 to 29 participated in this study. A single kiosk device was used with four screen layouts, each reflecting a different nudge strategy. Participants were unaware of these manipulations when making their purchases. After their purchases, participants completed a survey. All data were analyzed using IBM SPSS Statistics for Windows, Version 29.0. Results: Females reported significantly higher positive attitudes, preferences, and perceived necessity regarding nudges compared to males. In particular, both the single (variety) and combination (order and variety) nudges received positive responses from females (p < 0.001). The combination nudge significantly increased non-SSB purchases compared to the control (p < 0.05) and single (order) nudge groups (p < 0.01), which suggests that combination nudge is effective in promoting healthier beverage choices. Females were also more likely to purchase non-SSBs than males (p < 0.05). Conclusions: The findings suggest that the combination nudge strategy effectively promotes healthier beverage choices in real kiosk settings. Notably, females demonstrate significantly higher positive attitudes, preferences, and perceived necessity regarding nudges compared to males, and are also more likely to purchase non-SSBs. These findings offer valuable insights for real-world applications aimed at encouraging healthier consumption behaviors. Full article

Automated vehicles controlled by artificial intelligence are becoming capable of making moral decisions independently. This study investigates the differences in participants’ perceptions of the moral decision-maker’s permissibility when viewing scenarios (pre-test) and after witnessing the outcomes of moral decisions (post-test). It also investigates how permissibility, ten typical moral emotions, and perceived moral agency fluctuate when AI and the human driver make deontological or utilitarian decisions in a pedestrian-sacrificing dilemma (Experiment 1, N = 254) and a driver-sacrificing dilemma (Experiment 2, N = 269) from a third-person perspective. Moreover, by conducting binary logistic regression, this study examined whether these factors could predict the non-decrease in permissibility ratings. In both experiments, participants preferred to delegate decisions to human drivers rather than to AI, and they generally preferred utilitarianism over deontology. The results of perceived moral emotions and moral agency provide evidence. Moreover, Experiment 2 elicited greater variations in permissibility, moral emotions, and perceived moral agency compared to Experiment 1. Moreover, deontology and gratitude could positively predict the non-decrease in permissibility ratings in Experiment 1, while contempt had a negative influence. In Experiment 2, the human driver and disgust were significant negative predictor factors, while perceived moral agency had a positive influence. These findings deepen the comprehension of the dynamic processes of autonomous driving’s moral decision-making and facilitate understanding of people’s attitudes toward moral machines and their underlying reasons, providing a reference for developing more sophisticated moral machines. Full article

Cuscohygrine (CUS) and hygrine (HYG) are pyrrolidine alkaloids proposed as biomarkers of coca leaf consumption, a culturally accepted practice in some Latin American countries. Differentiating legal coca use from illicit cocaine consumption holds forensic importance. While LC-MS/MS is preferred, GC-MS remains widely used in Latin American toxicology labs due to accessibility. This study critically evaluates the analytical limitations of GC-MS for detecting CUS and HYG in biological matrices. Key parameters—injector temperature (180–290 °C), injection mode (split/splitless), solvent, liner condition, and matrix—were systematically studied. GC-MS showed significant limitations: low-abundance, non-specific fragments (m/z 42, 84, 98, 140) failed to meet the identification criteria in SIM mode. Thermal degradation of CUS to HYG and CUS-d6 to HYG-d3 was observed, especially with splitless injection and aged liners. Matrix effects produced signal enhancement ranging from +29% to +316%, meaning that analyte responses in biological samples were significantly higher than in neat standards, likely due to reduced degradation or adsorption. Although deuterated internal standards (CUS-d6) partially corrected signal variability and matrix enhancement, these corrections were not sufficient to overcome the fundamental limitations of GC-MS, including poor ion specificity and compound instability. These findings support the need for LC-MS/MS-based approaches for reliable alkaloid detection and question the suitability of GC-MS for CUS analysis in forensic toxicology contexts. Full article

The investigation of the reward system is a fascinating domain with future applications for pain therapy and understanding addiction. We investigated interactions between tramadol use and the imidazoline system, through the modulatory effects of imidazoline receptor blockers, by behavior analysis and electroencephalography (EEG). Thirty-six male Wistar rats were placed within a conditioned place preference (CCP) setting using a three-compartment box apparatus. The transition of the six groups of subjects from one compartment to another was constantly monitored, related to preconditioning for one day, conditioning for eight days, and post-conditioning testing on day 10. During the conditioning phase, the groups received: a saline solution, efaroxan, idazoxan, tramadol, tramadol + efaroxan, and tramadol + idazoxan, respectively. The administration of efaroxan, idazoxan, or a saline solution in the non-preferred compartment did not alter the time spent by rats there. On the other hand, the administration of tramadol alone in the non-preferred compartment significantly increased the time spent by animals there (151.66 ± 11.69 s) post-conditioning as compared to preconditioning (34.5 ± 5.31 s) (p < 0.01), while the combination of efaroxan and tramadol significantly reduced its effect. After the combination with idazoxan, the effect of tramadol on increasing the time spent by the animal in the non-preferred compartment remained significantly higher than in the preconditioning phase. A significant increase in time spent in the non-preferred compartment demonstrates the existence of a CPP induction effect (by changing the preference). The effects of tramadol on the reward system can cause changes in the brain’s neuroplasticity, potentially leading to learned behaviors that promote drug seeking in previous non-preferred environments. Full article

The intrinsic relationship between food and health has led to growing interest in functional foods, particularly among athletes seeking to optimize performance and recovery. This study investigates the impact of product information and sensory attributes on athletes’ willingness to pay for an innovative high-protein bread. Utilizing a two-treatment experimental design, athletes were exposed to sensory evaluations either before or after receiving information. A combination of hedonic sensory analysis and economic evaluation assessed preferences through a non-hypothetical auction. Findings show that both sensory attributes—especially taste and aroma—and product information significantly influenced willingness to pay. The order of presentation played a crucial role: providing information first enhanced perceived value more strongly. While sensory evaluation moderately increased willingness to pay, product information had a stronger impact. A key contribution of this study is its novel evidence on how athletes balance sensory and informational cues in food evaluation—an aspect rarely explored. Contrary to assumptions that athletes ignore sensory quality due to their focus on nutrition, they did value sensory aspects, though they prioritized product information. These findings suggest that developing functional foods for athletes should integrate nutritional benefits and sensory appeal, as both elements contribute to acceptance and potential market success. Full article

The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. Full article

Background/Objectives: The extensive use of computed tomography (CT) has led to a significant increase in the detection of small and non-palpable pulmonary nodules, necessitating the use of invasive methods for definitive diagnosis. Video-assisted thoracoscopic surgery (VATS) has become the preferred procedure for nodule resections; however, intraoperative localization remains challenging, especially for deep or subsolid lesions. This study explores whether SPECT/CT improves the technical and clinical outcomes of radio-guided occult lesion localization (ROLL) before uniportal video-assisted thoracoscopic surgery (u-VATS). Methods: This is a retrospective study involving consecutive patients referred for the resection of pulmonary nodules who underwent CT-guided ROLL followed by u-VATS between September 2017 and December 2024. From January 2023, SPECT/CT was systematically added after planar imaging. The cohort was divided into a planar group and a planar + SPECT/CT group. The inclusion criteria involved nodules sized ≤ 2 cm, with ground glass or solid characteristics, located at a depth of <6 cm from the pleural surface. ^99mTc-MAA injected activity, timing, the classification of planar and SPECT/CT image findings (focal uptake, multisite with focal uptake, multisite without focal uptake), spillage, and post-procedure complications were evaluated. Statistical analysis was performed, with continuous data expressed as the median and categorical data as the number. Comparisons were made using chi-square tests for categorical variables and the Mann–Whitney U test for procedural duration. Cohen’s kappa coefficient was calculated to assess agreement between imaging modalities. Results: In total, 125 patients were selected for CT-guided radiotracer injection followed by uniportal-VATS. The planar group and planar + SPECT/CT group comprised 60 and 65 patients, respectively. Focal uptake was detected in 68 (54%), multisite with focal uptake in 46 (36.8%), and multisite without focal uptake in 11 patients (8.8%). In comparative analyses between planar and SPECT/CT imaging in 65 patients, 91% exhibited focal uptake, revealing significant differences in classification for 40% of the patients. SPECT/CT corrected the classification of 23 patients initially categorized as multisite with focal uptake to focal uptake, improving localization accuracy. The mean procedure duration was 39 min with SPECT/CT. Pneumothorax was more frequently detected with SPECT/CT (43% vs. 1.6%). The intraoperative localization success rate was 96%. Conclusions: SPECT/CT imaging in the ROLL procedure for detecting pulmonary nodules before u-VATs demonstrates a significant advantage in reclassifying radiotracer positioning compared to planar imaging. Considering its limited impact on surgical success rates and additional procedural time, SPECT/CT should be reserved for technically challenging cases. Larger sample sizes, multicentric and prospective randomized studies, and formal cost–utility analyses are warranted. Full article

The advancement of non-thermal disinfection technologies represents a critical pathway for ensuring food safety, meeting environmental sustainability requirements, and meeting consumer preferences for clean-label products. This study systematically evaluated the combined preservation effect of ultrasonic-assisted slightly acidic electrolyzed water (US+SAEW) on mirror carp fillets during refrigeration. Results demonstrated that US+SAEW exhibited superior antimicrobial efficacy compared to individual US or SAEW, achieving reductions of 0.73, 0.74, and 0.79 log CFU/g in total viable counts (TVC), Aeromonas bacteria, and lactic acid bacteria counts compared to the control, respectively. Furthermore, the combined intervention significantly suppressed microbial proliferation throughout the refrigeration period while simultaneously delaying protein and lipid degradation/oxidation induced by spoilage bacteria, thereby inhibiting the formation of alkaline nitrogenous compounds. Consequently, lower levels of pH, total volatile basic nitrogen (TVB-N), protein carbonyl, and thiobarbituric acid reactive substances (TBARS) were observed in US+SAEW compared to the other treatments. Multimodal characterization through low-field nuclear magnetic resonance (LF-NMR), texture, and color analysis confirmed that US+SAEW effectively preserved quality characteristics, extending the shelf life of mirror carp fillets by four days. This study provides a novel non-thermal preservation strategy that combines microbial safety maintenance with quality retention, offering particular advantages for thermolabile food. Full article