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Authorship detection has played an important role in social information science. In this study, we propose a support vector machine (SVM)-based authorship detection model for classical Chinese texts. Term frequency-inverse document frequency (TF-IDF) feature extraction technique is combined with the SVM-based method. The linguistic features used in this model are based on TF-DIF calculations of different function words, including literary Chinese words, end-function words, vernacular function words, and transitional function words. Furthermore, a bidirectional long short-term memory (BiLSTM)-based authorship model is introduced to detect authorship in classical Chinese texts. The BiLSTM model incorporates an attention mechanism to better capture the meaning and weight of the words. We conduct a comparative analysis between the SVM-based and BiLSTM-based models in the context of authorship detection in Chinese classical literature. The applicability of the two authorship detection models for classical Chinese texts is examined. Results indicate varying authorship between different sections of the texts, with the SVM model outperforming the BiLSTM model. Notably, these classification outcomes are consistent with findings from prior studies in classical Chinese literary analysis. The proposed SVM-based authorship detection model is especially suited for automatic literary analysis, which underscores its potential for broader literary studies. more...

The high summer temperatures of PV (photovoltaic) glass curtain walls lead to reduced power generation performance of PV modules and increased indoor temperatures. To address this issue, this study constructed a test platform for planted photovoltaic glass curtain walls to investigate the effect of plants on their power generation performance. The study’s results indicate the following: (1) reducing the average surface temperature of the surface temperature measurement instrument for the photovoltaic glass curtain wall by 13.6 °C can increase its average power generation capacity by 76 w, demonstrating its power generation performance; (2) plant cultivation influences the micro-environmental temperature on the surface temperature of the photovoltaic glass curtain wall, resulting in a decrease in average micro-environmental temperature by 3.2 °C and average surface temperature by 10.1 °C; (3) compared to traditional PV glass curtain walls, the planted PV glass curtain wall increases cumulative PV power generation output by 21.5 kWh over 15 days and average daily power generation output by 1.4 kWh. Furthermore, during sunny weather with high temperatures, the PV power generation output of the planted PV glass curtain wall is significantly enhanced. more...

This study explores poly(methyl methacrylate) (PMMA)-based composites as potential alternatives to conventional TiO2-based photocatalysts. Specifically, it examines PMMA composites enriched with oxo–titanium(IV) complexes, [Ti8O2(OiPr)20(man)4] (1), [Ti4O(OiPr)10(O3C14H8)2] (2), and [Ti6O4(OiPr)2(O3C14H8)4(O2CEt)6] (3), alongside ruthenium(III) complexes, K[Ru(Hedta)Cl]∙2H2O (4) and [Ru(pic)3]·H2O (5). We assessed the physicochemical, adsorption, and photocatalytic properties of these composites with structural analyses (Raman spectroscopy, X-ray absorption (XAS), and SEM-EDX), confirming the stability of complexes within the PMMA matrix. Composites containing titanium(IV) compounds demonstrated notably higher photocatalytic efficiency than those with ruthenium(III) complexes. Based on activity profiles, composites were categorized into three types: (i) UV-light active (complexes (1) and (2)), (ii) visible-light active (complexes (4) and (5)), and (iii) dual-range active (complex (3)). The results highlight the strong potential of titanium(IV)–PMMA composites for UV-driven photocatalysis. Moreover, their activity can be extended to the visible range after structural modifications. Ruthenium(III)–PMMA composites, in turn, showed superior performance under visible light. Overall, PMMA composites with titanium(IV) or ruthenium(III) complexes demonstrate promising photocatalytic properties for applications using both UV and visible light ranges. more...

In the fields of prosthetic control and rapid response prediction for human motion, the accurate prediction of joint moments is crucial for understanding and simulating human behavior. However, traditional time series models, especially when trained using small batches and limited data for single-time step predictions, frequently encounter difficulties in managing long-term dependencies. This deficiency significantly compromises their ability to generalize and maintain predictive accuracy over extended periods. To address these challenges, an innovative model called Multi-Branch Adaptive Encoding (MAE) has been introduced. This model features an adaptive weight module that employs a multi-branch input strategy to dynamically allocate weights to different surface electromyography (sEMG) signals and joint angles, thereby optimizing the processing of small sample data. Additionally, a feature extraction encoder, named Simplified Feature Transformer (SFT) has been designed. This encoder substitutes traditional attention mechanisms with a Multilayer Perceptron (MLP) and omits the decoder component to enhance the model’s efficiency and offer significant advantages in small-batch training and long-term prediction capabilities. A Hybrid Time–Frequency Loss (HTFLoss) has also been introduced to complement the MAE model. This approach significantly enhances the model’s ability to handle long-term dependencies. The MAE model and HTFLoss demonstrate an increase in Variance Accounted For (VAF) of 0.08 ± 0.03, a reduction in Root Mean Square Error (RMSE) of 1.77 ± 0.735, and an improvement in the coefficient of determination (R²) of 0.09 ± 0.05, indicating substantial superiority. These enhancements highlight the extensive potential applications of the model in the fields of rehabilitation medicine, and human-machine interaction. The improved predictive accuracy and the ability to manage long-term dependencies make this model particularly valuable in designing advanced prosthetic devices that can better mimic natural limb movements, thereby improving the quality of life for amputees. more...

Mining dump trucks play an important role in engineering construction and resource extraction. Current research mainly focuses on the dynamic modeling and reliability analysis of the vehicle frame, suspension and overall model. However, with the development of electric drive, the wheel hub system has become an important component in mining truck equipment. This paper investigates the multi-body modeling of a mining truck’s hub drive reduction system in order to analyze its output characteristics including the stability of the angular velocity of its planetary carriers and the fluctuations in its meshing forces. A bench experiment was also conducted to verify the accuracy and stability of the proposed modeling. And the simulation results revealed that the fluctuations in the angular velocity of the planetary carriers were primarily influenced by the excitation from the hub motor’s input and the meshing forces between the gears of the reducers, which were mainly determined by the contact stiffness, damping, and clearance value during gear contact. more...

The relationship between slabbing failure and rockburst has become a hot issue in rockburst research. In this paper, the experimental system of impact rockburst is used to conduct a simulation experiment of rockburst induced by slab failure on metamorphic sandstone samples taken from the deep-buried horseshoe-shaped tunnel in Gaoloushan, with “pan-shaped” rockburst pits on site and laboratory simulation experiments, which prove the rationality of the experimental results of rockburst. The quantitative analysis of the displacement field in the process of the slab buckling rockburst is carried out, which shows that the slab structure will undergo a long period of gestation before its formation, and the formation of the slab structure will accelerate the occurrence of rockburst. This type of rockburst has attenuation characteristics in the process of rockburst; in addition, the phenomenon of “slab buckling circle” is found. The generation of the “slab buckling circle” and the formation of slab buckling cracks are inconsistent, which is a time-lagged fracture in engineering. The relationship between the rupture parameters of rockburst disaster rock mass and time shows a compound exponential growth relationship, revealing that the mechanism of the slab buckling rockburst can be regarded as the result of the combined action of shear crack and tension crack, which plays a leading role, reflecting the characteristic of progressive fracture development. It is a typical progressive fracture-induced instability rockburst model, which is a strain-lag rockburst. more...

In the road extraction task, for the problem of low utilization of spectral features in high-resolution remote sensing images, we propose a Multi-spectral image-guided fusion of Spatial and Channel Features for road extraction algorithm (SC-FMNet). The method is designed with a two-branch input network structure including Multi-spectral image and fused image branches. Based on the original MSNet model, the Spatial and Channel Reconstruction Convolution (SCConv) module is introduced in the coding part in each of the two branches. In addition, a Spatially Adaptive Feature Modulation Mechanism (SAFMM) module is introduced into the decoding structure. The experimental results in the GF2-FC and CHN6-CUG road datasets show that the method can better extract the road information and improve the accuracy of road segmentation, which verify the effectiveness of SC-FMNet. more...

The current design methods employed for composite pavement structures predominantly rely on cement concrete slabs, which unfortunately lack established design standards and associated control indicators for determining the appropriate thickness of the asphalt layer. Therefore, the emergence of reflective cracks at the bottom of the asphalt layer has become a prevalent issue in composite pavement. This article aims to enhance the existing design methodology for composite pavement structures by proposing the inclusion of “cracking at the bottom of the asphalt layer” as a design indicator. An extensive analysis was conducted to assess the influence of various factors, including the elastic modulus and thickness of the asphalt layer, the elastic modulus, and thicknesses of the cement concrete slab, as well as the dimensions of the cement concrete slab (length and width), foundation reaction modulus, and joint width, on the comprehensive stress at the bottom of the asphalt layer. Additionally, formulas were derived to calculate the temperature warping stress and load stress, and a formula was also provided for determining the equivalent modulus of the structure, taking into account the stress-absorbing layer. Subsequently, the proposed methodology was applied to the Weixu Expressway. The results suggest adopting a surface structure design scheme consisting of a 6 cm asphalt concrete + 2 cm stress absorption layer. This study found that, when the thickness of the stress-absorbing layer is less than 2 cm, the load stress is highly sensitive to changes in the thickness of this layer. Specifically, a 1 cm thick stress-absorbing layer reduces the maximum tensile stress at the bottom of the asphalt layer by approximately 69.7%, decreases the equivalent stress by about 34.1%, and lowers the maximum shear stress by around 30.9%. However, once the thickness exceeds 2 cm, the load stress remains relatively constant. Thus, it was advisable to utilize an optimal stress-absorbing layer thickness of 2 cm. more...

Vibrations observed as a result of moving vehicles can potentially affect both buildings and the people inside them. The impacts of these vibrations are complex, affected by a number of parameters, like amplitude, frequency, and duration, as well as by the properties of the soil beneath. These factors together lead to various effects, from slight disruptions to significant structural damage. Occupants inside affected buildings may experience discomfort, disrupted sleep patterns, and increased stress levels due to the pervasive nature of vibrations. Low-frequency vibrations, typically ranging from 5 to 25 Hz, are of particular concern since they can exacerbate these effects by resonating with internal human organs. To effectively mitigate these issues, a comprehensive approach is required, starting with some interventions at the source. This may involve strategic choices in road construction materials and advancements in vehicle design to reduce the transmission of vibrations through the ground to the surrounding environment. Understanding the complexities of vibration dynamics is essential in urban planning, serving as a fundamental consideration in the development of modern infrastructure that prioritizes the well-being and safety of its inhabitants. Therefore, the aim of the present study is to consider artificial neural networks to assess the potential impact of traffic-induced vibrations on a building’s residents. The results of the study indicate that the proposed method of utilizing machine learning can be effectively applied for such purposes. more...

Gait analysis was performed in patients with chronic ankle instability (CAI). Despite advancements in rehabilitation techniques, the integration of gait-training strategies with concurrent feedback (visual and auditory) remains underexplored. This study aimed to investigate the dynamic biomechanical characteristics of CAI patients using a gait-training device following a 6-week intervention program. Thirty patients with CAI (Intervention Group; Sex: Male 8, Female 7, Age: 30.6 ± 4.08, Height: 170.49 ± 11.09 cm), (Control Group; Sex: Male 9, Female 6, Age: 30.49 ± 4.39, Height: 171.63 ± 9.90 cm) participated in this single-blind, randomized controlled trial. The intervention group completed six weeks of gait training using novel devices. Following the intervention, dorsiflexion angles increased significantly from 88.66 ± 5.47% to 92.60 ± 4.45% (p = 0.002) for the FAAM-ADL score, while FAAM-Sport scores improved from 80.79 ± 8.20% to 85.67 ± 6.41% (p = 0.000). Increased dorsiflexion and eversion angles were observed during the early to mid-late stance and late swing phases of gait for both walking and running. Joint moments demonstrated significant changes, with dorsiflexion, eversion, and abduction tendencies increasing throughout the gait cycle after the intervention. The newly developed inertial measurement unit (IMU) proved to be a viable gait-training device for CAI, highlighting that concurrent feedback may allow for greater improvements in deficiencies associated with CAI. more...

Using the standardised SORT, the article analyses instantaneous energy consumption and recuperation processes in an electric bus. The test includes three scenarios: SORT 1 (heavy urban traffic), SORT 2 (mixed driving conditions), and SORT 3 (suburban routes), enabling precise assessment of the energy efficiency of vehicles while eliminating environmental variables. The recuperation system significantly enhances energy efficiency, though its effectiveness varies based on the driving scenario. Modelling methods were compared as follows: linear regression, KNN algorithms, and neural networks, achieving a high fit (R2 > 90%). While KNN and neural networks were better at reproducing nonlinearities, they indicated the need for additional variables and time delays to enhance accuracy. The article sets itself apart by incorporating predictive models and examining recuperation efficiency across various scenarios. It emphasizes the importance of combining SORT results with real operational data and developing adaptive energy management systems. The results indicate the potential for optimizing electric buses for public transport, including route planning and further improving recuperation technology, which can significantly reduce energy consumption and greenhouse gas emissions. more...

Multiple-hop tests are commonly used in both performance and rehabilitation settings to assess neuromuscular function. This study aimed to explore the relationship between hop performance and sprint ability. Specifically, it focused on three goals: (1) examining the connection between 3-Hop and 5-Hop distances and sprint performance and comparing the strength of relationship between hop kinetics and sprint times; (2) investigating two methods of calculating the 3-Hop and 5-Hop Reactive Strength Indexes (RSIhors) and their relationship to sprinting; and (3) assessing whether hop ratios or kinetic variables could distinguish sprinters of varying abilities. Forty-four male sportsmen participated, completing 3-Hop and 5-Hop tests and sprint times (5–45 m) over 54 inground force platforms. Ground reaction forces (GRFs) were collected during hop trials and horizontal and vertical hop propulsive and braking kinetics were determined. Results showed strong negative correlations between hop distances and sprint times (r = −0.700 to −0.796), while kinetic variables showed weaker relationships with sprint performance (r = −0.554 to 0.017). RSIhor, derived from hop distance, correlated more strongly with sprint performance than RSIhor from flight time. Hop ratios (5-Hop/3-Hop) did not differentiate fast from slow sprinters, and maximal vertical force and horizontal propulsive impulse were the best predictors of 10 m and 40 m sprint times. These findings suggest that hop distance and RSIhor are valuable tools for assessing sprint performance and reactive strength. more...

Automated buses are a component of future mobility concepts. However, these vehicles will only realize their full potential when they are highly automated, without a driver on board, and operate on public roads. In addition to the further development of vehicle technology, the planning of automated bus deployment represents an important field of research. The selection of suitable routes is influenced by various criteria related to infrastructure, road users, and the environment. There is limited literature on the criteria for the route evaluation of automated vehicles, and specifically, no studies exist concerning automated buses. To address this research gap and determine the weighting of these criteria, expert interviews and a case study were conducted. The expert interviews were utilized to identify the criteria that influence the operation of automated buses. Furthermore, the weighting of these criteria was determined, facilitating their prioritization. The case study examined the extent to which these criteria could be derived from publicly available data. With the criteria catalog developed in this work, transport authorities and transport companies will be able to assess entire operational areas regarding the deployment of automated buses and prioritize suitable routes at an early planning stage. more...

Electromyography (EMG) signals are fundamental in neurorehabilitation as they provide a non-invasive means of capturing the electrical activity of muscles, enabling precise detection of motor intentions. This capability is essential for controlling assistive devices, such as therapeutic exoskeletons, that aim to restore mobility and improve motor function in patients with neuromuscular impairments. The integration of EMG into neurorehabilitation systems allows for adaptive and patient-specific interventions, addressing the variability in motor recovery needs. However, achieving the high fidelity, low latency, and robustness required for real-time control of these devices remains a significant challenge. This paper introduces a novel multi-channel electromyography (EMG) acquisition system implemented on a System-on-Chip (SoC) architecture for robotic neurorehabilitation. The system employs the Zynq-7000 SoC, which integrates an Advanced RISC Machine (ARM) processor, for high-level control and an FPGA for real-time signal processing. The architecture enables precise synchronization of up to eight EMG channels, leveraging high-speed analog-to-digital conversion and advanced filtering techniques implemented directly at the measurement site. By performing filtering and initial signal processing locally, prior to transmission to other subsystems, the system minimizes noise both through optimized processing and by reducing the distance to the muscle, thereby significantly enhancing the signal-to-noise ratio (SNR). A dedicated communication interface ensures low-latency data transfer to external controllers, crucial for adaptive control loops in exoskeletal applications. Experimental results validate the system’s capability to deliver high signal fidelity and low processing delays, outperforming commercial alternatives in terms of flexibility and scalability. This implementation provides a robust foundation for real-time bio-signal processing, advancing the integration of EMG-based control in neurorehabilitation devices. more...

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models. more...

One of the main abiotic factors affecting agricultural productivity in semi-arid regions is salinity. Seed priming is a frequently used method to enhance plant growth under saline environments. The aim of this work was to demonstrate the differences in eight agronomical characteristics of two grass pea varieties under two salinity regimes (80 and 160 mM NaCl) when pre-exposed to seed priming (hydropriming, biopriming with Bacillus subtilis and their combination). The two varieties responded well to the priming treatments, with more beneficial effects monitored for the local variety. Evaluating the root characteristics that are most affected by stress, it was found that, at 80 mM NaCl, the combination of biopriming and hydropriming increased the fresh root weight by 36.8% and root length by 70% in the commercial variety, and by 124% and 47%, in the local variety, respectively. At 160 mM NaCl, biopriming increased the fresh root weight by 40.3% and root length by 50.3% in the commercial variety, while in the local variety, the combination of biopriming and hydropriming increased the fresh root weight by 124% and root length by 47%, respectively. Overall, biopriming and the combination of biopriming and hydropriming significantly enhanced plant growth characteristics of the two grass pea genotypes. more...

Falls are a leading cause of injury and mortality among older adults, placing significant physical, emotional, and financial burdens on individuals, families, and healthcare systems. The early identification of fall risk and frequent reassessments during rehabilitation are essential for prevention and recovery. However, conventional assessments are time-intensive, rely on multiple motor tasks, and are typically conducted in specialized facilities, limiting their accessibility. This study introduces a novel machine learning-based computerized adaptive testing algorithm that personalizes testing to individual capabilities. The adaptive approach reduces task sequences by over 50% while maintaining high predictive accuracy. It also enables remote testing, predicting performance on complex tasks using as few as 2–3 simpler, accessible tasks. This innovation supports scalable online fall risk screening and frequent balance assessments during rehabilitation, offering a practical and efficient solution for both personalized and community-wide healthcare needs. more...

The treatment of maxillofacial defects presents significant challenges due to the complexity of facial anatomy and the diversity of affected tissues. Traditional workflows are labor-intensive, costly, and limited in customization. Recent advancements in fully digital workflows and direct 3D printing technologies offer new possibilities for improving the fit, aesthetics, and efficiency of prosthetic manufacturing. This scoping review aims to evaluate the current state of direct 3D printing for maxillofacial soft prostheses, assess material properties and biocompatibility, and identify challenges and future directions in this field. Methods: A comprehensive search of PubMed and Scopus databases, along with a manual search of relevant journals, was conducted to identify studies published up to December 2024. Articles focusing on direct 3D printing of maxillofacial soft prostheses were included, while studies involving traditional or mold-based workflows, ocular prostheses, and literature reviews were excluded. Data on materials, manufacturing techniques, and clinical outcomes were extracted and analyzed. Results: Out of 898 articles screened, 11 were included, 5 of which were in vivo studies (case reports). The additive manufacturing methods used in these case reports were Drop-on-Demand (DoD) silicone printing and PolyJet technology. Conclusions: Fully digital workflows and direct 3D printing technologies show promise for advancing maxillofacial prosthesis manufacturing. However, the absence of dedicated software, biocompatible materials, and medium- to long-term clinical evaluations highlight significant research gaps. Future research should focus on material development, workflow optimization, and clinical validation to enable widespread clinical adoption. more...

In recent years, ultraviolet-visible (UV-Vis) spectroscopy has become one of the important methods used to measure water chemical oxygen demand (COD). However, environmental factors (pH, temperature, conductivity, etc.) can interfere with spectral information, thereby influencing the stability and accuracy of COD detection. The three environmental factors that influence UV-Vis spectroscopy were researched in this study. Considering the complexity of environmental factors, a data fusion method is proposed to compensate for the influence of three environmental factors simultaneously. This data fusion method is based on the weighted superposition of the spectrum and three environmental factors. A COD prediction model was established by fusing spectral feature wavelengths and environmental factors to reduce the influence of environmental factors on COD detection. Through the proposed data fusion method, the accuracy of COD detection based on UV-Vis spectroscopy has been improved. The determination coefficient of prediction (RPred2) reaches 0.9602, and the root mean square error of prediction (RMSEP) reaches 3.52. more...

Stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) require precise small-field dosimetry, verified through patient-specific quality assurance (PSQA). This study evaluated the feasibility of using a single-crystal cadmium–zinc–telluride (Cd0.9Zn0.1Te, CZT) detector for PSQA in SRS and SBRT. We fabricated a CZT detector with Au electrodes and examined its fundamental characteristics, including dose linearity, dose rate dependence, energy dependence, angular dependence, source-to-surface distance (SSD) dependence, field size dependence, depth dependence, and reproducibility, under 6 and 10 MV LINAC beam irradiation and compared the results with those from a standard ionization chamber. The results revealed that the CZT detector demonstrated excellent linearity across 0–1000 cGy with minimal deviation in the low-dose region, negligible dose rate dependence, and minimal energy dependence, exhibiting a 2.2% drop at 15 MV relative to 6 MV. Its angular and SSD dependencies deviated slightly from the ionization chamber, consistent with the expected physical behaviors and correctable in clinical practice. The detector also revealed consistent performance over time with excellent reproducibility, and its depth dependence results were consistent with those of the ionization chamber. Thus, the CZT detector provides consistent performance in small-field measurements under varying conditions, satisfying the requirements for SRS and SBRT. more...

Heart rate variability (HRV), which is the variation between consecutive heartbeats, reflects the electrical activity of the heart and provides insight into the autonomic nervous system (ANS) function. This study uses wavelet transform-based HRV feature extraction to investigate cardiac sympatho-vagal balance. Both the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) methods were applied in different steps. DWT was used for R-peak detection and CWT and MODWT were used to generate spectrograms from RR intervals. Frequency components (HF, LF, and VLF) within 0.003–0.4 Hz were extracted, and power estimation was performed. The LF/HF ratio, indicating sympatho-vagal balance, was calculated. ECG data from 42 arrhythmia patients and 18 normal sinus rhythm subjects were analyzed. The results showed a lower LF/HF ratio in arrhythmia patients, with higher HF power in arrhythmia subjects and higher LF power in normal sinus rhythm subjects. The study suggests that the parasympathetic nervous system dominates the ANS in arrhythmia patients, while the sympathetic nervous system dominates in normal sinus rhythm patients. more...

This paper concerns lightweight—up to 800 kg—UGVs (unmanned ground vehicles), where multi-tracked running gears are used to improve the obstacle negotiation performance. A comparison of four different multi-tracked systems and, for reference, classic tracked running gear is presented. Simulations in a multi-body dynamics program were performed, where running gear solutions overcame three typical obstacles and were assessed using a total of five effectiveness and functionality criteria. The simulation models took into account the variable track–ground contact surface. The ground parameters were validated by means of experimental tests for grassy terrain. In the results, the most advantageous solutions in each category are indicated, and design guidelines for increasing the obstacle-overcoming capabilities of multi-tracked UGVs are presented. more...

Field measurements with the Plaxis3D 24.1 software were performed on the 17# shaft of the Shanghai Zhuyuan Bailonggang sewage connecting the pipe project to analyze the ground deformation patterns during VSM (vertical shaft sinking machine) construction in soft soil areas. The results indicate that both the shaft sinking process and construction pauses at the pit bottom significantly exacerbate soil deformation. Compared with horizontal displacement, the measured settlement is more sensitive to excavation depth. The calculations revealed that significant pit bottom heave occurs when the excavation depth reaches 40% to 70% of the maximum excavation depth (Hm). Moreover, the heave pattern transitions from a single-peak “convex” shape to a double-peak “concave” shape during the sinking process. On the basis of the deformation of the soil outside the shaft after the completion of sinking, the vertical deformation zones are classified into a groove-shaped settlement zone, heave influence zone, and heave zone. Similarly, the horizontal deformation zones are categorized as an arch-shaped deformation zone, transition deformation zone, and cantilever bending zone. For regions where the sinking depth reaches 40% to 90% of Hm, the excavation disturbance should be further minimized, and overexcavation must be strictly avoided. In areas where the horizontal distance (L) from the shaft is less than 0.3 times Hm, environmental monitoring points should be appropriately densified to optimize the fine control of deformation in the surrounding region. more...

This paper presents a novel method for measuring organizational resilience by integrating the Rasch model into the Resilience Analysis Grid (RAG), providing a robust and objective tool for cross-sectional resilience studies. By treating the four cornerstones of resilience as abilities, Rasch’s model allows for an assessment that positions both the difficulty of the items and the organizations’ ability along a common scale. The requirement is the availability of a number of different organizations to be assessed. We employ a dataset generated through an artificial simulation and analyzed in a controlled environment, demonstrating the potential of Rasch-based resilience assessments to provide accurate, comparable, and scalable results in different organizational contexts. The traditional RAG is designed without a normative reference group, which makes it challenging to evaluate its results. The proposed model overcomes this limitation by offering a measurement scale on which different organizations can be placed without the need to use a normative group, facilitating the more consistent and timely monitoring of systems. This novel approach to quantifying resilience potentials highlights the transformative role of digital technologies in improving workplace safety and resilience. It advances resilience engineering and occupational health and safety practices in complex environments like manufacturing and industrial sectors. more...

Additive manufacturing (AM) has the potential to produce novel high-performance electrical machines, enabling the direct printing of complex shapes and the simultaneous processing of multiple feedstocks in a single build. We examined the properties and functional performance of Fe-3 wt.% Si materials that were printed via selective laser melting, machined down to thin laminates, and stacked to form a stator core of a prototype brushless permanent-magnet electrical motor. Big Area Additive Manufacturing of Nd2Fe14B (NdFeB)–polyphenylene sulfide (PPS) bonded magnets was performed, with them then being magnetized and used for the rotor. The magnetic, mechanical, and electrical properties of the as-printed and various heat-treated thin laminates and the back electromotive force (EMF) of the electrical motors at different rotational speeds were measured. The thin laminates exhibit a maximum relative permeability of 7494 at an applied field of 0.8 Oe and a core loss of about 20 W/lb at 60 Hz with the maximum induction of 15 kg. In addition to the demonstration of AM printing, motor assembly, and complete characterization of printed Fe-3 wt.% Si, this report highlights the areas of improvement needed in printing technologies to achieve AM built electrical motors and the need for isotropic microstructure refinements to make the laminates appropriate for high-mechanical-strength and low-loss rotational electrical devices. more...

In this article, the response of reinforced concrete frames concurrently subjected to both horizontal and vertical seismic motions is assessed. The article is not limited to the variation in response quantities but aims to identify which specific design parameters are affected and how, as well as which specific code provisions could be violated due to the omission of vertical oscillations during the design process. Furthermore, the consequences that a design against vertical ground motion would cause in both technical and economic terms were investigated. For this purpose, six eight-storey 2D frames were designed, neglecting the vertical seismic component in compliance with code provisions. Subsequently, the seismic response of the frames to the concurrent action of horizontal and vertical ground motion was evaluated by applying both modal response spectrum and inelastic dynamic analyses. It was found out that several code violations occurred, mainly due to the fluctuation of the columns’ normalized axial forces and the amplification of up to two times or more of the beam bending moments. Thereafter, the frames were redesigned without neglecting the vertical seismic component, and the changes in the members’ cross-sectional dimensions and reinforcement were determined. Finally, it was estimated that the economic impact of redesigning did not exceed 4% of the initial total construction cost of the frames. more...

While both psychopathology and cognitive deficits manifest in mental health disorders, the nature of their relationship remains poorly understood. Recent research suggests a potential common factor underlying both domains. Using data from the Philadelphia Neurodevelopmental Cohort (N = 9494, ages 8–21), we estimated and validated a “c” factor representing overall cerebral functioning through a structural model combining cognitive and psychopathology indicators. The model incorporated general factors of psychopathology (“p”) and cognitive ability (“g”), along with specific sub-domain factors. We evaluated the model’s criterion validity using external measures, including parent education, neighborhood socioeconomic status, global functioning, and intracranial volume, and assessed its predictive utility for longitudinal psychosis outcomes. The model demonstrated acceptable fit (CFI = 0.98, RMSEA = 0.021, SRMR = 0.030), and the “c” factor from this model showed stronger associations with parent education (r = 0.43), neighborhood SES (r = 0.47), and intracranial volume (r = 0.39) than “p” and “g” factors alone. Additionally, baseline “c” factor scores significantly predicted psychosis spectrum outcomes at follow-up (d = 0.30–0.57). These findings support the utility of a “c” factor in capturing overall cerebral function across cognitive and psychopathology domains, with potential implications for understanding brain function, improving clinical assessment, and optimally focusing interventions. more...

Caffeine is consumed owing to its stimulatory effects; however, its excessive intake triggers adverse effects. Herein, we analyzed changes in physiological cerebrovascular and cognitive functions following the consumption of 100 and 200 mg of caffeine in healthy adults after 0/30/60 min to ascertain appropriate caffeine consumption habits. The peak systolic velocity (PSV), pulse wave velocity (PWV), and common carotid artery (CCA) diameter were measured using diagnostic ultrasound. Cognitive function was evaluated using the accuracy rate and response time on the two-back task. Percutaneous oxygen saturation (SpO2) and heart rate (HR) were assessed using patient monitoring systems. After consuming 100 mg of caffeine, systolic blood pressure (SBP) increased (p > 0.05) and SpO2 and accuracy rate improved by 30 min (p = 0.018 and p = 0.356) but declined by 60 min (p = 0.924 and p = 0.055). HR and response time continuously decreased (p = 0.209 and p = 0.061, respectively), while PWV showed no change (p > 0.05). After consuming 200 mg of caffeine, SBP (p < 0.05), diastolic blood pressure (p = 0.004 and p = 0.820), and SpO2 (p = 0.002 and p = 0.666) increased significantly, while the accuracy rate (p = 0.634 and p = 0.055, respectively) and response time (p < 0.05) decreased. PWV remained unchanged (p > 0.05). The results revealed distinct dose-dependent patterns on physiological and cognitive changes, with SBP and SpO2 exhibiting greater changes when a higher dose was consumed in a short duration. Although moderate caffeine intake may support vascular health and cognitive function, excessive intake reduces blood flow, alters vascular elasticity, and impairs cognitive activation. These findings highlight the need for guidelines to ensure safe and effective caffeine consumption. more...

Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction. more...

This paper addresses the three-dimensional trajectory tracking problem of underactuated autonomous underwater vehicles (AUVs) operating in the presence of external disturbances and unmodeled dynamics by proposing a predefined-time adaptive control scheme. Firstly, the underactuated AUV system was decoupled into drive and non-drive subsystems to facilitate the design of a controller that does not rely on specific model parameters. Radial basis function neural networks (RBFNNs) were employed to estimate the external disturbances. To enhance tracking performance, a predefined-time adaptive control law was designed to ensure that tracking errors converged to a small neighborhood around the origin within the predefined time. The adaptive control law compensated for the unmodeled components. Finally, we used theoretical proofs and simulations to show that our method is effective and superior. more...

In crude oil production, large volumes of produced water are generated as a highly polluting waste byproduct. On average, at least two barrels of produced water are generated for every barrel of oil. This water contains oil traces in stable and complex emulsions. To purify it, a method is proposed based on breaking these emulsions using solvents that induce the coalescence of oil droplets, facilitating their separation from the water. The method has two main objectives: (1) To identify the characteristics a solvent must have to effectively break oil emulsions according to the Hansen solubility parameter (HSP) model. (2) To select, from 40 solvents of different chemical families, the most suitable ones based on efficiency, low toxicity, industrial availability, and cost. The experimental procedure included the following steps: (1) Contacting the solvent with produced water containing 150 ppm of oil under agitation. (2) Allowing the mixture to rest until a layer of recovered oil formed. (3) Spectrophotometric analysis of the residual oil. Three distinct HSP solubility spheres were identified, within which the most effective solvents were xylene (99.4% recovery), cyclohexane (99.5% recovery), and tetrahydrofuran (100% recovery). Their high efficiency not only facilitated oil separation but also made the recovered oil suitable for commercialization. more...

To achieve both safety and a lightweight design for rotary drilling rig masts, this study proposes an optimization method incorporating safety evaluation constraints. The method employs the limit state method to validate the mast structure and uses fuzzy comprehensive evaluation to quantify safety performance metrics. The optimization objective is to minimize the mast’s self-weight, with design variables defined as the geometric dimensions of key cross-sections, while imposing constraints on the strength, stiffness, stability, and safety scores. The safety score utilizes fuzzy comprehensive evaluation and a weighted aggregation method, considering indicators such as strength, stiffness, stability, and fatigue strength. An improved Salp Swarm Algorithm is utilized to execute the optimization process. Engineering case studies demonstrate that the optimized design reduces the mast’s self-weight by 6.5% under safety constraints. Compared to designs without safety constraints, the material usage increases slightly by 7.3%, but the safety performance improves by 14.74%. The findings indicate that integrating safety evaluation constraints into the optimization process not only enhances the structural safety of the mast but also achieves a favorable balance between safety and economic efficiency. This approach provides a valuable reference for the safety-focused design of rotary drilling rig masts. more...

Thirty-two ambient cured alkali-activated engineered composites (AAECs) were developed by incorporating MgO, multi-walled carbon nanotubes (MWCNTs), reduced graphene oxide (rGO), and polyvinyl alcohol (PVA) fiber with a one-part dry mix technique using powder-based activators/reagents. The effects of material variables, namely binary or ternary combination source materials (fly ash C or F and ground granulated blast furnace slag ‘GGBFS’), two types of reagents with varying chemical ratios and dosages of additives (from 0 to 5% MgO and from 0 to 6% MWCNT/rGO), on the physical (slump flow, flow time, flow velocity, and density), hardness (compressive strength from 0 to 180 days and 28-day ultrasonic pulse velocity ‘UPV’), and micro-structural (SEM/EDS, XRD and FTIR) properties were evaluated. All these variables, individually or combined, influenced the properties and microstructural aspects of AAECs. Problems associated with the dispersion and agglomeration of nanomaterials, which could disrupt the microstructure and weaken its mechanical/physical properties, were avoided through the use of defined ultra-sonication with a high-shear mixing protocol. All AAECs achieved a 28-day compressive strength ranging from 26.0 MPa to 48.5 MPa and a slump flow > 800 mm, satisfying the criteria for flowable structural concrete. The addition of 5% MgO and up to 0.3% MWCNT/rGO increased the compressive strength/UPV of AAECs with MgO-MWCNT or rGO combination provided an improved strength at a higher dosage of 0.6%. A linear correlation between compressive strength and UPV was derived. As per SEM/EDS and XRD analyses, besides common C-A-S-H/N-C-A-S-H or C-A-S-H/C-S-H gels, the addition of MgO led to the formation of magnesium-aluminum hydrotalcite (Ht) and M-S-H (demonstrating self-healing potential), while the incorporation of rGO produced zeolites which densified the matrix and increased the compressive strength/UPV of the AAECs. Fourier transform infrared spectrometer (FTIR) analysis also suggested the formation of an aluminosilicate network in the AAECs, indicating a more stable structure. The increased UPV of MWCNT/rGO-incorporated AAECs indicated their better conductivity and ability of self-sensing. The developed AAECs, incorporating carbon-nano materials and MgO additive, have satisfactory properties with self-healing/-sensing potentials. more...

Recently, there is a renewed interest from the scientific community in the study of the electrical signal generated by plants due to its wide range of applications in agriculture, for example, environmental monitoring, detection of pests, diseases in crops, etc. Therefore, the aim of this work is to characterize the electrical signal of Aloe vera var. chinensis by using non-parametric and parametric signal analysis techniques, in order to extract some fundamental features which could be used in the design of a bio-dosimeter. Non-parametric analysis of the signal was carried out in the time, frequency, and time-frequency domains, using the short-time Fourier transform (STFT) and the wavelet transform in order to determine the different characteristics and frequency changes over time. Parametric analysis was then performed by using auto-regressive (AR) models for signal prediction and modeling, and in this case the coefficients of the model will be considered as fundamental features to be extracted. It has been identified that the majority of the signal energy is found in low frequencies, possibly associated with physiological processes or changes in the environment. Subsequently, some metrics like mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) were used in order to establish the capability of modeling the signal in its totality, considering that it is affected by the abrupt changes present in the signal. In this way, the relevance of combining both analyses is discussed in order to take their advantages for the benefit of the compression and feature extraction of the electrical signal of Aloe vera var. chinensis. This analysis allows the Aloe vera var. chinensis plant to be used for environmental monitoring, pest and disease detection in crops, or in a pattern recognition and signal classifier systems. more...

Diagnosing bipolar disorder (BD) and schizophrenia (SCH) presents significant challenges due to overlapping symptoms, reliance on subjective assessments, and the late-stage manifestation of many symptoms. Current methods using structural magnetic resonance imaging (sMRI) as input data often fail to provide the objectivity and sensitivity needed for early and accurate diagnosis. sMRI is well known to be capable of detecting anatomical changes, such as reduced gray matter volume in SCH or cortical thickness alterations in BD. However, advanced techniques are required to capture subtle neuroanatomical patterns critical for distinguishing these disorders in sMRI. Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. Building on its success in other domains, including autism spectrum disorder and Alzheimer’s disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. In this context, this study introduces a novel deep learning framework for distinguishing BD and SCH using sMRI data. The model is specifically designed to address subtle neuroanatomical differences, offering three key contributions: (1) a tailored DL model that leverages explainability to extract features that boost psychiatric MRI analysis performance, (2) a comprehensive evaluation of the model’s performance in classifying BD and SCH using both spatial and morphological analysis together with classification metrics, and (3) detailed insights, which are derived from both quantitative (performance metrics) and qualitative analyses (visual observations), into key brain regions most relevant for differentiating these disorders. The results have achieved an accuracy of 78.84%, an area under the curve (AUC) of 83.35%, and a Matthews correlation coefficient (MCC) of 59.10% using the proposed framework. These metrics significantly outperform traditional machine learning models. Furthermore, the proposed method demonstrated superior precision and recall for both BD and SCH, with notable improvements in identifying subtle neuroanatomical patterns. Depending on the acquired result, it can be said that the proposed method enhances the application of DL in psychiatry, paving the way for more objective, non-invasive diagnostic tools with the potential to improve early detection and personalized treatment. more...

Natural products have been an essential driver of human advancement, providing unparalleled chemical diversity and a wide range of biological activities [...]

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. more...

The aim of this study is to detect expressions of violence against healthcare workers using natural language processing techniques. Experiments on various NLP models have shown that violent expressions can be successfully classified using textual data. The RAG-ECE model performed the best in this study with a 97.97% accuracy rate and a 97.67% F1 score. The model provided a strong balancing performance in the “no violence” class with 97.71% precision and 97.67% recall rates. In the “violence present” class, it reached 97.67% accuracy and was evaluated as a reliable classifier with both low false positive (3.92%) and low false negative (2.78%) rates. In addition to RAG-ECE, the GPT model provided a milder alternative with 96.19% accuracy and a 96.26% F1 score. The study also compared the performances of other models, such as GPT, BERT, SVM, and NB, and stated that they are considered suitable alternatives due to their low computational costs, especially in small- and medium-sized datasets. The findings of the study show that NLP-based systems offer an effective solution for the early detection and prevention of expressions of violence against healthcare workers. more...

In this paper, a methodology for selecting the Energy Management Strategy (EMS) that best suits a heavy-duty Fuel Cell Electric Vehicle (FCEV) operating under specific conditions along a given driving cycle is proposed. Using a simulation model that incorporates the powertrain architecture and components of a specific FCEV—validated through a more detailed model operating at the power converter switching level—the performance of the entire system can be tested under different EMSs. The multicriteria evaluation system developed in this study enables the calculation of hydrogen and energy consumption, as well as the aging of the battery and fuel cell associated with each EMS. The proposed methodology serves as an evaluation tool for both the dimensioning of powertrain components and the selection of the EMS that best meets the operational requirements of a given FCEV. Results demonstrate that applying this methodology to a use case tailored for commercial devices and a standard driving cycle enables the identification of the most suitable EMS, minimizing hydrogen and energy consumption while reducing battery and fuel cell aging. more...

This study investigates the impact of low-intensity continuous ultrasound (LICU) on pancreatic adenocarcinoma (PANC-1) spheroids, emphasizing morphological and volumetric transformations. PANC-1 spheroids were cultured and treated with LICU across frequencies from 1 to 5 MHz. Cell viability and mortality were analyzed through Calcein AM/PI staining, while volumetric and morphological changes were quantified across frequencies from 2 to 4 MHz using advanced imaging techniques and computational tools, including a custom Python OpenCv Library, AnaSP 3.0, a MATLAB based open source tool. Notably, a frequency of 3.5 MHz yielded optimal outcomes, also achieving a reduction in spheroid volume and mortality while minimizing disgregation, a factor linked to metastasis risk. These findings underscore LICU’s potential as an effective therapeutic strategy, balancing tumor reduction with the preservation of structural cohesion. The study establishes a methodological framework for optimizing LICU parameters, presenting a less invasive avenue for improving therapeutic outcomes in pancreatic cancer treatment. more...

This study investigates the essential oil (EO) isolated from the seeds and cones of Canadian hemlock (Tsuga canadensis), highlighting notable differences in their chemical composition and biological activities. The seed EO was uniquely dominated by oxygenated derivatives of monoterpene hydrocarbons, particularly bornyl acetate (40%), whereas the cone EO exhibited higher levels of monoterpene hydrocarbons such as α-pinene (23%), β-pinene (20%), and myrcene (23%). A significant finding was the strong cytotoxic activity of cone EO against melanoma cell lines, with IC50 values as low as 0.104 ± 0.015 μL/mL, compared to the minimal effects of seed EO. Additionally, cone EO demonstrated stronger antimicrobial activity, with lower minimum inhibitory concentrations (MICs) against Gram-positive and Gram-negative bacteria, further highlighting its therapeutic potential. Lipophilic extracts from seeds were characterized by unsaturated fatty acids (linoleic, oleic, and sciadonic acids—specific to conifers) and bioactive molecules with high antioxidant and nutritional potential, such as β-tocopherol, β-sitosterol, and campestrol. These findings underscore the unique chemical composition of T. canadensis seed EO and its lipophilic extract, along with the potent cytotoxic and antimicrobial properties of cone EO, offering insights into their potential applications in natural products for pharmaceutical and therapeutic uses. more...

Bottarga is a traditional Sardinian (Italy) food derived from several treatments of female mullet gonads (Mugil cephalus) that occur in specific humidity (53%) and temperature (25 °C) conditions. In this work, samples from the east (BEC) and west coasts (BWC) of Sardinia were evaluated for microbiological quality, physico-chemical composition, and sensorial features. Chemical analyses show a protein and fat content of about 39% and 18.6%, respectively, without any difference between the two areas. The bottarga also had a concentration of calcium equal to 455 and 413 mg/kg for BWC and BEC, respectively, as well as a sodium concentration of about 0.70% in both samples. The cholesterol found in the samples was 417 and 389 mg/100 g of the edible part of the bottarga from the west and east coasts, respectively. Overall, microbiological evaluation indicates appropriate hygiene and safety conditions. No significant differences were observed between BEC and BWC samples regarding color uniformity and texture attributes (greasiness, adhesiveness, and friability), while the global odor and sea odor were higher for BWC than BEC. more...

Intrusion detection systems face significant challenges, including the inability to detect unknown threats and imbalances between normal and anomalous traffic. To address these limitations, we propose a semi-supervised intrusion detection algorithm based on GAN with a Transformer backbone for network security in IoT devices. To address the issue of imbalanced normal and anomalous traffic due to the diversity of network behavior and the difficulty that supervised algorithms experience in detecting unknown intrusions, we use only normal traffic as training data. By integrating the self-attention mechanism of Transformers, we leverage their ability to capture long-range dependencies in sequential data, enhancing the core capability of the GAN. The experimental results show that our algorithm achieves an F1-score of 95.2% and a false omission rate (FOR) of 10.7% on the CIC-IDS2017 dataset. On the Kitsune dataset, it attains an F1-score of 83.2% and a FOR of 15.8%. In real-world applications, when the algorithm was deployed on actual vehicle devices, it maintained strong performance with a FOR of 13%, further validating the practical applicability and value of the algorithm. more...

To meet the 2050 targets about climate change and decarbonization, accomplishing thermal comfort, Internet of Things (IoT) ecosystems are key enabling technologies to move the Built Environment (BE) towards Smart Built Environment (SBE). The first contributions of this paper conceptualise SBE from its dynamic and adaptative perspectives, considering the human habitat, and enunciate SBE as a multidimensional approach through six ways of inhabiting: defensive, projective, scientific, thermodynamic, subjective, and complex. From these premises, to analyse the performance indicators that characterise these multidisciplinary ways of inhabiting, an IoT-driven methodology is proposed: to deploy a sensor infrastructure to acquire experimental measurements; analyse data to convert them into context-aware information; and make knowledge-based decisions. Thus, this work tackles the inefficiency and high energy consumption of public buildings with the challenge of balancing energy efficiency and user comfort in dynamic scenarios. As current systems lack real-time adaptability, this work integrates an IoT-driven approach to enhance energy management and reduce discrepancies between measured temperatures and normative thresholds. Following the energy efficiency directives, the obtained results contribute to the following: understanding the complexity of the SBE by analysing its thermal performance, quantifying the potential of energy saving, and estimating its economic impact. The derived conclusions show that IoT-driven solutions allow the generation of real-data-based models on which to enhance SBE knowledge, by increasing energy efficiency and guaranteeing user comfort while minimising environmental effects and economic impact. more...

This paper presents a systematic review to explore how the Internet of Things (IoT) is empowering the Internet of Pets (IoP) to enhance the quality of life for companion animals. Thirty-six relevant papers published between 2010 and 2024 were retrieved and analyzed following both the PRISMA and the Kitchenham and Charters guidelines for conducting literature reviews. The findings demonstrate that the IoP is transforming pet care by offering innovative solutions for monitoring, feeding, and animal welfare. Asian countries are leading the development of these technologies, with a surge in research activity in recent years (2020–2024). While remote feeding prototypes currently dominate the field (79%), the IoP is anticipated to expand into other areas. Monitoring health (25%), surveillance and monitoring activities (49%), and providing comfort (17%) for pets are the primary research interests. The IoT holds immense potential to improve pet care. Research in this area is expected to continue growing, driving innovation and the creation of new IoP solutions utilizing artificial intelligence to achieve smart and predictive devices. In the future, the development of multifunctional devices that combine various capabilities in a single unit will become commonplace in a society where it is trending for young people to adopt pets instead of having children. more...

In response to the inadequate color-matching effectiveness and the difficulty of restoring color intentions in cultural heritage recreation, a Cultural Color Interactive Genetic Algorithm (Cultural Color IGA) is proposed, which combines a color network model and a color harmony prediction model. First, the role of the color network model in providing color genes for subsequent design is emphasized. Then, a dataset of 10,743 color and color rating data points is used to train 12 color harmony prediction models, with the most efficient stacking model selected to improve the efficiency of user evaluation of color schemes. A prototype system for color regeneration is built in Python, and a user interface is designed. The example analysis is conducted using the Yungang Grottoes as the source of color imagery, and image colorization is tested. Independent experiments compare the proposed method with traditional IGA in terms of average fitness, maximum fitness, and evaluation time. Fuzzy evaluation is applied to assess the effectiveness of cultural heritage color regeneration design. The results show that the trained stacking model achieves an accuracy of 65.52% in color harmony prediction, outperforming previous methods. Compared to the traditional IGA algorithm, Cultural Color IGA reduces the number of user evaluations by 67.4%, improves the average fitness by 22.68%, and increases the maximum fitness by approximately 13.37%. Regarding cultural heritage color regeneration effectiveness, 80.6% of respondents considered the generated color schemes to be of good or higher quality. This method not only generates design solutions with high cultural representation and color harmony but also improves the efficiency and sustainability of the design process by reducing trial numbers and manual evaluation workload. It demonstrates the potential of digital technologies in the protection and sustainable application of cultural heritage color, offering valuable references for the digital preservation and innovative design of cultural heritage. more...

Based on the projected growth in passenger air traffic and the need for better utilization of existing capacities, the level of service (LOS) concept in the design and planning of airport terminal facilities is crucial. By monitoring and quickly responding to expected changes in passengers’ and airlines’ needs, better utilization of airport terminal facilities in the passenger terminal can be achieved. The factors that influence the level of service (LOS) from the passenger perspective were evaluated in order to improve the user experience. Definitions of the level of service, key indicators of customer satisfaction, and a decision-making process using the analytical hierarchy process (AHP) method are described. A survey questionnaire was developed, passengers’ preferences were collected, and an analysis of the results was conducted. A hierarchical AHP decision-making model with associated criteria and sub-criteria was developed to determine the optimal level of service for low-cost carriers. Finally, by using the AHP model, new spatial–temporal parameters for the optimal level of service (LOS) for low-cost carriers (LCCs) are proposed, developed, and presented. The main objective is to adjust the existing LOS concept considering the business characteristics of low-cost carriers, in order to improve the efficiency of airport terminal facilities. more...

In modern wargaming, accurately predicting the locations of the opponent units is crucial for effective strategy and decision making. However, situational data provided by tactical wargame systems present significant challenges: high redundancy across consecutive frames and extreme data sparsity, with units occupying only a small fraction of the overall map. Traditional convolutional neural networks (CNNs) struggle to extract meaningful patterns from such data. To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. By capturing complex inter-node relationships, our model effectively reduces the impact of data repetition and sparsity, achieving robust location predictions in dynamic and sparse wargaming environments. Experimental results demonstrate that our approach significantly improves the prediction accuracy (the combined use of both the GNN and transformer modules results in a 6.4% average performance boost), highlighting its potential to advance intelligent decision making in wargaming applications. more...

The sensory quality of meat, encompassing the traits of appearance, texture, and flavour, is essential to consumer acceptance. Conventional quality assessment techniques, such as instrumental methods and trained sensory panels, often face limitations due to their destructive and time-consuming nature. In recent years, optical sensing techniques have emerged as a fast, non-invasive, and non-destructive technique for the prediction of quality attributes in meat and meat products, achieving prediction accuracies of over 90%. This review critically examines the potential of optical sensing techniques, such as near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), to inform about the sensory attributes of red meat, aligning with industrial demands for early information on the predicted sensory performance of inventory to support meeting consumer requirements. Recent trends and the remaining challenges associated with these techniques will be described. While technical issues related to spectral data acquisition and data processing are important challenges when considering industrial implementation, overall, optical sensing techniques, in tandem with recent developments in digitalisation and data analytics, provide potential for the online prediction of meat sensory quality in the meat processing industries. Establishing technologies for enhanced information on the product and improved possibilities for quality control will help the industry to meet consumer demands for a consistent quality of product. more...