A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment. Full article

As the global aging population increases, the demand for rehabilitation of elderly hand conditions has attracted increased attention in the field of wearable sensors. Owing to their distinctive anti-electromagnetic interference properties, high sensitivity, and excellent biocompatibility, optical fiber sensors exhibit substantial potential for applications in monitoring finger movements, physiological parameters, and tactile responses during rehabilitation. This review provides a brief introduction to the principles and technologies of various fiber sensors, including the Fiber Bragg Grating sensor, self-luminescent stretchable optical fiber sensor, and optic fiber Fabry–Perot sensor. In addition, specific applications are discussed within the rehabilitation field. Furthermore, challenges inherent to current optical fiber sensing technology, such as enhancing the sensitivity and flexibility of the sensors, reducing their cost, and refining system integration, are also addressed. Due to technological developments and greater efforts by researchers, it is likely that wearable optical fiber sensors will become commercially available and extensively utilized for rehabilitation. Full article

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance. Full article

This research unveils a cutting-edge navigation system for deep space missions that utilizes cosmic microwave background (CMB) sensor readings to enhance spacecraft positioning and velocity estimation accuracy significantly. By exploiting the Doppler-shifted CMB spectrum and integrating it with optical measurements for celestial navigation, this approach employs advanced data processing through the Unscented Kalman Filter (UKF), enabling precise navigation amid the complexities of space travel. The simulation results confirm the system’s exceptional precision and resilience in deep space missions, marking a significant advancement in astronautics and paving the way for future space exploration endeavors. Full article

We assessed the feasibility of implementing a virtually guided Neuromuscular Electrical Stimulation (NMES) protocol over the tibialis anterior (TA) muscle while collecting heart rate (HR), Numeric Pain Rating Scale (NPRS), and quality of contraction (QoC) data. We investigated if HR, NPRS, and QoC differ ON and OFF the TA motor point and explored potential relationships between heart rate variability (HRV) and the NPRS. Twelve healthy adults participated in this cross-sectional study. Three NMES trials were delivered ON and OFF the TA motor point. HR, QoC, and NPRS data were collected. There was no significant difference in HRV ON and OFF the motor point (p > 0.05). The NPRS was significantly greater OFF the motor point (p < 0.05). The QoC was significantly different between motor point configurations (p < 0.05). There was no correlation between the NPRS and HRV (p > 0.05, r = −0.129). We recommend non-electrical methods of measuring muscle activity for future studies. The NPRS and QoC can be administered virtually. Time-domain HRV measures could increase the validity of the protocol. The variables should be explored further virtually to enhance the protocol before eventual ICU studies. Full article

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications. Full article

Fabry–Perot interferometers (FPIs), comprising foundry-compatible dielectric thin films on sapphire wafer substrates, were investigated for possible use in chemical sensing. Specifically, structures comprising two vertically stacked distributed Bragg reflectors (DBRs), with the lower DBR between a sapphire substrate and a silicon-oxide (SiO₂) resonator layer and the other DBR on top of this resonator layer, were investigated for operation in the near-ultraviolet (near-UV) range. The DBRs are composed of a stack of nitride-rich silicon-nitride (SiN_x) layers for the higher index and SiO₂ layers for the lower index. An exemplary application would be formaldehyde detection at sub-ppm concentrations in air, using UV absorption spectroscopy in the 300–360 nm band, while providing spectral selectivity against the main interfering gases, notably NO₂ and O₃. Although SiN_x thin films are conventionally used only for visible and near-infrared optical wavelengths (above 450 nm) because of high absorbance at lower wavelengths, this work shows that nitride-rich SiN_x is suitable for near-UV wavelengths. The interplay between spectral absorbance, transmittance and reflectance in a FPI is presented in a comparative study between one FPI design using stoichiometric material (Si₃N₄) and two designs based on N-rich compositions, SiN_1.39 and SiN_1.49. Spectral measurements confirm that if the design accounts for phase penetration depth, sufficient performance can be achieved with the SiN_1.49-based FPI design for gas absorption spectroscopy in near-UV, with peak transmission at 330 nm of 64%, a free spectral range (FSR) of 20 nm and a full-width half-magnitude spectral resolution (FWHM) of 2 nm. Full article

As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model’s capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications. Full article

To rapidly eliminate toluene from aqueous environments during leakage accidents, this paper synthesized porous silico-aluminate nanoparticles (SANs) via a hydrothermal method, using cost-effective and non-toxic natural kaolin as the basic raw material. The morphology and structure of the porous SANs were characterized using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and BET-specific surface area tests. The effects of different conditions, such as the dosage of porous SANs, initial concentration of toluene, temperature, capture time, and pH, on the adsorption performance of porous SANs were analyzed. The as-prepared SANs exhibited a high removal efficiency and rapid adsorption performance toward toluene in aqueous solution. Finally, the kinetics of the adsorption of toluene in aqueous solution by porous SANs were investigated. The mechanism of the adsorption of toluene by porous SANs was further discussed. These findings provide a cost-effective and highly efficient absorbent for the emergency disposal of toluene leakage accidents. Full article

Trichostatin A (TSA), a histone deacetylase (HDAC) inhibitor, promotes the cytotoxicity of the genotoxic anticancer drug cisplatin, yet the underlying mechanism remains poorly understood. Herein, we revealed that TSA at a low concentration (1 μM) promoted the cisplatin-induced activation of caspase-3/6, which, in turn, increased the level of cleaved PARP1 and degraded lamin A&C, leading to more cisplatin-induced apoptosis and G2/M phase arrest of A549 cancer cells. Both ICP-MS and ToF-SIMS measurements demonstrated a significant increase in DNA-bound platinum in A549 cells in the presence of TSA, which was attributable to TSA-induced increase in the accessibility of genomic DNA to cisplatin attacking. The global quantitative proteomics results further showed that in the presence of TSA, cisplatin activated INF signaling to upregulate STAT1 and SAMHD1 to increase cisplatin sensitivity and downregulated ICAM1 and CD44 to reduce cell migration, synergistically promoting cisplatin cytotoxicity. Furthermore, in the presence of TSA, cisplatin downregulated TFAM and SLC3A2 to enhance cisplatin-induced ferroptosis, also contributing to the promotion of cisplatin cytotoxicity. Importantly, our posttranslational modification data indicated that acetylation at H4K8 played a dominant role in promoting cisplatin cytotoxicity. These findings provide novel insights into better understanding the principle of combining chemotherapy of genotoxic drugs and HDAC inhibitors for the treatment of cancers. Full article

Due to the high content of impurities such as proteins in tamarind seed polysaccharide (TSP), they must be separated and purified before it can be used. TSP can disperse in cold water, but a solution can only be obtained by heating the mixture. Therefore, it is important to understand the dispersion and dissolution process of TSP at different temperatures to expand the application of TSP. In this study, pasting behavior and rheological properties as a function of temperature were characterized in comparison with potato starch (PS), and their relationship with TSP molecular features and microstructure was revealed. Pasting behavior showed that TSP had higher peak viscosity and stronger thermal stability than PS. Rheological properties exhibited that G′ and G′′ of TSP gradually increased with the increase in temperature, without exhibiting typical starch gelatinization behavior. The crystalline or amorphous structure of TSP and starch was disrupted under different temperature treatment conditions. The SEM results show that TSP particles directly transformed into fragments with the temperature increase, while PS granules first expanded and then broken down into fragments. Therefore, TSP and PS underwent different dispersion mechanisms during the dissolution process: As the temperature gradually increased, TSP possibly underwent a straightforward dispersion and was then dissolved in aqueous solution, while PS granules initially expanded, followed by disintegration and dispersion. Full article

In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network’s capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model’s generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ($R^2$). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in $R^2$. Full article

The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly. Full article

Objective: To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia. Methods: A total of 153 patients with insomnia (I, 83 females, mean age + SD = 41.37 + 16.19 years) and 121 healthy controls (HC, 78 females, mean age + SD = 36.99 + 14.91 years) wore an actigraph for one week. Insomnia was classified into sleep onset insomnia (SOI), maintenance insomnia (MaI), mixed insomnia (MixI) and negative misperception insomnia (NMI). To study their activity-based prospective memory performance, all the participants were required to push the actigraph event marker button twice, at bedtime (task 1) and at get-up time (task 2). Results: Only patients with maintenance and mixed insomnia had a significantly lower accuracy in the activity-based prospective memory task at get-up time compared with the healthy controls. Conclusion: The results show that maintenance and mixed insomnia involve an impaired activity-based prospective memory performance, while sleep onset and negative misperception insomnia do not seem to be affected. This pattern of results suggests that the fragmentation of sleep may play a role in activity-based prospective memory efficiency at wake-up in the morning. Full article

Background: App-based contemplative interventions, such as mindfulness-based interventions, have gained popularity for the promotion of mental health; however, the understanding of underlying intervention-specific mechanisms remains limited, especially related to novel inter-relational dyadic practices. Methods: We tested (n = 253) seven putative mechanisms underlying two brief (daily 12-min) online mental interventions: attention-focused mindfulness and socio-emotional partner-based, both supported by weekly online coaching. Weekly self-reports of rumination, worry, psychological flexibility, affective control, social support, acceptance, and mindfulness were obtained over 10 weeks of intervention, and depression, anxiety, and resilience were assessed as pre- and post-intervention outcomes. Results: Significant week-to-week reductions in rumination and increases in psychological flexibility were observed in both interventions. Only attention-based practice led to temporal reductions in worry, and only socio-emotional dyadic practice led to temporal increases in affective control. Mediation analyses with slopes of weekly variables as mediators detected no significant indirect effects. However, exploratory moderation analyses revealed that intervention-related reductions in depressive symptomatology and anxiety vulnerability and increases in resilience were predicted by weekly increases in acceptance and affective control in the socio-emotional dyadic group, and by weekly reductions in rumination and worry in the mindfulness group. Limitations of the study include reliance on brief self-report measures, relatively small sample size, and absence of long-term follow-up assessments indicating the need for future well-powered longitudinal studies comparing intervention modalities. Conclusion: We present preliminary evidence for practice-specific active ingredients of contemplative interventions, which can be leveraged to enhance their efficiency for mental health. Full article

Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not. Full article

The binary heterostructured semiconducting visible light photocatalyst of the iron-doped graphitic carbon nitride/bismuth molybdate (Fe-g-C₃N₄/Bi₂MoO₆) composite was prepared by coupling with Fe-doped g-C₃N₄ and Bi₂MoO₆ particles. In the present study, a comparison of structural characteristics, optical properties, and photocatalytic degradation efficiency and activity between Fe-doped g-C₃N₄ particles, Bi₂MoO₆ particles, and Fe-g-C₃N₄/Bi₂MoO₆ composite was investigated. The results of X-ray diffraction (XRD) examination indicate that the hydrothermal Bi₂MoO₆ particles have a single orthorhombic phase and Fourier transform infrared (FTIR) spectroscopy analysis confirms the formation of Fe-doped g-C₃N₄. The optical bandgaps of the Fe-doped g-C₃N₄ and Bi₂MoO₆ particles are 2.74 and 2.73 eV, respectively, as estimated from the Taut plots obtained from UV-Vis diffuse reflectance spectroscopy (DRS) spectra. This characteristic indicates that the two semiconductor materials are suitable for absorbing visible light. The transmission electron microscopy (TEM) micrograph reveals the formation of the heterojunction Fe-g-C₃N₄/Bi₂MoO₆ composite. The results of photocatalytic degradation revealed that the developed Fe-g-C₃N₄/Bi₂MoO₆ composite photocatalyst exhibited significantly better photodegradation performance than the other two single semiconductor photocatalysts. This property can be attributed to the heterostructured nanostructure, which could effectively prevent the recombination of photogenerated carriers (electron–hole pairs) and enhance photocatalytic activity. Furthermore, cycling test showed that the Fe-g-C₃N₄/Bi₂MoO₆ heterostructured photocatalyst exhibited good reproducibility and stability for organic dye photodegradation. Full article

Patients with long COVID syndrome present with various symptoms affecting multiple organs. Vaccination before or after SARS-CoV-2 infection appears to reduce the incidence of long COVID or at least limit symptom deterioration. However, the impact of vaccination on the severity and extent of multi-organ long COVID symptoms and the relationship between the circulating anti-spike protein antibody levels and the severity and extent of multi-organ symptoms are unclear. This prospective cohort study included 198 patients with previous PCR-verified SARS-CoV-2 infection who met the criteria for long COVID syndrome. Patients were divided into vaccinated (n = 138, 69.7%) or unvaccinated (n = 60, 30.3%) groups. Anti-spike protein antibody levels were determined at initial clinical presentation and compared between the groups. Long COVID symptoms were quantified on the basis of the number of affected organs: Class I (mild) with symptoms in three organs, Class II (moderate) with symptoms in four to five organs, and Class III (severe) with symptoms in six or more organ systems. Associations between time to infection and vaccination with anti-spike protein antibody levels were assessed. The anti-spike protein antibody levels were 1925 ± 938 vs. 481 ± 768 BAU/mL (p < 0.001) in the vaccinated vs. unvaccinated patients. The circulating anti-spike antibody cutoff of 665.5 BAU/mL allowed us to differentiate the vaccinated from the unvaccinated patients. Vaccinated patients had fewer class II and class III multi-organ symptoms (Class II 39.9% vs. 45.0%; Class III 10.1% vs. 23.3%, p-value 0.014). Anti-spike antibody level correlated negatively with multi-organ symptom classes (p = 0.016; 95% CI −1.229 to −0.126). Anti-spike antibody levels in unvaccinated patients declined markedly with time, in contrast to the persistence of high anti-spike antibody levels in the vaccinated patients. Multi-organ symptoms were lower in vaccinated long-COVID patients, especially in those with higher anti-spike antibody levels (≥665.5 BAU/mL). Classifying the symptoms on the basis of the number of affected organs enables a more objective symptom quantification. Full article

The visible-light-driven photocatalytic production of hydrogen peroxide (H₂O₂) is currently an emerging approach for transforming solar energy into chemical energy. In general, the photocatalytic process for producing H₂O₂ includes two pathways: the water oxidation reaction (WOR) and the oxygen reduction reaction (ORR). However, the utilization efficiency of ORR surpasses that of WOR, leading to a discrepancy with the low oxygen levels in natural water and thereby impeding their practical application. Herein, we report a novel donor–bridge–acceptor (D-B-A) organic polymer conjugated by the Sonogashira–Hagihara coupling reaction with tetraphenylethene (TPE) units as the electron donors, acetylene (A) as the connectors and pyrene (P) moieties as the electron acceptors. Notably, the resulting TPE-A-P exhibits a remarkable solar-to-chemical conversion of 1.65% and a high BET-specific surface area (1132 m²·g⁻¹). Furthermore, even under anaerobic conditions, it demonstrates an impressive H₂O₂ photosynthetic efficiency of 1770 μmol g⁻¹ h⁻¹, exceeding the vast majority of previously reported photosynthetic systems of H₂O₂. The outstanding performance is attributed to the effective separation of electrons and holes, along with the presence of sufficient reaction sites facilitated by the incorporation of alkynyl electronic bridges. This protocol presents a successful method for generating H₂O₂ via a water oxidation reaction, signifying a significant advancement towards practical applications in the natural environment. Full article

This study advances the vibration-assisted welding (VAW) technique for joining medium-carbon, low-alloy steels, which are typically challenging to weld. Traditional welding methods suggest low linear energy and mandatory pre- and post-heating due to these steels’ poor weldability. However, VAW employs a vibrating table to maintain part vibration throughout the automatic MIG/MAG welding process. This study tested the VAW technique on 42CrMo4 steel samples, achieving satisfactory weld quality without the need for pre- and post-heating treatments. This research revealed that while vibration frequencies between 550 Hz and 9.5 kHz minimally affect the appearance of the weld joint, the oscillation acceleration has a significant impact. The acceleration along the weld axis (a_x), combined with the welding speed and vibration frequency, affects the weld surface’s appearance, particularly its scaly texture and size. Lateral acceleration (a_y) alters the seam width, whereas vertical acceleration (a_z) affects penetration depth at the root. Notably, if the effective acceleration (a_ef) surpasses 40 m/s², there is a risk of molten metal expulsion from the weld pool or piercing at the joint’s base. The quality of the joints was assessed through macroscopic and microscopic structural analyses, micro-hardness tests in the weld zone, and bending trials. The mechanical properties of the VAW samples were found to be acceptable, with hardness slightly exceeding that of the samples subjected to pre- and post-heating. Moreover, the VAW process significantly reduced energy consumption and operational time. The employed vibration system, with a power rating of 100 W, operates for just a few minutes, resulting in substantially lower energy usage compared to the traditional pre- and post-heating method, which typically requires a 5 kW electric furnace. Full article

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure. Full article

The prevalence of carbapenem-resistant P. aeruginosa has dramatically increased over the last decade, and antibiotics alone are not enough to eradicate infections caused by this opportunistic pathogen. Phage therapy is a fresh treatment that can be administered under compassionate use, particularly against chronic cases. However, it is necessary to thoroughly characterize the virus before therapeutic application. Our work describes the discovery of the novel sequenced bacteriophage, vB_PaeP-F1Pa, containing an integrase, performs a phylogenetical analysis, describes its stability at a physiological pH and temperature, latent period (40 min), and burst size (394 ± 166 particles per bacterial cell), and demonstrates its ability to infect MDR and XDR P. aeruginosa strains. Moreover, this novel bacteriophage was able to inhibit the growth of bacteria inside preformed biofilms. The present study offers a road map to analyze essential areas for successful phage therapy against MDR and XDR P. aeruginosa infections, and shows that a phage containing an integrase is also able to show good in vitro results, indicating that it is very important to perform a genomic analysis before any clinical use, in order to prevent adverse effects in patients. Full article