We describe the current Good Manufacturing Practice (cGMP) production and subsequent characterization of eOD-GT8 60mer, a glycosylated self-assembling nanoparticle HIV-1 vaccine candidate and germline targeting priming immunogen. Production was carried out via transient expression in the human embryonic kidney 293 (HEK293) cell line followed by a combination of purification techniques. A large-scale cGMP (200 L) production run yielded 354 mg of the purified eOD-GT8 60mer drug product material, which was formulated at 1 mg/mL in 10% sucrose in phosphate-buffered saline (PBS) at pH 7.2. The clinical trial material was comprehensively characterized for purity, antigenicity, glycan composition, amino acid sequence, and aggregation and by several safety-related tests during cGMP lot release. A comparison of the purified products produced at the 1 L scale and 200 L cGMP scale demonstrated the consistency and robustness of the transient transfection upstream process and the downstream purification strategies. The cGMP clinical trial material was tested in a Phase 1 clinical trial (NCT03547245), is currently being stored at −80 °C, and is on a stability testing program as per regulatory guidelines. The methods described here illustrate the utility of transient transfection for cGMP production of complex products such as glycosylated self-assembling nanoparticles. Full article

The omega-3 fatty acid desaturase enzyme gene FAD3 is responsible for converting linoleic acid to linolenic acid in plant fatty acid synthesis. Despite limited knowledge of its role in cotton growth, our study focused on GhFAD3-4, a gene within the FAD3 family, which was found to promote fiber elongation and cell wall thickness in cotton. GhFAD3-4 was predominantly expressed in elongating fibers, and its suppression led to shorter fibers with reduced cell wall thickness and phosphoinositide (PI) and inositol triphosphate (IP₃) levels. Transcriptome analysis of GhFAD3-4 knock-out mutants revealed significant impacts on genes involved in the phosphoinositol signaling pathway. Experimental evidence demonstrated that GhFAD3-4 positively regulated the expression of the GhBoGH3B and GhPIS genes, influencing cotton fiber development through the inositol signaling pathway. The application of PI and IP₆ externally increased fiber length in GhFAD3-4 knock-out plants, while inhibiting PI led to a reduced fiber length in GhFAD3-4 overexpressing plants. These findings suggest that GhFAD3-4 plays a crucial role in enhancing fiber development by promoting PI and IP₃ biosynthesis, offering the potential for breeding cotton varieties with superior fiber quality. Full article

Background and Objective: Understanding whether cranial nerve palsy (CNP) acts as an independent risk factor for kidney cancer could have important implications for patient care, early detection, and potentially the development of preventive strategies for this type of cancer in individuals with CNP. This study aimed to examine the risk of kidney cancer following the onset of ocular motor CNP and assess whether CNP could be considered an independent risk factor for kidney cancer. Materials and Methods: A population-based cohort study was conducted using data from the National Sample Cohort (NSC) database of Korea’s National Health Insurance Service which was collected from 2010 to 2017. Follow-up was until kidney cancer development, death, or 31 December 2018. Cox proportional hazard regression analysis was performed to determine hazard ratios (HRs) for kidney cancer according to CNP status. Participants aged 20 years or more diagnosed with CNP from 2010 to 2017 were included. Exclusions comprised individuals with specific pre-existing conditions, inability to match a control group, and missing data, among others. CNP patients were age–sex matched in a 1:5 ratio with control cases. The primary outcome was incidence of kidney cancer during the follow-up period. Results: This study comprised 118,686 participants: 19,781 in the CNP group, and 98,905 in the control group. Compared to the control group, participants with CNP had a higher risk of kidney cancer (adjusted HR in model 4, 1.599 [95% CI, 1.116–2.29]). After a 3-year lag period, the CNP group had a significantly higher risk (adjusted HR in model 4, 1.987 [95% CI, 1.252–3.154]). Conclusions: Ocular motor CNP may be an independent risk factor for kidney cancer, as indicated by a higher incidence of kidney cancer in CNP patients. Further research is needed to elucidate the underlying mechanisms and explore potential preventive measures for kidney cancer in patients with ocular motor CNP. Full article

Liver transplant recipients (LTRs) have lower long-term survival rates compared with the general population. This underscores the necessity for developing biomarkers to assess post-transplantation mortality. Here we compared plasma trimethylamine-N-oxide (TMAO) levels with those in the general population, investigated its determinants, and interrogated its association with all-cause mortality in stable LTRs. Plasma TMAO was measured in 367 stable LTRs from the TransplantLines cohort (NCT03272841) and in 4837 participants from the population-based PREVEND cohort. TMAO levels were 35% higher in LTRs compared with PREVEND participants (4.3 vs. 3.2 µmol/L, p < 0.001). Specifically, TMAO was elevated in LTRs with metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, and polycystic liver disease as underlying etiology (p < 0.001 for each). Among LTRs, TMAO levels were independently associated with eGFR (std. β = −0.43, p < 0.001) and iron supplementation (std. β = 0.13, p = 0.008), and were associated with mortality (29 deaths during 8.6 years follow-up; log-rank test p = 0.017; hazard ratio of highest vs. lowest tertile 4.14, p = 0.007). In conclusion, plasma TMAO is likely elevated in stable LTRs, with impaired eGFR and iron supplementation as potential contributory factors. Our preliminary findings raise the possibility that plasma TMAO could contribute to increased mortality risk in such patients, but this need to be validated through a series of rigorous and methodical studies. Full article

Upland cotton accounts for a high percentage (95%) of the world’s cotton production. Plant height (PH) and branch number (BN) are two important agronomic traits that have an impact on improving the level of cotton mechanical harvesting and cotton yield. In this research, a recombinant inbred line (RIL) population with 250 lines developed from the variety CCRI70 was used for constructing a high-density genetic map and identification of quantitative trait locus (QTL). The results showed that the map harbored 8298 single nucleotide polymorphism (SNP) markers, spanning a total distance of 4876.70 centimorgans (cMs). A total of 69 QTLs for PH (9 stable) and 63 for BN (11 stable) were identified and only one for PH was reported in previous studies. The QTLs for PH and BN harbored 495 and 446 genes, respectively. Combining the annotation information, expression patterns and previous studies of these genes, six genes could be considered as potential candidate genes for PH and BN. The results could be helpful for cotton researchers to better understand the genetic mechanism of PH and BN development, as well as provide valuable genetic resources for cotton breeders to manipulate cotton plant architecture to meet future demands. Full article

Utilizing and improving the productivity of reclaimed land are highly significant for alleviating the problem of food production shortage in China, and the integrated rice–frog farming model can improve soil fertility. However, there are few studies on the use of integrated rice–frog farming technology to improve the fertility of reclaimed land and increase its efficiency in food production. Therefore, this study was conducted to evaluate the effects of the rice–frog co-cropping mode on the soil fertility and microbial diversity of reclaimed land. A rice monoculture group (SF), low-density rice–frog co-cropping group (SD, 5000 frogs/mu, corresponds to 8 frogs/m²), and high-density rice–frog co-cropping group (SG, 10,000 frogs/mu, corresponds to 15 frogs/m²) were established and tested. The contents of total nitrogen, soil organic matter, available potassium, and available phosphorus of the soil in the SG group were significantly higher than those in the SF group (p < 0.05) in the mature stage of rice. Compared with the SF group, the SD and SG groups improved the soil microbial diversity and changed the structure of the microbial community. This study indicates that compared with the rice monoculture mode, the rice–frog co-cropping pattern can improve the soil fertility, as well as microbial diversity, of reclaimed land. Full article

Dielectric elastomer is a kind of electronic electroactive polymer, which plays an important role in the application of soft robots and flexible electronics. In this study, an all-organic polyaniline/copper phthalocyanine/silicone rubber (PANI/CuPc/PDMS) dielectric composite with superior comprehensive properties was prepared by manipulating the arrangement of filler in a polymer matrix assisted by electric fields. Both CuPc particles and PANI particles can form network structures in the PDMS matrix by self-assembly under electric fields, which can enhance the dielectric properties of the composites at low filler content. The dielectric constant of the assembled PANI/CuPc/PDMS composites can reach up to 140 at 100 Hz when the content of CuPc and PANI particles is 4 wt% and 2.5 wt%, respectively. Moreover, the elastic modulus of the composites remains below 2 MPa, which is important for electro-deforming. The strain of assembled PANI/CuPc/PDMS three-phase composites at low electric field strength (2 kV/mm) can increase up to five times the composites with randomly dispersed particles, which makes this composite have potential application in the field of soft robots and flexible electronics. Full article

This study investigates the chemical composition, nutritional, and biological properties of extracts obtained from A. melanocarpa berries using different extraction methods and solvents. Hydrodistillation and supercritical fluid extraction with CO₂ allowed us to isolate fruit essential oil (HD_EX) and fixed oil (SFE_EX), respectively. A phenol-enriched extract was obtained using a mild ultrasound-assisted maceration with methanol (UAM_M). The HD_EX most abundant component, using gas chromatography-mass spectrometry (GC/MS), was italicene epoxide (17.2%), followed by hexadecanoic acid (12.4%), khusinol (10.5%), limonene (9.7%), dodecanoic acid (9.7%), and (E)-anethole (6.1%). Linoleic (348.9 mg/g of extract, 70.5%), oleic (88.9 mg/g, 17.9%), and palmitic (40.8 mg/g, 8.2%) acids, followed by α-linolenic and stearic acids, were the main fatty acids in SFE_EX determined using high-performance liquid chromatography coupled with a photodiode array detector and an evaporative light scattering detector (HPLC-DAD/ELSD). HPLC-DAD analyses of SFE_EX identified β-carotene as the main carotenoid (1.7 mg/g), while HPLC with fluorescence detection (FLU) evidenced α-tocopherol (1.2 mg/g) as the most abundant tocopherol isoform in SFE_EX. Liquid chromatography-electrospray ionization-MS (LC-ESI-MS) analysis of UAM_M showed the presence of quercetin-sulfate (15.6%, major component), malvidin 3-O-(6-O-p-coumaroyl) glucoside-4-vinylphenol adduct (pigment B) (9.3%), di-caffeoyl coumaroyl spermidine (7.6%), methyl-epigallocatechin (5.68%), and phloretin (4.1%), while flavonoids (70.5%) and phenolic acids (23.9%) emerged as the most abundant polyphenol classes. UAM_M exerted a complete inhibition of the cholesterol oxidative degradation at 140 °C from 75 μg of extract, showing 50% protection at 30.6 μg (IA₅₀). Furthermore, UAM_M significantly reduced viability (31–48%) in A375 melanoma cells in the range of 500–2000 μg/mL after 96 h of incubation (MTT assay), with a low toxic effect in normal HaCaT keratinocytes. The results of this research extend the knowledge of the nutritional and biological properties of A. melanocarpa berries, providing useful information on specific extracts for potential food, cosmetic, and pharmaceutical applications. Full article

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and to support operational decision-making based on real-time information. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore the operational pain points, the best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal, and leave space for optimisation. The experts see great potential in analysing the past working shift data to identify the reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of emulation for detailed ad hoc simulation studies to improve operational decision-making. Full article

The current state of the literature lacks a clear characterization of gastrointestinal (GI) symptoms, gut microbiota composition, and general physical and mental wellbeing in well-trained athletes. Therefore, this study aimed to characterize differences in self-reported symptoms, gut microbiota composition, and wellbeing (i.e., sleep quality, mood, and physical (PHQ) and mental wellbeing) between athletes with and without GI symptoms. In addition, we assessed the potential impact of a 3-week multi-ingredient fermented whey supplement in the GI complaints group, without a control group, on the gut microbiota and self-reported GI symptoms and wellbeing. A total of 50 athletes (24.7 ± 4.5 years) with GI issues (GI group at baseline, GI-B) and 21 athletes (25.4 ± 5.3 years) without GI issues (non-GI group, NGI) were included. At baseline, there was a significant difference in the total gastrointestinal symptom rating scale (GSRS) score (24.1 ± 8.48 vs. 30.3 ± 8.82, p = 0.008) and a trend difference in PHQ (33.9 ± 10.7 vs. 30.3 ± 8.82, p = 0.081), but no differences (p > 0.05) were seen for other outcomes, including gut microbiota metrics, between groups. After 3-week supplementation, the GI group (GI-S) showed increased Bifidobacterium relative abundance (p < 0.05), reported a lower number of severe GI complaints (from 72% to 54%, p < 0.001), and PHQ declined (p = 0.010). In conclusion, well-trained athletes with GI complaints reported more severe GI symptoms than an athletic reference group, without showing clear differences in wellbeing or microbiota composition. Future controlled research should further investigate the impact of such multi-ingredient supplements on GI complaints and the associated changes in gut health-related markers. Full article

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths. Classically, liver transplantation (LT) can be curative for HCC tumors within the Milan criteria. Bridging strategies to reduce the dropouts from LT waiting lists and/or to downstage patients who are beyond the Milan criteria are widely utilized. We conducted a literature-based review to evaluate the role of systemic therapies as a bridging treatment to liver transplantation (LT) in HCC patients. Tyrosine kinase inhibitors (TKIs) can be used as a systemic bridging therapy to LT in patients with contraindications for locoregional liver-directed therapies. Immune checkpoint inhibitor (ICI) treatment can be utilized either as a monotherapy or as a combination therapy with bevacizumab or TKIs prior to LT. Acute rejection after liver transplantation is a concern in the context of ICI treatment. Thus, a safe ICI washout period before LT and cautious post-LT immunosuppression strategies are required to reduce post-LT rejections and to optimize clinical outcomes. Nevertheless, prospective clinical trials are needed to establish definitive conclusions about the utility of systemic therapy as a bridging modality prior to LT in HCC patients. Full article

Phytoremediation is considered an effective strategy for remediation of heavy-metal-contaminated soil in mining areas. However, single-species plants cannot reach the highest potential for uptake of heavy metals due to inhibition of their growth by high concentrations of heavy metals in the soil. Therefore, this study has explored the effects of illite application and two plant species’ co-cropping on soil quality, plant growth, and heavy metal transformation in a soil–plant system. The results reveal that the addition of 1% (mass fraction) of illite significantly enhances soil pH. The co-cropping of Ilex cornuta and Epipremnum aureum is beneficial for improving the organic matter content of the soil. The contents of EDTA-extractable Pb, Zn, and Cu were significantly reduced by 29.8–32.5%, 1.85–5.72%, and 30.0–32.9%, respectively, compared to the control. The co-cropping of Ilex cornuta and Epipremnum aureum promoted enrichment effects of Epipremnum aureum on Pb and Ilex cornuta on Cd (p < 0.05). The co-cropping pattern lowered the biomass of Ilex cornuta and Epipremnum aureum; however, co-cropping of Ilex cornuta and Epipremnum aureum promoted the elimination of Pb, Zn, Cu, and Cd from the soil at 13.0–75.8%, 11.1–38.2%, 8.39–88.4%, and 27.8–72.5%, respectively. It is concluded that illite application combined with co-cropping of Ilex cornuta and Epipremnum aureum is highly effective for the elimination of Pb, Zn, Cu, and Cd from contaminated soil. This study provides a theoretical basis and pathway for the restoration of heavy-metal-contaminated soil in mining with the application of bentonite combined with phytoremediation. Full article

High- and very high-resolution (HR, VHR) remote sensing (RS) images can provide comprehensive and intricate spatial information for land cover classification, which is particularly crucial when analyzing complex built-up environments. However, the application of HR and VHR images to large-scale and detailed land cover mapping is always constrained by the intricacy of land cover classification models, the exorbitant cost of collecting training samples, and geographical changes or acquisition conditions. To overcome this limitation, we propose an unsupervised domain adaptation (UDA) with contrastive learning-based discriminative feature augmentation (CLDFA) for RS image classification. In detail, our method first utilizes contrastive learning (CL) through a memory bank in order to memorize sample features and improve model performance, where the approach employs an end-to-end Siamese network and incorporates dynamic pseudo-label assignment and class-balancing strategies for adaptive domain joint learning. By transferring classification models trained on a source domain (SD) to an unlabeled target domain (TD), our proposed UDA method enables large-scale land cover mapping. We conducted experiments using a massive five billion-pixels dataset as the SD and tested the HR and VHR RS images of five typical Chinese cities as the TD and applied the method on the completely unlabeled world view 3 (WV3) image of Urumqi city. The experimental results demonstrate that our method excels in large-scale HR and VHR RS image classification tasks, highlighting the advantages of semantic segmentation based on end-to-end deep convolutional neural networks (DCNNs). Full article

The concept of Green Human Resource Management (GHRM) is regarded as a major turning point in managing human capital among firms. Sustainable practices, ecofriendly initiatives, and adequate management of employees (i.e., recruitment, training, performance, rewards, and involvement) are fundamental aspects of GHRM, which enable improvements in the performance of firms and enhanced competitiveness among their rivals. In this regard, the current study takes a quantitative approach towards analyzing GHRM practices and their effects on employee retention among hotels in the UAE. Furthermore, the indirect effect of green innovation is analyzed as a potential mediating variable that can better explain the GHRM–employee retention relationship. A total of 207 employees from five 5-star hotels were selected as participants to provide information regarding the factors under examination in this research. The collected data were analyzed using Smart-PLS v.3 and a partial least squares–structural equation modeling technique, which is a fitting technique for causal models. The perspective of employees on the outcome of GHRM initiatives and their willingness to remain in their firms can greatly contribute to the current understanding of GHRM and its effectiveness on employee retention in the context of the hotel industry of the UAE, and thus, aid practitioners and scholars alike. Full article

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article

The Snake Optimizer (SO) is an advanced metaheuristic algorithm for solving complicated real-world optimization problems. However, despite its advantages, the SO faces certain challenges, such as susceptibility to local optima and suboptimal convergence performance in cases involving discretized, high-dimensional, and multi-constraint problems. To address these problems, this paper presents an improved version of the SO, known as the Snake Optimizer using Sobol sequential nonlinear factors and different learning strategies (SNDSO). Firstly, using Sobol sequences to generate better distributed initial populations helps to locate the global optimum solution faster. Secondly, the use of nonlinear factors based on the inverse tangent function to control the exploration and exploitation phases effectively improves the exploitation capability of the algorithm. Finally, introducing learning strategies improves the population diversity and reduces the probability of the algorithm falling into the local optimum trap. The effectiveness of the proposed SNDSO in solving discretized, high-dimensional, and multi-constraint problems is validated through a series of experiments. The performance of the SNDSO in tackling high-dimensional numerical optimization problems is first confirmed by using the Congress on Evolutionary Computation (CEC) 2015 and CEC2017 test sets. Then, twelve feature selection problems are used to evaluate the effectiveness of the SNDSO in discretized scenarios. Finally, five real-world technical multi-constraint optimization problems are employed to evaluate the performance of the SNDSO in high-dimensional and multi-constraint domains. The experiments show that the SNDSO effectively overcomes the challenges of discretization, high dimensionality, and multi-constraint problems and outperforms superior algorithms. Full article

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The ${\mathcal{G}}_I^0$ distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and ${\mathcal{G}}_I^0$ distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data. Full article

The preservation of perishable food items within the cold chain is a critical aspect of modern food logistics. Traditional refrigeration systems consume large amounts of energy, without an optimal temperature distribution, leading to potential food spoilage and economic losses. In recent years, the integration of Phase Change Materials (PCMs) into cold chain systems has emerged as a promising solution to address these challenges. This article presents a comprehensive analysis of the utilization of PCMs for food preservation in a refrigerated truck, focusing on the impact on temperature control, phase change fraction, costs, and energy savings. The effectiveness of PCM-based refrigeration system to maintain the refrigerated truck at a temperature of −18 °C under various scenarios and environmental conditions using a transient model was evaluated. The TRNSYS model includes a representation of a conventional refrigerated van’s system, with simulations conducted in a Mediterranean climate (Naples). The model’s core components consist of Type 56 for cooling load estimation and Type 1270a for the new PCM component. Results indicate that for guaranteeing −18 °C for 10 h, 96.4 kg and 102.2 kg of E-26 and E-29 PCM are needed, respectively, for scenarios with 10 door openings during transportation and for two different velocities of the truck: 30 and 80 km h⁻¹. Results indicate that the incorporation of PCMs in the refrigerated van leads to significant improvements in temperature stability and uniformity, thereby extending the shelf life of perishable food products and reducing the risk of spoilage. Furthermore, the analysis shows that, using the PCMs, a significant reduction of the energy costs can be obtained (up to a maximum of around 79%). Full article

Phytophthora nicotianae is a global and polyphagous pathogen with a wide host range. P. nicotianae can infect Areca catechu, Durio zibethinus L., Psidium guajava L., Hevea brasiliensis, and other tree species. The pathogen is capable of inducing butt rot and affecting aerial parts, including stems, leaves, and fruits. Compared to other Phytophthora species, P. nicotianae is more adaptable to abiotic stress. In this study, recombinase polymerase amplification (RPA) in combination with the CRISPR/Cas12a system was used for the detection of P. nicotianae, and achieved rapid and efficient detection of P. nicotianae. The assay was highly specific to P. nicotianae. All 4 tested isolates of P. nicotianae yielded positive results, whereas 30 isolates belonging to 17 other Phytophthora species, 8 fungal species, and 4 Bursaphelenchus xylophilus vermicules lacked detection. Under the conditions of 37 °C, after 20 min of RPA reaction and 25 min of Cas12a cleavage, a DNA concentration as low as 10 pg·μL⁻¹ could be detected. In addition, it detected P. nicotianae from artificially inoculated leaves of Fatsia japonica. In this study, a novel method was established for the efficient and accurate detection of P. nicotianae based on the combination of RPA and the CRISPR/Cas12a system. Full article

The study addressed two main objectives: (i) to investigate disparities in motivation dimensions regarding extracurricular physical activity and (ii) to identify the influence of motivation on time spent in formal and informal physical activity. A sample of 704 adolescents (56% girls) from middle (46%) and high school (54%), with an average age of 14.88 ± 2.52, was assessed for different motivation dimensions using the Questionnaire of Motivation for Sports Activities (QMSA). Additionally, participants were categorized based on extracurricular physical activity practice. Multivariate analyses and multiple linear regressions were conducted to examine the effect of physical activity type on motivation dimensions and identify predictors of time spent in formal and informal physical activities, respectively. Results indicated that motivation varied significantly with extracurricular physical activity practice (p < 0.05), with students involved in extracurricular activities being more motivated. Sex and age differences were observed, with boys showing higher motivation in certain dimensions (achievement status (p < 0.001); group activity (p = 0.027); contextual (p = 0.004); technical improvement (p = 0.012) and older participants having lower scores in all dimensions. The influence of family and friends was a significant predictor only for boys in formal physical activity (p = 0.039). In terms of time spent in physical activity, group activity was a predictor for informal activities (p < 0.001), while technical improvement was a predictor for formal activities (p < 0.001), with notable sex differences. These findings underscore the importance of considering sex- and age-specific motivations when promoting physical activity among adolescents. Full article

Automatic Modulation Recognition (AMR) is currently a research hotspot, and research under low Signal-to-Noise Ratio (SNR) conditions still poses certain challenges. This paper proposes an AMR method based on phase transformation and deep residual shrinkage network to improve recognition accuracy. Firstly, the raw I/Q data from the benchmark dataset RML2016.10a are used as the input. Then, an end-to-end modulation recognition is performed using the model. Phase transformation is used to correct the raw I/Q data and reduce the interference of phase shift on modulation recognition. Convolutional neural network (CNN) and Gate Recurrent Unit (GRU) extract the spatial and temporal features of the modulation signal, respectively. The improved deep residual shrinkage network is added after CNN to eliminate unimportant features through soft thresholding. Finally, the proposed model is trained and tested. The experimental results show that the proposed model notably reduces the number of parameters compared to other models, effectively improving the recognition accuracy under low SNR conditions. The average recognition accuracy reaches 62.46%, and the highest recognition accuracy reaches 92.41%. Full article

Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators. Full article