Search Results (228)

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article

Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling. Due to the complex structural environment, diverse lithofacies types, and differences in logging data and core data recording standards, there is significant overlap in the logging responses between different lithologies in the second member of the Lucaogou Formation in the Santanghu Basin. Machine learning methods have demonstrated powerful nonlinear capabilities that have a strong advantage in addressing complex nonlinear relationships between data. In this paper, based on felsic content, the lithologies in the study area are classified into four categories from high to low: tuff, dolomitic tuff, tuffaceous dolomite, and dolomite. We also study select logging attributes that are sensitive to lithology, such as natural gamma, acoustic travel time, neutron, and compensated density. Using machine learning methods, XGBoost, random forest, and support vector regression were selected to conduct lithology identification and favorable reservoir prediction in the study. The prediction results show that when trained with 80% of the predictors, the prediction performance of all three models has improved to varying degrees. Among them, Random Forest performed best in predicting felsic content, with an MAE of 0.11, an MSE of 0.020, an RMSE of 0.14, and a R² of 0.43. XGBoost ranked second, with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R² of 0.42. SVR performed the poorest. By comparing the actual core data with the predicted data, it was found that the results are relatively close to the XRD results, indicating that the prediction accuracy is high. Full article

Many behavioral tasks in intelligent agent research involve working with mathematical vectors. While traditional methods perform well in some cases, they struggle in complex and dynamic environments. Recently, bionic neural networks have emerged as a novel solution. Studies on the Drosophila central complex have revealed that these insects use neural signals from the ellipsoid body and fan to track allocentric travel angles and update spatial awareness during movement, a process that heavily relies on directional vector manipulation. Our model accurately replicates the neural connectivity of the Drosophila central complex, drawing inspiration from the half-adder unit to efficiently encode and process spatial direction information. This framework significantly enhances the accuracy of coordinate transformations while increasing adaptability and resilience in challenging environments. Our experimental results demonstrate that the bionic neural network outperforms traditional methods, delivering superior precision and robust generalizability within the coordinate system. Full article

Background: Leishmaniasis is a vector-borne zoonotic disease caused by protozoa of the genus Leishmania. While it is endemic in the Mediterranean Basin and the Balkans, Romania remains a non-endemic country. However, climate change, increased international travel, and the documented presence of competent vectors (Phlebotomus spp.) have raised concerns about the potential emergence of autochthonous cases. Case Presentation: We report two cases of imported cutaneous leishmaniasis (CL) diagnosed in central Romania, a region without previously confirmed human or animal cases. The first case involved a 31-year-old male with a recent travel history to Spain, presenting with erythematous papules and plaques that evolved into ulcerated lesions. The diagnosis was confirmed histopathologically and by a PCR. Treatment with miltefosine was effective, with minimal hepatic toxicity and a sustained response at a six-month follow-up. The second case concerned an 11-year-old boy who had traveled to Elba, Italy. He developed ulcerative lesions that progressed rapidly and were complicated by Pseudomonas aeruginosa superinfection. Despite an initially negative smear, PCR testing of the skin lesion confirmed the presence of CL. Antifungal therapy with fluconazole led to clinical improvement; treatment was ongoing at the time of publication. Discussion: These cases highlight the diagnostic and therapeutic challenges associated with CL in non-endemic settings. The varied clinical evolution underscores the importance of considering leishmaniasis in the differential diagnosis of chronic, non-healing cutaneous lesions, particularly in patients with a travel history to endemic regions. Conclusions: Increased awareness among clinicians, supported by accurate diagnostic tools and public health surveillance, is essential to identify and manage imported leishmaniasis. Given the absence of a licensed vaccine and the growing risk of vector expansion in Eastern Europe, these cases support the WHO’s inclusion of leishmaniasis among the priority neglected tropical diseases targeted for intensified global control efforts by 2030. Full article

Migration is a diverse behavior exhibited by a wide array of organisms. Variability in the type of movements is rooted in their purpose, environmental factors, demographics, and individual physiological condition. The ability of caribou (Rangifer tarandus granti) to efficiently move long distances and have a high degree of plasticity in their movements allows them to respond and be resilient to dynamic and dramatic differences in environmental conditions. I used 88 collared, sympatric, adult, female, barren-ground Nelchina Caribou Herd caribou in east-central Alaska to assess their migratory strategy (as indexed by the distance between winter and summer ranges) and how this might affect their movements. Employing 41,682 movement vectors from 39 of these individuals equipped with GPS collars, I compared the annual and monthly movements of caribou that were found on different winter ranges. Distances between winter and summer ranges for individual caribou were correlated with their annual movement, but not for caribou that wintered within the same area. As expected, caribou with the greatest distance between their winter and summer ranges (300 km) traveled the most annually (2132 km/year), whereas caribou with the shortest distance between ranges (71 km) traveled the least annually (1368 km/year). However, caribou that migrated the furthest exhibited greater movement rates in all non-migratory summer months and most non-migratory winter months, as well as during migration. Movement rates were the greatest in summer, peaking in July, regardless of where caribou wintered. During the winter months, movement rates were similar among caribou found on different winter ranges and decreased over the winter, reaching minimums in January-March. Caribou that migrated the shortest distance and had lower movement rates tended to be found in smaller groups in summer. The connection between group size and movement rates may be a function of competition or a small-scale example of the larger-scale phenomenon of range expansion of large herds. Environmental factors, such as snow depth and temperature, were also correlated (negatively and positively, respectively) with caribou movement rates. Survival was not significantly different for caribou utilizing different winter ranges, which implies that the benefits of this long-distance migration can be offset by its costs. A more detailed understanding of the drivers and variability of caribou movement should help improve the management of this declining species. Full article

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models. Full article

Broiler gait score (GS) evaluation relies on manual assessments by experts, which can be laborious, hindering timely welfare management. Deep learning (DL) models, conversely, may serve as a cost-effective solution in evaluating GS via automated detection of broiler mobility. This study aimed to develop a vision-based YOLOv8 model to detect the locations of individual broilers, allowing for continuous tracking of birds within a pen and determining bird walking distances, speeds, idleness and movement ratios, and time at the feeder and drinker ratios. Then, Machine Learning (ML) models were developed to estimate the GS level from the mobility indicators in a lab setting. Ten broilers were color-coded and recorded via a top-view camera for 41 days. Their GS were assessed manually twice per week. The YOLOv8 model was trained, validated, and tested with 600, 150, and 50 images, respectively, and subsequently applied to the dataset yielding each broiler’s mobility indicators. The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. The YOLOv8 model was developed with 91% training, 89% testing, and 87% validation mean average precision (mAP) accuracies in identifying color-coded broilers. After tracking, the model estimated an average of 472.26 ± 234.18 cm hourly distance traveled and 0.13 ± 0.07 cm/s speed by a broiler. It was found that with deteriorated GS levels (i.e., worse walking ability), broilers walked shorter distances (p = 0.001), had lower speeds (p = 0.001), were increasingly idle and less mobile, and were increasingly stationed near or around the feeder. The movement ratio, average hourly walking distance, hourly average speed, and age variables were found to be the most significant variables (p < 0.005) in predicting GS levels. These variables were further reduced to one, the average hourly walking distance, because of high collinearity and were used to predict the GS with ML models. The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy. Full article

Optimizing the allocation of basic educational facilities in mountainous rural areas is important for narrowing the education gap between urban and rural areas, constructing high-quality regional education systems, and achieving sustainable education development. This paper considered preschool, primary, and secondary schools in Qixingguan District, which is located in a mountainous area of China, using vector data of rural residential areas and educational facility points as a source of information on supply and demand. The study combined travel modes and acceptable time of rural school-age population, and applied the Gaussian two-step mobile search method to calculate the level of accessibility of basic educational facilities at the scale of residential areas. Location optimization and scale optimization models were used to determine the optimal location and service qualities for basic educational facilities. Our results yielded three main conclusions. First, the spatial pattern for the distribution density and accessibility of basic educational facilities in Qixingguan differed at all stages, but all of them showed a strong orientation toward the central urban area. Service capacity in each stage tended to extend toward the northeast and southwest, except for a certain orientation toward the central urban area. Second, the main reason for the low spatial accessibility of schools was that the density and service capacity of the available schools did not align with the distribution of the school-age population. Third, after optimizing for location and service capacity, schools at all stages shifted to the northeast of Qixingguan, which reduced the difference in service capacity between schools and improved the accessibility and balance of schools in the northeast and southwest. Full article

Cutaneous leishmaniasis (CL) is a vector-borne infection caused by protozoan parasites belonging to the genus Leishmania. CL is an emerging global health concern due to increasing migration, travel, and climate change. Traditionally, it was confined to endemic regions such as the Americas, the Middle East, and Central Asia; however, it is now spreading to non-endemic areas. Climate change has further contributed to the expansion of sandfly habitats, increasing CL transmission risk in previously unaffected areas. Healthcare providers in non-endemic regions often misdiagnose CL, delaying treatment and morbidity. Diagnosis remains challenging due to the need for species-specific identification, while treatment is limited by cost, availability, and personnel expertise. This review explores the epidemiology, clinical presentation, diagnostic challenges, and management of CL in the context of global mobility. It highlights rising CL cases in refugee settlements, particularly in Lebanon and Jordan, due to poor living conditions, inadequate vector control, and healthcare barriers. While there have been advances in systemic and topical therapies, access in refugee and resource-poor settings remains a barrier. Addressing the global burden of CL requires improved surveillance, healthcare provider training, and increased awareness. By enhancing global collaboration and policy changes, public health efforts can mitigate the expanding impact of CL. Full article

Urban carbon emissions account for 75% of the total social emissions and are a key area for achieving the country’s “dual carbon” goals. This study takes the Sino-Singapore Tianjin Eco-City as a case, constructs a multi-dimensional carbon emission accounting model, integrates six systems, including buildings, transportation, water systems, solid waste, renewable energy, and carbon sinks, and proposes a comprehensive research method that takes into account both long-term prediction and a short-term dynamic analysis. The long-term emission trends under different scenarios are simulated through the KAYA model. It is found that under the enhanced low-carbon scenario, the Eco-City will reach its peak in 2043 (2.253 million tons of CO₂) and drop to 2.182 million tons of CO₂ in 2050. At the same time, after comparing models, such as random forest and support vector machine, the XGBoost algorithm is adopted for short-term prediction (R² = 0.984, MAE = 0.195). The results show that it is significantly superior to traditional methods and can effectively capture the dynamic changes in fields, such as buildings and transportation. Based on the prediction results, the study proposes six types of collaborative emission-reduction paths: improving building energy efficiency (annual emission reduction of 93800 tons), promoting green travel (58,900 tons), increasing the utilization rate of non-conventional water resources (3700 tons), reducing per capita solid waste generation (14,400 tons), expanding the application of renewable energy (288,200 tons), and increasing green space carbon sinks (135,000 tons). The total annual emission-reduction potential amounts to 594,000 tons. This study provides a valuable reference for developing carbon reduction strategies in urban areas. Full article

In this study, we developed an application for radiation protection that calculates in real time the distribution of scattered radiation during fluoroscopy using ray tracing technology, assuming that most of the scattered radiation in the room originates from the patient and that the scattered radiation originating from the patient travels linearly. The directional vectors and energy information for the scattered radiation spreading from the patient’s body surface to the outside of the body were obtained via simulation in a virtual X-ray fluoroscopy room. Based on this information, the scattered dose distribution in the X-ray room was calculated. The ratio of the scattered doses calculated by the method to those obtained from the Monte Carlo simulation was mostly within the range of 0.7 to 1.8 times, except for behind the X-ray machine. The scattered radiation distribution changed smoothly as the radiation protective plates were moved. When using protection plates with a high degree of freedom in their placement, it is not practical to measure the scattered radiation distribution each time. This application cannot be used for dose estimation for medical staff in clinical settings because it does not take into account the scattered radiation of non-patients and its dose calculation accuracy is low. However, the simple confirmation of the scattered radiation distribution and changes in staff dose led to an intuitive understanding of the appropriate placement of the protection plates. Full article

Expo parks are characterized by dense crowds and a high risk of accidents. A multi-robot patrolling system equipped with multiple sensors can provide personalized services to visitors and quickly locate emergencies, effectively accelerating response times. This study focuses on developing efficient patrolling strategies for multi-robot systems. In expo parks, this requires solving the multiple traveling salesman problem (MTSP) and addressing multi-robot obstacle avoidance in static environments. The main challenge is to plan paths and allocate tasks effectively while avoiding collisions and balancing workloads. Traditional methods often struggle to optimize task allocation and path planning at the same time. This can lead to an unbalanced distribution of patrol tasks. Some robots may have too much workload, while others are not fully utilized. In addition, poor path planning may increase the total patrol length and reduce overall efficiency. It can also affect the coordination of the multi-robot system, limiting its scalability and applicability. To solve these problems, this paper proposes a multi-objective immune optimization algorithm based on the Van der Waals force mechanism (VDWF-MOIA). It introduces an innovative double-antibody coding scheme that adapts well to environments with obstacles, making it easier to represent solutions more diversely. The algorithm has two levels. At the lower level, the path cost matrix based on vector rotation-angle-based obstacle avoidance (PCM-VRAOA) calculates path costs and detour nodes. It effectively reduces the total patrol path length and identifies optimal obstacle avoidance paths, facilitating collaborative optimization with subsequent task allocation. At the higher level, a crossover operator inspired by the Van der Waals force mechanism enhances solution diversity and convergence by enabling effective crossover between antibody segments, resulting in more effective offspring. The proposed algorithm improves performance by enhancing solution diversity, speeding up convergence, and reducing computational costs. Compared to other algorithms, experiments on test datasets in a static environment show that the VDWF-MOIA performs better in terms of total patrol path length, load balancing metrics, and the hypervolume (HV) indicator. Full article

Oropouche virus (OROV) is a neglected and emerging arbovirus that infects humans and animals in South and Central America. OROV is primarily transmitted to humans through the bites of infected midges and possibly some mosquitoes. It is the causative agent of Oropouche fever, which has high morbidity but low mortality rates in humans. The disease manifests in humans as high fever, headache, myalgia, arthralgia, photophobia, and, in some cases, meningitis and encephalitis. Additionally, a recent report suggests that OROV may cause fetal death, miscarriage, and microcephaly in newborns when women are infected during pregnancy, similar to the issues caused by the Zika virus (ZIKV), another mosquito-borne disease in the same regions. OROV was first reported in the mid-20th century in the Amazon basin. Since then, over 30 epidemics and more than 500,000 infection cases have been reported. The actual case numbers may be much higher due to frequent misdiagnosis, as OROV infection presents similar clinical symptoms to other co-circulating viruses, such as dengue virus (DENV), chikungunya virus (CHIKV), ZIKV, and West Nile virus (WNV). Due to climate change, increased travel, and urbanization, OROV infections have occurred at an increasing pace and have spread to new regions, with the potential to reach North America. According to the World Health Organization (WHO), over 10,000 cases were reported in 2024, including in areas where it was not previously detected. There is an urgent need to develop vaccines, antivirals, and specific diagnostic tools for OROV diseases. However, little is known about this surging virus, and no specific treatments or vaccines are available. In this article, we review the most recent progress in understanding virology, transmission, pathogenesis, diagnosis, host–vector dynamics, and antiviral vaccine development for OROV, and provide implications for future research directions. Full article

CHIKV infection is transmitted by Aedes mosquitoes spp., with Ae. aegypti considered as the primary vector and Ae. Albopictus playing an important role in sustaining outbreaks in Europe. The ECSA-Indian Ocean Lineage (IOL) strain emerged in Reunion, subsequently spreading to areas such as India, the Indian Ocean, and Southeast Asia, also causing outbreaks in naive countries, including more temperate regions, which originated from infected travelers. In Italy, two authocthounous outbreaks occurred in 2007 (Emilia Romagna region) and 2017 (Lazio and Calabria regions), caused by two different ECSA-IOL strains. The phylogenetics, evolution, and phylogeography of ECSA-IOL-CHIKV strains causing the 2007 and 2017 outbreaks in Italy were investigated. The mean evolutionary rate and time-scaled phylogeny were performed through BEAST. Specific adaptive vector mutations or key signature substitutions were also investigated. The estimated mean value of the CHIKV E1 evolutionary rate was 1.313 × 10⁻³ substitution/site/year (95% HPD: 8.709 × 10⁻⁴–1.827 × 10⁻³). The 2017 CHIKV Italian sequences of the outbreak in Lazio and of the secondary outbreak in Calabria were located inside a sub-clade dating back to 2015 (95% HPD: 2014–2015), showing an origin in India. Continued genomic surveillance combined with phylogeographic analysis could be useful in public health, as a starting point for future risk assessment models and early warning. Full article

The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussions of the TSP, the issues of improving convergence and optimization capability are still open. In this study, we aim to address this issue by proposing a new algorithm named IDINFO (Improved version of the discretized INFO). The proposed IDINFO is an extension of the INFO (weighted mean of vectors) algorithm in discrete space with optimized searching strategies. It applies the multi-strategy search and a threshold-based 2-opt and 3-opt local search to improve the local searching ability and avoid the issue of local optima of the discretized INFO. We use the TSPLIB library to estimate the performance of the IDINFO for the TSP. Our algorithm outperforms the existing representative algorithms (e.g., PSM, GWO, DSMO, DJAYA, AGA, CNO_PSO, Neural-3-OPT, and LIH) when tested against multiple benchmark sets. Its effectiveness was also verified in the real world in solving the TSP in short-distance delivery. Full article