Search Results (8,119)

The electrical power system is composed of three essential sectors, generation, transmission, and distribution, with the latter being crucial for the overall efficiency of the system. Enhancing the capabilities of active distribution networks involves integrating various advanced technologies such as distributed generation units, energy storage systems, banks of capacitors, and electric vehicle chargers. This paper provides an in-depth review of the primary strategies for incorporating these technologies into the distribution network to improve its reliability, stability, and efficiency. It also explores the principal metaheuristic techniques employed for the optimal allocation of distributed generation units, banks of capacitors, energy storage systems, electric vehicle chargers, and network reconfiguration. These techniques are essential for effectively integrating these technologies and optimizing the active distribution network by enhancing power quality and voltage level, reducing losses, and ensuring operational indices are maintained at optimal levels. Full article

Rhabditis (Rhabditella) axei is a predominantly free-living nematode commonly found in sewage systems and decomposing organic matter. While primarily saprophytic, it has been documented as an opportunistic pathogen in human urinary and gastrointestinal tracts. The Chinese red panda (Ailurus styani), a rare and protected species in China, has not previously been reported as a host for Rhabditis (Rhabditella) spp. infections. This study reports the first documented occurrence of R. axei in red panda feces, unambiguously confirmed through integrative taxonomic approaches combining morphological and molecular analyses. The nematodes exhibited key morphological features consistent with R. axei, including a cylindrical rhabditiform esophagus, sexually dimorphic tail structures, and diagnostic spicule morphology. Molecular analysis based on 18S-ITS-28S rDNA sequencing confirmed their identity, showing >99% sequence similarity to R. axei reference strains (GenBank: PP135624.1, PP135622.1). Phylogenetic reconstruction using 18S rDNA and ITS rDNA sequences placed the isolate within a well-supported R. axei clade, clearly distinguishing it from related species such as R. blumi and R. brassicae. The findings demonstrate the ecological plasticity of R. axei as a facultative parasite capable of infecting non-traditional hosts and further highlight potential zoonotic risks associated with environmental exposure in captive wildlife populations. Our results emphasize the indispensable role of molecular diagnostics in accurately distinguishing morphologically similar nematodes within the Rhabditidae family, while providing essential baseline data for health monitoring in both in situ and ex situ conservation programs for this endangered species. Full article

This paper selects 50 listed commercial banks in China from 2012 to 2023 as research samples, and employs the fixed effects model and Hansen’s threshold regression method to systematically examine the impact mechanism and non-linear characteristics of FinTech development on the profitability of commercial banks. The key findings are summarized as follows: (1) FinTech significantly undermines the overall profitability of commercial banks by reshaping the competitive landscape of the industry and intensifying the technology substitution effect. This is primarily reflected in the reduction in traditional interest income and the erosion of market share in intermediary business. (2) Heterogeneity analysis indicates that large state-owned banks and joint-stock banks experience more pronounced negative impacts compared to small and medium-sized banks. (3) Additional research findings reveal a significant single-threshold effect between FinTech and bank profitability, with a critical value of 4.169. When the development level of FinTech surpasses this threshold, its inhibitory effect diminishes substantially, suggesting that after achieving a certain degree of technological integration, commercial banks may partially alleviate external competitive pressures through synergistic effects. This study offers crucial empirical evidence and theoretical support for commercial banks to develop differentiated technology strategies and for regulatory authorities to design dynamically adaptable policy frameworks. Full article

Fertility is considered a significant demographic concern, especially in relation to climate change. This study examines how awareness of climate change, measured by five subscales—climate-friendly behavior, knowledge, personal concern, attitude, and multiplicative action—affects fertility intentions, emphasizing the mediating role of threat perception. Data were collected through an online survey administered to a sample of 817 Palestinian citizens aged 18–49 residing in the West Bank. Structural equation modeling (SEM) was utilized for the data analysis. The results revealed that climate change awareness does not directly affect fertility intentions. However, an indirect effect of climate change awareness on fertility intentions was observed, mediated by threat perception as an intervening variable. Individuals exhibiting increased awareness of climate change and perceptions of future risks demonstrated a greater likelihood of reducing their fertility intentions compared to others. Policymakers in the Palestinian territories should prioritize enhancing public awareness regarding climate change and its associated short- and long-term threats. Therefore, incorporating climate education and associated risks into fertility health programs is essential. Full article

Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats. Full article

Glioma is a type of neoplasia that spontaneously arises from the glial cells of the brain in humans and dogs, and its prognosis is grave. Current treatment options for glioma include surgery, radiation therapy, chemotherapy, or symptomatic treatment. Evidence has shown that cannabidiol (CBD) may have anticancer, anti-angiogenic, and anti-inflammatory properties in both in vitro and in vivo studies. In this in vivo murine experiment, the canine glioma cell line J3TBG was injected into the frontoparietal cortex of immunodeficient mice using xenogeneic tissue transplantation. A total of 20 mice were randomly assigned to one of four treatment groups—Control group (C), CBD group (CBD), Radiation Therapy group (RT), and CBD plus Radiation Therapy group (CBD + RT). After transplantation of J3TBG, a single fraction of 5.5 Gy RT was administered to the RT and CBD + RT groups, and CBD was administered daily to the CBD and CBD + RT groups. Necropsies were performed to collect blood and brain tissue. Although there was not a statistically significant difference, the survival time among mice were longer in the CBD + RT group than the RT group. These results indicate that CBD may be used as an adjunctive therapy to enhance RT treatment. Larger cohort studies are required to substantiate the hypothesis. Full article

This research examines the compounding risks of global environmental change, including climate change, environmental law, biodiversity loss, and pollution, which threaten the stability of economic systems worldwide. While digital technology and global governance regulation are increasingly being proposed as solutions, their synergistic potential in advancing economic sustainability has been less explored. How can these technologies mitigate environmental risks while promoting sustainable and equitable development, aligning with the Sustainable Development Goals? We analyze policy global environmental data from the World Bank and the United Nations, as well as literature reviews on digital interventions, artificial intelligence, and smart databases. Global environmental change presents economic stability and rule of law threats, and innovative governance responses are needed. This study evaluates the potential for digital technology to be leveraged to enhance climate resilience and regulatory systems and address key implementation, equity, and policy coherence deficits. Policy recommendations for aligning economic development trajectories with planetary boundaries emphasize that proactive digital governance integration is indispensable for decoupling growth from environmental degradation. However, fragmented governance and unequal access to technologies undermine scalability. Successful experiences demonstrate that integrated policies, combining incentives, data transparency, and multilateral coordination, deliver maximum economic and environmental co-benefits, matching digital innovation with good governance. We provide policymakers with an action plan to leverage technology as a multiplier of sustainability, prioritizing inclusive governance structures to address implementation gaps and inform legislation. Full article

The perianal skin is a unique “skin–gut” boundary that serves as a critical hotspot for the exchange and evolution of antibiotic resistance genes (ARGs). However, its role in the dissemination of antimicrobial resistance (AMR) has often been underestimated. To characterize the resistance patterns in the perianal skin environment of patients with perianal diseases and to investigate the drivers of AMR in this niche, a total of 51 bacterial isolates were selected from a historical strain bank containing isolates originally collected from patients with perianal diseases. All the isolates originated from the skin site and were subjected to antimicrobial susceptibility testing, whole-genome sequencing, and co-occurrence network analysis. The analysis revealed a highly structured resistance pattern, dominated by two distinct modules: one representing a classic Staphylococcal resistance platform centered around mecA and the bla operon, and a broad-spectrum multidrug resistance module in Gram-negative bacteria centered around tet(A) and predominantly carried by IncFIB and other IncF family plasmids. Further analysis pinpointed IncFIB-type plasmids as potent vehicles driving the efficient dissemination of the latter resistance module. Moreover, numerous unexplained resistance phenotypes were observed in a subset of isolates, indicating the potential presence of emerging and uncharacterized AMR threats. These findings establish the perianal skin as a complex reservoir of multidrug resistance genes and a hub for mobile genetic element exchange, highlighting the necessity of enhanced surveillance and targeted interventions in this clinically important ecological niche. Full article

This paper measures the post-treatment effects of Central Bank Digital Currencies (CBDCs) on mobile money and outstanding loans from commercial banks as a percentage of the GDP in Nigeria and the Bahamas, respectively, from the perspective of financial inclusion. The literature on the topic has primarily focused on the technological specifications of CBDCs and their potential future implementation. This article addresses a gap in the empirical literature by examining the effects of CBDCs. To this end, a Synthetic Control Method (SCM) is applied to the Bahamas (SandDollar) and Nigeria (eNaira) to construct a counterfactual scenario and assess the impact of CBDCs on mobile money and commercial bank loans. Nigeria’s mobile money transactions as a percentage of the GDP increased significantly compared to the synthetic control group, suggesting a notable positive effect of the eNaira. Conversely, in the Bahamas, actual performance fell below the synthetic control, implying that SandDollar may have contributed to a decline in outstanding loans. These results suggest that CBDCs could pose a “deposit substitution risk” for commercial banks. However, they may also enhance the performance of other Fintech tools, as observed in the case of mobile money. As CBDC implementations worldwide remain in their early stages, their long-term effects require further analysis. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

Background/Objectives: Due to the significant overlap in symptoms between COVID-19 and other respiratory infections, a multiplex PCR-based platform was developed to simultaneously detect 22 respiratory pathogens. Target sequences were retrieved from the GenBank database and aligned using Clustal Omega 2.1 to identify conserved regions prioritized for primer design. Primers were designed using Primer Express^® 3.0.1 and evaluated in Primer Explorer to ensure specificity and minimize secondary structures. A multiplex strategy organized primers into three groups, each labeled with distinct fluorophores (FAM, VIC, or NED), allowing for detection by conventional PCR or capillary electrophoresis (CE). Methods: After reverse transcription for RNA targets, amplification was performed in a single-tube reaction. A total of 340 clinical samples—nasopharyngeal and saliva swabs—were collected from patients, during the COVID-19 pandemic period. The automated analysis of electropherograms enabled precise pathogen identification. Results: Of the samples analyzed, 57.1% tested negative for all pathogens. SARS-CoV-2 was the most frequently detected pathogen (29%), followed by enterovirus (6.5%). Positive results were detected in both nasopharyngeal and saliva swabs, with SARS-CoV-2 predominating in saliva samples. Conclusion: This single-tube multiplex PCR-CE assay represents a cost-effective and robust approach for comprehensive respiratory pathogen detection. It enables rapid and simultaneous diagnosis, facilitating targeted treatment strategies and improved patient outcomes. Full article

Acacia mearnsii De Wild. has been introduced to over 150 countries for its economic value. However, it easily escapes from plantations and establishes monospecific stands across plains, hills, valleys, and riparian habitats, including protected areas such as national parks and forest reserves. Due to its negative ecological impact, A. mearnsii has been listed among the world’s 100 worst invasive alien species. This species exhibits rapid stem growth in its sapling stage and reaches reproductive maturity early. It produces a large quantity of long-lived seeds, establishing a substantial seed bank. A. mearnsii can grow in different environmental conditions and tolerates various adverse conditions, such as low temperatures and drought. Its invasive populations are unlikely to be seriously damaged by herbivores and pathogens. Additionally, A. mearnsii exhibits allelopathic activity, though its ecological significance remains unclear. These characteristics of A. mearnsii may contribute to its expansion in introduced ranges. The presence of A. mearnsii affects abiotic processes in ecosystems by reducing water availability, increasing the risk of soil erosion and flooding, altering soil chemical composition, and obstructing solar light irradiation. The invasion negatively affects biotic processes as well, reducing the diversity and abundance of native plants and arthropods, including protective species. Eradicating invasive populations of A. mearnsii requires an integrated, long-term management approach based on an understanding of its invasive mechanisms. Early detection of invasive populations and the promotion of public awareness about their impact are also important. More attention must be given to its invasive traits because it easily escapes from cultivation. Full article

The purpose of this research is to examine employee perceptions regarding the implementation of ESG (environmental, social, and governance) practices in financial institutions, with a comparative focus on urban and rural banks in the Republic of Serbia. The study investigates how employees assess environmental, social, and governance aspects of ESG, as well as their own role in applying these principles in everyday work. The results reveal statistically significant differences between the two groups; employees in urban banks report greater engagement, more access to training, and stronger involvement in ESG decision-making. These findings suggest the existence of more developed institutional support, infrastructure, and organisational culture in urban banks. In contrast, employees in rural banks highlight the need for enhanced training, clearer ESG guidance, and improved oversight mechanisms. The study underlines the importance of investing in employee development and internal communication, particularly in rural contexts, to improve ESG outcomes. By focusing on employee-level perceptions, this research contributes to the understanding of how organisational and geographic factors influence the implementation of ESG-related practices in financial institutions. Full article

Objective: This retrospective observational study evaluated the efficacy and safety of an ovarian stimulation protocol for embryo banking that involves continuous administration of clomiphene citrate (CC) in combination with gonadotropins, without the use of GnRH antagonists. Methods: Conducted at the Serum IVF Clinic in Athens, Greece, the study included 250 women aged 25–45 who underwent IVF for embryo banking. The protocol involved administering 150 mg of CC daily from day 2 of the menstrual cycle until the day before hCG trigger, alongside 150 IU/day of Meriofert. Outcomes assessed included oocyte yield, fertilization rates, incidence of ovarian hyperstimulation syndrome (OHSS), and hormonal correlations. Comparative and regression analyses explored differences between age groups and predictors of success. Results: The protocol demonstrated a favorable safety profile with no cases of OHSS and yielded a mean of 10.25 oocytes per patient. Group analysis showed significantly more oocytes retrieved in women under 40 (mean: 12.5) versus those over 40 (mean: 8.43), while fertilization rates were paradoxically higher in the older cohort (59.16% vs. 30.68%, p < 0.0001). Regression models revealed basal FSH to be a significant inverse predictor of oocyte yield, but it was positively associated with fertilization rate. Continuous CC use effectively suppressed premature LH surges without compromising oocyte or embryo quality, allowing flexible and cost-effective stimulation with minimal monitoring. Conclusions: Continuous administration of clomiphene citrate in combination with gonadotropins presents a promising, antagonist-free ovarian stimulation protocol for embryo banking. The approach is economically efficient, reduces monitoring requirements, and maintains safety and effectiveness and is particularly notable in women over 40. Further studies are warranted to validate these findings and refine protocol mechanisms. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article