Search Results (248)

Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for aircraft cruising based on proximal policy optimization (PPO) with nonlinear attitude constraints. This method first introduces a combination of long short-term memory (LSTM) and a fully connected layer (FC) to form the policy network of the PPO method, improving the algorithm’s learning efficiency for sequential data while avoiding feature compression. Secondly, it transforms cruise control into tracking target heading, altitude, and speed, achieving a mapping from motion states to optimal control actions within the policy network, and designs nonlinear constraints as the maximum reward intervals for pitch and roll to mitigate abnormal attitudes during maneuvers. Finally, a JSBSim simulation platform is established to train the network parameters, obtaining the optimal strategy for cruise control and achieving precise end-to-end control of the aircraft. Experimental results show that, compared to the cruise control method without dynamic constraints, the improved method reduces heading deviation by approximately 1.6° during ascent and 4.4° during descent, provides smoother pitch control, decreases steady-state altitude error by more than 1.5 m, and achieves higher accuracy in overlapping with the target trajectory during hexagonal trajectory tracking. Full article

This narrative review examines the effects of caffeine on brain health in older adults, with particular attention to its potential for dependence—an often-overlooked issue in geriatric care. Caffeine acts on central adenosine, dopamine, and glutamate systems, producing both stimulating and rewarding effects that can foster tolerance and habitual use. Age-related pharmacokinetic and pharmacodynamic changes prolong caffeine’s half-life and increase physiological sensitivity in the elderly. While moderate consumption may enhance alertness, attention, and possibly offer neuroprotective effects—especially in Parkinson’s disease and Lewy body dementia—excessive or prolonged use may lead to anxiety, sleep disturbances, and cognitive or motor impairment. Chronic exposure induces neuroadaptive changes, such as adenosine receptor down-regulation, resulting in tolerance and withdrawal symptoms, including headache, irritability, and fatigue. These symptoms, often mistaken for typical aging complaints, may reflect a substance use disorder yet remain under-recognized due to caffeine’s cultural acceptance. The review explores caffeine’s mixed role in neurological disorders, being beneficial in some and potentially harmful in others, such as restless legs syndrome and frontotemporal dementia. Given the variability in individual responses and the underestimated risk of dependence, personalized caffeine intake guidelines are warranted. Future research should focus on the long-term cognitive effects and the clinical significance of caffeine use disorder in older populations. Full article

Background: Anhedonia, defined as the diminished capacity to experience pleasure, represents a core negative symptom in first-episode psychosis (FEP) with profound implications for functional outcomes and long-term prognosis. Despite its clinical significance, comprehensive understanding of anhedonia prevalence, underlying mechanisms, and optimal intervention strategies in early psychosis remains limited. Objectives: To systematically examine the prevalence and characteristics of anhedonia in FEP patients, explore neurobiological mechanisms, identify clinical correlates and predictive factors, and evaluate intervention efficacy. Methods: Following PRISMA 2020 guidelines, we conducted comprehensive searches across PubMed, Embase, PsycINFO, and Web of Science databases from January 1990 to June 2025. Studies examining anhedonia and negative symptoms in FEP patients (≤24 months from onset) using validated assessment instruments were included. Quality assessment was performed using appropriate tools for study design. Results: Twenty-one studies comprising 3847 FEP patients met inclusion criteria. Anhedonia prevalence ranged from 30% at 10-year follow-up to 53% during acute phases, demonstrating persistent motivational deficits across illness trajectory. Factor analytic studies consistently supported five-factor negative symptom models with anhedonia as a discrete dimension. Neuroimaging investigations revealed consistent alterations in reward processing circuits, including ventral striatum hypofunction and altered network connectivity patterns. Social anhedonia demonstrated stronger associations with functional outcomes compared to other domains. Epigenetic mechanisms involving oxytocin receptor methylation showed gender-specific associations with anhedonia severity. Conventional antipsychotic treatments showed limited efficacy for anhedonia improvement, while targeted psychosocial interventions demonstrated preliminary promise. Conclusions: Anhedonia showed high prevalence (30–53%) across FEP populations with substantial clinical burden (13-fold increased odds vs. general population). Meta-analysis revealed large effect sizes for anhedonia severity in FEP vs. controls (d = 0.83) and strong negative correlations with functional outcomes (r =·−0.82). Neuroimaging demonstrated consistent ventral striatum dysfunction and altered network connectivity. Social anhedonia emerged as the strongest predictor of functional outcomes, with independent suicide risk associations. Conventional antipsychotics showed limited efficacy, while behavioral activation approaches demonstrated preliminary promise. These findings support anhedonia as a distinct treatment target requiring specialized assessment and intervention protocols in early psychosis care. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Credit supervision of practitioners in the water conservancy construction market, a vital pillar of national infrastructure development, significantly impacts project safety and the maintenance of order in the industry. From the perspective of indirect supervision, this study constructs a tripartite evolutionary game model involving government departments, enterprises, and practitioners to analyze the dynamic evolution mechanism of credit supervision. By examining the strategic interactions among the three parties under different regulatory scenarios, we identify key factors influencing the stable equilibrium of evolution and verify the theoretical conclusions through numerical simulations. The study yields several key insights. First, while government regulation and social supervision can substantially increase the likelihood of practitioners’ integrity, relying solely on administrative regulation has an efficiency limit. Second, the effectiveness of the reward and punishment mechanism of the direct manager plays a crucial leveraging role in credit evolution. Lastly, under differentiated regulatory strategies, high-credit practitioners respond more strongly to long-term cost optimization, while low-credit practitioners are more effectively deterred by short-term, high-intensity disciplinary actions. Based on these findings, this study proposes a systematic governance framework of “regulatory model innovation–corporate responsibility enhancement–social supervision deepening.” Unlike previous studies, this framework adopts a comprehensive approach from three dimensions: regulatory model innovation, corporate responsibility enhancement, and social supervision deepening. It offers a more holistic and systematic solution for refining the credit system in the water conservancy construction market, providing both theoretical support and practical approaches. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

Background/Objectives: Consuming comfort food is a common experience in daily life, but the underlying motives for engaging in comfort eating remain unclear. This study examined people’s expectations regarding their comfort food and investigated whether these expectations are associated with their frequency of comfort eating. As an exploratory aim, we also examined whether there are gender differences in preference for different categories of comfort food (i.e., sweet or savory) and the frequency of engaging in comfort eating. Methods: Through an online survey, participants (n = 214) reported their primary comfort food, the frequency of comfort eating in the short term (i.e., the past two weeks), and the general trend over the long term. They also rated statements related to their primary comfort food based on five expectation subscales (i.e., Manage Negative Affect; Pleasurable and Rewarding; Enhances Cognitive Competence; Alleviates Boredom; Positive Feelings). Results: Although Pleasurable and Rewarding and Positive Feelings received the strongest level of endorsement, their associations with the frequency-related variables were weak in both correlational and regression analyses. In contrast, Manage Negative Affect, Alleviates Boredom, and Enhances Cognitive Competence were positively associated with all frequency-related variables, with Alleviates Boredom showing the most consistent pattern. There were no significant gender differences in preferences for sweet or savory comfort food, and no significant gender differences in the frequency of eating comfort food. Conclusions: These findings suggest people believe they can gain a range of expected benefits from consuming comfort foods and perceive themselves as consuming comfort food primarily for rewarding themselves or gaining positive feelings. However, it is the expectations of managing negative affect, alleviating boredom, and enhancing cognitive competence that motivate them to engage in comfort eating. Full article

Ketamine, originally developed as an anesthetic, is gaining increasing attention due to its multifaceted pharmacological properties. In addition to its use in anesthesia, ketamine exerts potent analgesic effects via N-methyl-D-aspartate (NMDA) receptor antagonism, modulating pain perception and reducing central sensitization, particularly in chronic and neuropathic pain conditions. Emerging evidence also supports ketamine’s potential in the treatment of substance use disorder, where it may disrupt maladaptive reward-related memories and promote neuroplasticity which facilitates behavioral change. Moreover, in recent years, S-ketamine has shown rapid and potent antidepressant effects, especially in treatment-resistant depression (TRD), probably due to increased glutamatergic signaling, synaptic plasticity and the release of neurotrophic factors. This heterogeneous therapeutic profile positions ketamine as a unique agent at the interface of anesthesia, pain management, addiction medicine and psychiatry, warranting further exploration into its mechanism and long-term effectiveness. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs), which were originally developed for managing type 2 diabetes by enhancing insulin secretion and reducing appetite, have emerged as promising candidates in alcohol use disorder (AUD). These medications offer a dual mechanism of action that aligns with the multifaceted nature of addiction by targeting both peripheral metabolic and central reward pathways. This review focused on the current clinical trials and real-world evidence regarding the effects of GLP-1RAs as novel therapeutics for AUD. We also discussed early but encouraging results from clinical trials in AUD, observational and real-world evidence, safety profiles, psychiatric considerations, and future directions leading beyond GLP-1RAs. Methods: A comprehensive English-language literature search was conducted per PRISMA guidelines across PubMed, Medline, Google Scholar, Web of Science, and trial registries. Using targeted keywords, we identified relevant clinical and observational studies on GLP-1RAs for alcohol use disorder, excluding off-topic or non-English works and assessing all studies for eligibility. Results: Out of 1080 records identified, seven studies met the inclusion criteria. The findings from recent clinical trials, large-scale observational studies, and real-world evidence suggest that GLP-1RAs may significantly reduce alcohol consumption, cravings, and alcohol-related hospitalizations. Their central effect on reward processing, coupled with a generally favorable safety profile, supports their potential therapeutic role beyond metabolic disorders. Conclusions: Emerging evidence positions GLP-1RAs as a promising new pharmacologic approach for managing AUD. Ongoing and future research should prioritize larger, longer-duration randomized controlled trials that include diverse populations, with specific attention to treatment motivation, co-occurring psychiatric conditions, and long-term outcomes. Full article

Organisms respond to environmental stress primarily through the autonomic nervous system and hypothalamic–pituitary–adrenal (HPA) axis, regulating metabolism, psychological states, and immune function and modulating memory, reward processing, and immune responses. The HPA axis plays a central role in stress response, exhibiting distinct activation patterns under acute versus chronic social defeat stress. However, differences in physiological impacts and regulatory pathways between these stress conditions remain understudied. This study integrates RNA sequencing and behavioral analyses to reveal that acute social defeat stress triggers transient anxiety-like behaviors, accompanied by systemic inflammation and immediate-early gene (IEG) activation. In contrast, chronic social defeat stress induces long-term behavioral and physiological alterations, including neurotransmitter imbalance (e.g., reduced GABA and increased glutamate), sustained activation of maladaptive pathways (e.g., IL-17 signaling), and disrupted corticosterone synthesis. These findings highlight the dynamic regulatory role of the HPA axis under varying stress conditions, providing novel insights into mental health disorders such as anxiety and depression. The study identifies potential therapeutic targets to mitigate chronic social defeat stress effects and offers a theoretical foundation for personalized interventions. Full article

Background: Previous syntheses on the neural effects of alcohol have been restricted to tasks assessing craving, cognitive control, and reward processing. Despite extensive research, a comprehensive synthesis of functional magnetic resonance imaging (fMRI) findings on alcohol use disorder (AUD) remains lacking. This study aimed to identify consistent brain activation alterations across all cognitive and emotional tasks administered to individuals with AUD while distinguishing between short-term and long-term abstinence and using activation likelihood estimation meta-analysis. Sub-analyses on task types were performed. Methods: A systematic review identified 67 fMRI studies on participants with an AUD. Results: The meta-analysis revealed significant alterations in brain activity, including both hypo- and hyperactivation in the left putamen across all AUD participants. These alterations were observed more frequently during decision-making and reward tasks. Short-term abstinent individuals exhibited hypoactivation in the right middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex. In contrast, long-term abstinent individuals displayed hypoactivation in the right superior frontal gyrus (SFG) and dorsal anterior cingulate cortex (dACC). This meta-analysis highlights critical neural alterations in AUD, particularly in regions associated with reward processing (putamen), executive functions (MFG and SFG), and attentional salience (dACC). Putamen changes were predominantly observed during short-term abstinence and in decision-making, as well as reward processing tasks. dACC and SFG hypoactivation were specific to long-term abstinence, while MFG hypoactivation was specific to short-term abstinence. Conclusions: These findings support prior research indicating a motivational imbalance and persistent executive dysfunctions in AUD. Standardizing consumption metrics and expanding task diversity in future research is essential to further refine our understanding of the neural effects of AUD. Full article

This study aimed to identify the impact of the e-marketing mix on brand equity in the Jordanian banking sector. This study used a descriptive analysis approach. The study population consisted of all (13) commercial banks in Jordan listed on the Amman Stock Exchange. A random sample of administrative personnel at commercial banks was selected, including administrators and department heads. It was distributed electronically via Google Forms, and 293 questionnaires (79.1%) from the total study sample were retrieved. The SPSS program version 26 was used to examine the collected data. The results showed that there is a statistically significant effect at the level (α ≥ 0.05) of e-marketing on brand equity in the Jordanian banking sector. It was found that there is a positive correlation between e-marketing and the three dimensions of brand equity (brand loyalty, perceived quality, and brand image). Moreover, a positive brand image should be maintained through the consistent delivery of promises, excellent customer service, and a strong online reputation. Cultivating brand loyalty among customers can be achieved through personalized services, exclusive rewards, and tailored promotions. By integrating these strategies into their e-marketing efforts, banks can effectively engage with customers, differentiate themselves from competitors, and build long-term relationships. Full article