Search Results (145)

Background: This pilot study examined whether oxidative–nitrosative stress is associated with clinical status in amyotrophic lateral sclerosis (ALS). We analyzed associations between plasma markers of oxidative–nitrosative imbalance and ALSFRS–R, disease duration, survival, and routine biochemical parameters. Methods: Twenty-nine ALS patients fulfilling the Gold Coast diagnostic criteria were enrolled. Plasma levels of 3-nitrotyrosine (3–NT), 8-oxo-2′-deoxyguanosine (8–oxodG), malondialdehyde (MDA), glutathione (GSH), non-protein thiols (NP–SH), and non-protein disulfides (NP–SS–NP), as well as creatinine, urea, uric acid and BMI, were measured. Associations with ALSFRS–R and disease duration were evaluated using non-parametric correlation analyses and second-order polynomial regression (adjusted R²), while survival was explored using Kaplan–Meier analysis and multivariable Cox regression. Given the modest sample, we considered statistical power and applied Benjamini–Hochberg false discovery rate (FDR) correction within marker families. Results: At the uncorrected significance level, 3–NT showed a positive correlation with ALSFRS–R and a negative correlation with disease duration, and NP–SH correlated negatively with disease duration; however, these associations did not remain significant after FDR correction (FDR-adjusted p ≥ 0.099). Other oxidative–nitrosative markers and biochemical parameters showed no robust relationships with clinical measures. In Cox models, 3–NT was not significantly associated with survival (HR 3.44 per 1 nM, 95% CI 0.25–47.97, p = 0.358), whereas older age predicted higher mortality (HR 1.05 per year, 95% CI 1.00–1.10, p = 0.036). Conclusions: 3–NT and NP–SH exhibited the strongest trends among the investigated markers, but their clinical associations in this small cross-sectional cohort remain exploratory and require confirmation in larger longitudinal studies. Full article

In light of the global issue of “Carbon Neutrality”, a high proportion of renewable energy integrated into modern power systems has become the key to energy strategic transformation, which has escalated the demand for high-gain, high-power converters for DC energy conversion. In this paper, a non-isolated double-layer multi-port parallel-connected high-gain DC–DC conversion system has been proposed. The system consists of two energy layers: the upper layer is designed as a non-isolated high-gain three-port DC conversion topology, which includes two energy inputs and one output port, and the bottom layer is a three-port constant current output module. The output ports of these layers are connected in parallel, while the input ports are independent. Thus, both high output voltage gain and power capacity were fulfilled for the renewable power application condition. The system is capable of operating in both input-parallel–output-parallel (IPOP) and multi-input–independent-output-parallel (MIIOP) modes, thereby enabling multi-port high-gain DC power conversion. Detailed analysis of the operation strategies under a switching cycle for both energy layers is presented. A small signal was introduced to establish the mathematical model of both energy topologies. In order to simultaneously regulate the output voltage and achieve dynamic current sharing between the layers, an adaptive current-sharing control strategy was developed based on the established system models. The proposed control strategy can control the output voltage through the upper-layer topology and dynamically allocates output current between the layers based on the output power level, which will effectively enhance the system’s power rating. The simulation mode was built in the PSIM environment, open-loop simulations were carried out for obtaining system characteristics, and closed-loop simulations were conducted for control efficiency validation. Finally, a 2000-W experimental prototype was developed based on the digital control center dsPIC33FJ64GS606. Open-loop and closed-loop experiments were carried out for system performance evaluation. Both simulation and experimental results successfully evaluated the power transfer performance and control system performance of the proposed system, and a peak efficiency of 95.7% under 10 times voltage gain was achieved. Full article

Currently, with the widespread popularization of e-commerce systems, enterprises have increasingly high requirements for the timeliness of order fulfillment. It has become particularly critical to enhance the operational efficiency of heterogeneous robotic “goods-to-person” (G2P) systems in book e-commerce fulfillment, reduce enterprise operational costs, and achieve highly efficient, low-carbon, and sustainable warehouse management. Therefore, this study focuses on determining the optimal storage location assignment strategy and order batching method. By comprehensively considering the characteristics of book e-commerce, such as small-batch, high-frequency orders and diverse SKU requirements, as well as existing system issues including uncoordinated storage assignment and order processing, and differences in the operational efficiency of heterogeneous robots, this study proposes a joint optimization framework for storage location assignment and order batching centered on a multi-objective model. The framework integrates the time costs of robot picking operations, SKU turnover rates, and inter-commodity correlations, introduces the STCSPBC storage strategy to optimize storage location assignment, and designs the SA-ANS algorithm to solve the storage assignment problem. Meanwhile, order batching optimization is based on dynamic inventory data, and the S-O Greedy algorithm is adopted to find solutions with lower picking costs. This achieves the joint optimization of storage location assignment and order batching, improves the system’s picking efficiency, reduces operational costs, and realizes green and sustainable management. Finally, validation via a spatiotemporal network model shows that the proposed joint optimization framework outperforms existing benchmark methods, achieving a 45.73% improvement in average order hit rate, a 48.79% reduction in total movement distance, a 46.59% decrease in operation time, and a 24.04% reduction in conflict frequency. Full article

This study examined whether layering spatial partitions and symbolic identity cues can transform underdefined university lounges into supportive social environments. In a within-subjects experiment, 60 architecture students experienced three lounge configurations in random order: (A) baseline, (B) partitions presented via mixed reality (MR), and (C) partitions plus identity cues via MR. After each condition, participants rated perceived territorial characteristics (PTC; spatial comfort, openness, territorial control, and symbolic identity) and sense of community (SOC; need fulfillment, membership, influence, and emotional connection). Repeated-measures ANOVAs with sphericity corrections showed stepwise increases (A < B < C) for territorial outcomes—overall PTC, spatial comfort, territorial control, and symbolic identity (ηp² ≈ 0.61–0.70, all p < 0.001). Openness remained high but was unchanged across conditions and unrelated to SOC (all p > 0.05). For community outcomes, improvements emerged only in Design C across the total score and all subdimensions (A = B < C; ηp² ≈ 0.17–0.28; all p ≤ 0.01). MR presence did not differ between B and C (all p > 0.05), indicating that gains in C are attributable to territorial/identity cues rather than immersion. Design-wise correlation analyses revealed positive links between territorial control/symbolic identity and SOC indices (r ≈ 0.45–0.78). The findings support a complementary design framework: permeable partitions make boundaries salient, and symbolic identity cues add shared meaning, modestly strengthening community connection. MR serves as a context-embedded preoccupancy evaluation platform for capturing psychosocial responses to spatial design. Full article

Grassland communities consist primarily of perennial herbaceous species, with grasses forming a dominant or significant component. These ecosystems have been utilised for economic purposes since the earliest periods of human history. In the natural environment, they fulfil numerous critical functions that, despite increasing awareness of climate change, often remain undervalued. Grasslands contribute directly to climate regulation, air purification, soil conservation, flood mitigation, and public health—all of which positively affect the well-being of nearby populations. Moreover, they satisfy higher-order human needs known as “cultural” services, providing aesthetic enjoyment and recreational opportunities. These services, in tangible terms, support the development of rural tourism. The objective of this study was to examine the perception of cultural ecosystem services provided by different types of grassland communities—meadows, pastures, and lawns. The study employed a structured questionnaire to evaluate the perceived significance and functions of these communities. Respondents assessed their aesthetic and recreational value based on land-use type. To quantify these dimensions, the study applies the Recreational and Leisure Attractiveness Index (RLAI), the Aesthetic Attractiveness Index (AAI), ranking methods, and contingent valuation techniques. Based on the respondents’ declared WTP (willingness to pay) and WTA (willingness to accept) values, statistically significant differences in the perceived value of land-use types were identified. Lawns were rated highest in terms of recreational attractiveness, meadows in terms of aesthetics, while pastures achieved the highest economic values. Significant differences were also observed depending on respondents’ place of residence and academic background. The results indicate that the valuation of cultural services encompasses both functional and psychological aspects and should be integrated into local land-use and landscape planning policies. Full article

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands. Full article

Front distribution centers are extensively employed in E-commerce distribution networks to shorten the delivery time, thereby stimulating customers’ purchase intentions and enhancing customer loyalty. When a customer places an order, the designated front distribution center quickly processes it to ensure prompt delivery. If the front distribution center is out of stock, the order will be fulfilled by its corresponding regional distribution center, which will result in a longer delivery time. Once the regional distribution center is also out of stock, a lost sale occurs. This paper improves a distributionally robust allocation model aimed at enhancing the fulfillment rates of front distribution centers while also preserving the overall fulfillment rate within the region. We reformulate this distributionally robust allocation model into an equivalent mixed-integer linear programming model and develop a corresponding approximation algorithm. Through numerical experiments, we comprehensively reveal the impact of moment information in demand forecasting on the distributionally robust fulfillment rate improvement algorithm by discovering how demand forecasting influences the allocation rule and how forecasted variance influences the fulfillment rates at fixed or changing inventory levels. Full article

Introduction: Alexithymia is a multidimensional construct characterized by difficulties in identifying and describing emotions, distinguishing emotional states from bodily sensations, and an externally oriented thinking style. Although the prevalence in the general population is around 10%, it is significantly higher in clinical groups, including those with autism spectrum disorders, depression, anxiety, and neurological conditions. Neuroimaging research, especially using magnetic resonance imaging, has documented structural and functional alterations in alexithymia; however, electroencephalography (EEG)—an older yet temporally precise method—remains less comprehensively explored. This mechanistic review aims to synthesize EEG-based evidence of the neural correlates of alexithymia and to propose potential neurophysiological mechanisms underpinning its affective and cognitive dimensions. Methods: A thorough literature search was conducted in December 2024 across PubMed/Medline, ResearchGate, Google Scholar, and Cochrane using combined keywords (“EEG”, “QEEG”, “electroencephalography”, “alexithymia”) to identify English-language clinical trials or case studies published from January 1980 to December 2024. Two reviewers independently screened the titles and abstracts, followed by a full-text review. Studies were included if they specifically examined EEG activity in participants with alexithymia. Of the 1021 initial records, eight studies fulfilled the inclusion criteria. Results: Across the reviewed studies, individuals with alexithymia consistently demonstrated right-hemisphere dominance in EEG power and connectivity, particularly in the theta and alpha bands, during both neutral and emotion-eliciting tasks. Many exhibited reduced interhemispheric coherence and disrupted connectivity in the frontal and parietal regions, potentially contributing to difficulties in cognitive processing and emotion labeling. Some studies have also reported diminished gamma band activity and phase synchrony in response to negative stimuli, suggesting impaired higher-order integration of emotional information. Crucially, subjective reports (e.g., valence ratings) often do not differ between alexithymic and non-alexithymic groups, highlighting that EEG measures may capture subtle emotional processing deficits not reflected in self-reports. Conclusions: EEG findings emphasize that alexithymia involves specific disruptions in cortical activation and network-level coordination, rather than merely the absence of emotional experiences. Right-hemisphere over-reliance, reduced interhemispheric transfer, and atypical oscillatory patterns in the alpha, theta, and gamma bands appear to be central to the condition’s pathophysiology. Understanding these neural signatures offers avenues for future research—particularly intervention studies that test whether modulating these EEG patterns can improve emotional awareness and expression. These insights underscore the potential clinical utility of EEG as a sensitive tool for detecting and tracking alexithymic traits in both research and therapeutic contexts. Full article

Objective: The aim of this systematic review is to compare the survival rates of zirconia and titanium dental implants, by evaluating the most recent scientific evidence, in order to comprehend the behaviour of zirconia implants as an alternative to titanium, due to the latter’s biological properties. Methods: An electronic search was performed on the Pubmed/MEDLINE and Scopus databases in November 2023 to identify clinical trials that investigated zirconia and titanium implants’ behaviour with a follow-up of at least 5 years. The primary outcome was the implant survival rate—defined as the maintenance of the implant in situ during the period of study. The secondary outcome was the implant success rate, which is associated with the values of the peri-implant variables—the probing depth, marginal bone loss, gingival recession, bleeding on probing, plaque index, and aesthetics scores. Results: A total of 17 articles were selected from the search, resulting in a sample of 364 studies. A total of 15 articles fulfilled the selection criteria. Zirconia implants showed satisfactory results. Due to the lack of data available with follow-up times of more than five years, it is not possible to conclusively describe the benefits of zirconia in comparison with titanium implants. Conclusions: While zirconia implants show promise as a future alternative to metal implants, more research is needed to understand their long-term benefits and peri-implant behaviour. Full article

Background/Objectives: Obinutuzumab was approved for front-line treatment of chronic lymphocytic leukemia in combination with chlorambucil pulses administered every 2 wks. Alternative schedules of chlorambucil enable the administration of higher total chlorambucil doses, and have better antileukemia activity. So far, evidence on the feasibility of combining obinutuzumab with alternative chlorambucil schedules is lacking. We performed this retrospective analysis to analyze real life outcomes in chronic lymphocytic leukemia patients receiving a combination of obinutuzumab with different chlorambucil schedules. Methods: This was a retrospective survey performed in order to analyze the feasibility and efficacy of different obinutuzumab and chlorambucil combinations in a real-life setting. Patients receiving this combination as a front-line therapy for chronic lymphocytic leukemia in participating centers, outside of clinical trials, in 2017 and 2018 were included. Results: Seventy-three patients fulfilling entry criteria were identified. Their median age was 76 years, and ranged from 58 to 90 years. The median follow up time was 59 months. The response rate was 89%, with a median progression-free survival time of 27 months, and an overall survival time of 49 months. Chlorambucil was administered as planned in 15 of the 22 (79%) patients treated with chlorambucil pulses every 2 weeks; in 15 of the 42 (34%) patients treated with 7-day courses of chlorambucil administered every 4 weeks; and in 0 of the 10 patients treated with a continuous high dose of chlorambucil (p = 0.002). Changes in treatment schedules were made due to side effects. The progression-free and overall survival rates were similar between the three groups. Conclusions: The combinations of obinutuzumab with more intensive chlorambucil schedules are less feasible, preventing the administration of the intended higher total dose of chlorambucil, and do not improve outcomes in comparison to chlorambucil pulses administered every 2 weeks. Full article

Aiming at the difficulty of controlling the longitudinal relative position of agricultural machines during the agricultural master–slave navigation cooperative operation and the weak adaptability of the unitary traditional control method in the face of the working conditions of complex farmland environments, this paper proposes a supervised reinforcement learning (SRL)-based longitudinal stable and safe control method applicable to master–slave navigation harvesting and unloading operations. Firstly, to improve the algorithm training success rate, a supervisor trained on actual driving data is introduced into the actor–critic reinforcement learning method. Secondly, in order to improve the effect of agricultural machine operation, considering the actual grain unloading operation scene and combining the smoothness of operation and the safety of unloading, a new reward function in the supervised reinforcement learning algorithm is designed. Finally, the performance of the proposed SRL control strategy is verified by simulation and agricultural machines following grain unloading tests. The results of field operation show that, when the harvester speed is 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.048 m, and the maximum deviation of the steady state is 0.26 m. In the variable speed test, when the harvester speed is 0.4 m/s and 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.079 m and 0.091 m, and the maximum deviation of the steady state is 0.20 m and 0.21 m, and the cooperative accuracy can fulfill the operational demands of harvesting cooperative unloading. The study’s results can serve as a technological reference for autonomous harvesting operations in the field. Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users’ demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints. Full article

Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors. Full article

In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks. Full article