Search Results (410)

Background/Objectives: Tegoprazan (TPZ) is a potassium-competitive acid blocker (P-CAB) used to treat conditions such as gastroesophageal reflux disease, peptic ulcer, and Helicobacter pylori infection. It exists in three solid forms: amorphous, Polymorph A, and Polymorph B. This study investigates the molecular basis of polymorph selection, focusing on conformational bias and solvent-mediated phase transformations (SMPTs). Methods: The conformational energy landscapes of two TPZ tautomers were constructed using relaxed torsion scans with the OPLS4 force field and validated by nuclear Overhauser effect (NOE)-based nuclear magnetic resonance (NMR). Hydrogen-bonded dimers were analyzed using DFT-D. Powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), solubility, and slurry tests were conducted using methanol, acetone, and water. Kinetic profiles were modeled with the Kolmogorov–Johnson–Mehl–Avrami (KJMA) equation. Results: Polymorph A was thermodynamically stable across all analyses. Both amorphous TPZ and Polymorph B converted to A in a solvent-dependent manner. Methanol induced direct A formation, while acetone showed a B → A transition. Crystallization was guided by solution conformers and hydrogen bonding. Conclusions: TPZ polymorph selection is governed by solution-phase conformational preferences, tautomerism, and solvent-mediated hydrogen bonding. DFT-D and NMR analyses showed that protic solvents favor the direct crystallization of stable Polymorph A, while aprotic solvents promote the transient formation of metastable Polymorph B. Elevated temperatures and humidity accelerate polymorphic transitions. This crystal structure prediction (CSP)-independent strategy offers a practical framework for rational polymorph control and the mitigation of disappearing polymorph risks in tautomeric drugs. Full article

Niacinamide was recently shown to directly interact with bacterial DNA and interfere with cell replication; niacinamide mode of interaction and efficacy as a natural anti-microbial molecule were also described. The aim of this study is to elucidate the exact binding mechanism of niacinamide to microbial DNA. Intercalation is a binding mode where a small planar molecule, such as niacinamide, is inserted between base pairs, causing structural changes in the DNA. Melting curve analysis with various intercalating dyes demonstrated that niacinamide interaction with bacterial DNA reduces its melting temperature in a linear dose-dependent manner. Niacinamide’s effect on the melting temperature was found to be % GC-dependent, while purine stretches were also found to influence the binding kinetics. Finally, fluorescent intercalator displacement (FID) assays demonstrated that niacinamide strongly reduces SYBR Safe signal in a dose-dependent manner. Interestingly, competition assays with a minor groove binder also reduced Hoechst signal but in a non-linear manner, which can be attributed to strand lengthening and unwinding following niacinamide intercalation. Taken altogether; our results suggest a “disruptive intercalation” as the mode of interaction of niacinamide with bacterial DNA. Formation of locally destabilized DNA portions by niacinamide might interfere with protein–DNA interaction and potentially affect several crucial bacterial cellular processes, e.g., DNA repair and replication, subsequently leading to cell death. Full article

The increasingly widespread concept of Industry 4.0 and Digital Twins, as well as the competitive global markets, make it essential to produce goods in a timely and cost-effective manner. Therefore, one of the most important requirements for current production scheduling systems is to automate the control of production progress and to quickly correct schedules in response to feedback from production machines while minimizing the number of changes in the order of tasks to be performed. In this paper, we propose a new extension to the dispatching rules, which takes into account the uncertainty of job processing times and allows for the production of robust schedules. The important feature of the proposed extension is that it does not increase the computational time of the underlying techniques, i.e., it has no negative impact on their most important advantage over other production scheduling algorithms. The results obtained from the real data of a large international offset printing company with various machine and job heterogeneities confirm the effectiveness of the proposed EPO extension in producing schedules that are indeed robust to changes in production time. Full article

This study explores the identification, characterization, and biological evaluation of angiotensin I-converting enzyme (ACE)-inhibitory peptides derived from enzymatic hydrolysates of crucian carp swim bladders. Following sequential purification by size-exclusion and reversed-phase chromatography, two bioactive peptides—Hyp-Gly-Ala-Arg (Hyp-GAR) and Gly-Ala-Hyp-Gly-Ala-Arg (GA-Hyp-GAR)—were identified using ultra-high-performance liquid chromatography coupled with linear ion trap–Orbitrap tandem mass spectrometry. The synthetic peptides demonstrated potent ACE-inhibitory activity in vitro, with IC₅₀ values of 12.2 μM (Hyp-GAR) and 4.00 μM (GA-Hyp-GAR). Molecular docking and enzyme kinetics confirmed competitive inhibition through key interactions with ACE active site residues and zinc coordination. In vivo antihypertensive activity was evaluated in spontaneously hypertensive rats, revealing that GA-Hyp-GAR significantly reduced systolic blood pressure in a dose-dependent manner. At a dose of 36 mg/kg, GA-Hyp-GAR reduced systolic blood pressure by 60 mmHg—an effect comparable in magnitude and timing to that of captopril. Mechanistically, GA-Hyp-GAR modulated levels of angiotensin II, bradykinin, endothelial nitric oxide synthase, and nitric oxide. A 90-day subchronic oral toxicity study in mice indicated no significant hematological, biochemical, or histopathological alterations, supporting the peptide’s safety profile. These findings suggest that GA-Hyp-GAR is a promising natural ACE inhibitor with potential application in functional foods or as a nutraceutical for hypertension management. Full article

Background: Location and distribution decisions are key to efficient logistics network design and are often addressed in an integrated manner as Location–Routing Problems (LRPs). Today, sustainability and resilience must be considered when designing competitive networks. This systematic review examines how and at what decision level both concerns are explored in LRPs, highlighting trends and future research challenges. Methods: A search was conducted in the Scopus database on 3 January 2024. Articles not written in English or lacking a sustainability or resilience focus were excluded. The 36 most-cited articles were selected and analyzed descriptively and theoretically, considering their approaches to sustainability and resilience, as well as the decision levels at which these approaches were considered. The studies were also analyzed based on model features and solving approaches. Results: Our findings indicated that social sustainability was the most neglected. The environmental pillar was often focused on minimizing atmospheric pollution from distribution. Regarding resilience, proactive and reactive strategies were employed to minimize disruption costs and risks and maximize network reliability. Conclusions: Research on sustainable and resilient LRPs is growing, but remains fragmented. Future studies should explore the integration of social impacts, uncertainty modeling, and real-world applications. Stronger alignment with decision maker needs and more holistic evaluation frameworks are essential to support resilient and sustainable network design. Full article

Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NSCT) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NSCT has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NSCT coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components. Full article

Over the last decade, a transformation in the automotive industry has been witnessed, as advancements in artificial intelligence and sensor technology have continued to accelerate the development of driverless vehicles. These systems are expected to significantly reduce traffic accidents and associated costs, making their integration into future transportation systems highly impactful. To explore this field in a controlled and flexible manner, scaled autonomous vehicle platforms are increasingly adopted for experimentation. In this work, we propose a set of methodologies to perform autonomous driving tasks through a software–hardware co-design approach. The developed system focuses on deploying a modular and reconfigurable software stack tailored to run efficiently on constrained embedded hardware, demonstrating a balance between real-time capability and computational resource usage. The proposed platform was implemented on a 1:10 scale vehicle that participated in the Bosch Future Mobility Challenge (BFMC) 2024. It integrates a high-performance embedded computing unit and a heterogeneous sensor suite to achieve reliable perception, decision-making, and control. The architecture is structured across four interconnected layers—Input, Perception, Control, and Output—allowing flexible module integration and reusability. The effectiveness of the system was validated throughout the competition scenarios, leading the team to secure first place. Although the platform was evaluated on a scaled vehicle, its underlying software–hardware principles are broadly applicable and scalable to larger autonomous systems. Full article

Background/Objectives: Rhodesain is a cysteine protease crucial for the life cycle of Trypanosoma brucei rhodesiense, a parasite that causes the lethal form of human African trypanosomiasis. For these reasons, rhodesain is considered an important target for the drug discovery process of novel antitrypanosomal agents. Methods: In the present work, we carried out a combination study of two novel synthetic nitriles, Nitrile 1 and Nitrile 2, with curcumin, the golden multitarget nutraceutical obtained from Curcuma longa L., which we demonstrated to inhibit rhodesain in a non-competitive manner. We calculated the combination index (CI) in both the combination studies by using the Chou and Talalay method. Results: Comparing the CI values of the combinations Nitrile 1 + curcumin and Nitrile 2 + curcumin, we assessed that the inhibitory effect of the combination Nitrile 2 + curcumin against rhodesain was much more potent than that of the other combination. At the IC₅₀ value, in the case of the combination Nitrile 1 + curcumin an additive effect occurred, while in the case of Nitrile 2 + curcumin, we observed a moderate synergism: at 99% of the effect, the synergism induced by the combination Nitrile 2 + curcumin was much stronger than the synergism promoted by the combination Nitrile 1 + curcumin (CI = 0.3843 vs 0.6622, respectively). Conclusions: The co-administration of dipeptide nitriles with curcumin enhances rhodesain inhibition through synergistic effects. Notably, Nitrile 2 + curcumin exhibits a stronger synergy at higher inhibition levels, indicating a greater therapeutic potential. Full article

The ongoing exploration of brain–computer interfaces (BCIs) provides deeper insights into the workings of the human brain. Motor imagery (MI) tasks, such as imagining movements of the tongue, left and right hands, or feet, can be identified through the analysis of electroencephalography (EEG) signals. The development of BCI systems opens up opportunities for their application in assistive devices, neurorehabilitation, and brain stimulation and brain feedback technologies, potentially helping patients to regain the ability to eat and drink without external help, move, or even speak. In this context, the accurate recognition and deciphering of a patient’s imagined intentions is critical for the development of effective BCI systems. Therefore, to distinguish motor tasks in a manner differing from the commonly used methods in this context, we propose a fractal dimension (FD)-based approach, which effectively captures the self-similarity and complexity of EEG signals. For this purpose, all four classes provided in the BCI Competition IV 2a dataset are utilized with nine different combinations of seven FD methods: Katz, Petrosian, Higuchi, box-counting, MFDFA, DFA, and correlation dimension. The resulting features are then used to train five machine learning models: linear, Gaussian, polynomial support vector machine, regression tree, and stochastic gradient descent. As a result, the proposed method obtained top-tier results, achieving 79.2% accuracy when using the Katz vs. box-counting vs. correlation dimension FD combination (KFD vs. BCFD vs. CDFD) classified by LinearSVM, thus outperforming the state-of-the-art TWSB method (achieving 79.1% accuracy). These results demonstrate that fractal dimension features can be applied to achieve higher classification accuracy for online/offline MI-BCIs, when compared to traditional methods. The application of these findings is expected to facilitate the enhancement of motor imagery brain–computer interface systems, which is a key issue faced by neuroscientists. Full article

Background: Due to the highly competitive nature of the retail sector, companies need to improve their operational efficiency. This study focuses on the application of the concepts of Lean production in the process of replenishing products on the shelves of the shops of one of the largest food retail chains in Portugal. Methods: This study aims to apply a transition from a “push” to a “pull” approach, based on real-time consumption. During a 5-day test, the impacts of implementing Lean principles and ergonomic trolleys in the process were investigated. Results: The test led to improved efficiency in product replenishment by eliminating pallets and reducing unfavourable product handling from the initial process. The implementation of a replenishment system in this manner allowed a reduced labour requirements from 28.4 to 19.2 man-hours per day and, the elimination of unnecessary handling and increase of product availability were reflected in gains on operational time and in replenishment time. Conclusions: This study provides empirical evidence for the effectiveness of Lean pull systems in retail replenishment, demonstrating a 32% reduction in labour hours. Full article

This study reports the experimental validation of several designs of photon sieves with focusing capabilities. These permeable optical elements were implemented with a spatial light modulator working in pure-amplitude mode. The focal region was scanned using a traveling stage, holding a camera. Using this experimental setup, we characterized the focal region of the photon sieves and determined some parameters of interest, such as the depth of focus and the transverse extent of the focal region. These parameters and their evolution were measured and analyzed to compare the optical performance of different designs. Moreover, the permeability of the mask was also evaluated and is included in the discussion. When the photon sieve is intended to be used as an optical element for the monitoring of running fluids, one of the designs studied, labeled the Ring-by-Ring method, behaves in a quite balanced manner and thus has become the preferred choice. Through simulations for a refractometric sensor, we obtained the Figure of Merit of the Ring-by-Ring mask, which reached a maximum value of 7860 RIU⁻¹, which is competitive with plasmonic sensing devices. Full article

In the post-pandemic era, tourism is recovering and historical and cultural scenic spots are highly favored but face serious homogenization and fierce competition. It is clear to both the industry and in academia that brand image building through social media is the key to relieving the situation; however, existing studies are mostly undertaken from the perspective of branding, often ignoring the use of brand equity theory in evaluating the brand image of such scenic spots. Based on the social media perspective, this study proposes and validates a set of brand image assessment frameworks for historical and cultural scenic spots centered on brand awareness, satisfaction, and reputation, which provides a scientific basis for scenic spot branding. The study constructs a multidimensional index system, utilizes the fuzzy optimal inferiority method and the TOPSIS hybrid evaluation model, and takes six historical and cultural scenic spots in Xi’an, China, as samples for quantitative and qualitative evaluation. By analyzing the rankings of these scenic spots, this study provides suggestions on how to publicize and shape brand images on social media platforms. These suggestions can enhance scenic spots’ competitiveness, leading to increased tourist flow, improved economic benefits, and enhanced cultural preservation efforts. This, in turn, contributes to the long-term, sustainable development of historical tourism destinations, addressing socio-economic and cultural challenges in a more targeted manner. Full article

The particle swarm optimization algorithm, known for its swift convergence and minimal parameters, is widely used to solve various complex optimization problems. Despite its merits, it encounters challenges such as susceptibility to premature convergence and reduced search capability in later stages. To improve the optimization performance of the algorithm, this study designs an advanced adaptive Group Learning Particle Swarm Optimization (GLPSO) algorithm based on the concept of symmetry. Firstly, a new coefficient is proposed to enable rapid convergence of the algorithm, allowing the population to quickly converge to the optimal position. By adopting a group learning mechanism, the optimal particles in each group are learned during particle updates; this enhances the diversity of particle learning in the algorithm, improving its global search capability. A novel adaptive perturbation rule is proposed, enabling particles to perform perturbations in a more suitable manner, thereby enhancing the population’s ability to escape local optima. GLPSO is compared with several advanced algorithms on internationally recognized high-dimensional benchmark functions and is also applied to some constrained engineering problems. The results indicate that GLPSO outperforms other improved algorithms on most test problems and provides competitive and high-quality solutions for engineering problems. Overall, GLPSO enhances its optimization capability while maintaining robustness. Full article

Background/Objectives: Master athletes in team sports represent a growing population of individuals who continue to engage in structured training and competition. Among these sports, basketball has primarily been investigated in older athletes; however, age-related effects on training load within the broader master athlete population remain largely unexplored. This study aimed to examine the age-related differences in workloads among master basketball athletes and determine whether game participation can facilitate the achievement of the recommended physical activity level. Methods: A total of 178 male athletes were divided into three age groups (35–45, 46–60, and ≥60 years) and participated in a national tournament. External load metrics, including accumulated acceleration load (AAL), mechanical load (ML), and jump load (JL), were recorded using tri-axial microsensors. Internal load (IL) was assessed via session ratings of perceived exertion (sRPE). Physical activity levels were categorized into light, moderate, and vigorous intensity using accelerometry-derived metabolic equivalents. Results: Significant age-related differences (p < 0.01) were observed in AAL, ML, and JL, with the youngest group showing the highest values. Likewise, the time spent in moderate-to-vigorous physical activity (MVPA) displayed an age-dependent manner and decreased with age. Older athletes spent more time in low-intensity activities and employed energy-conserving strategies, avoiding high-speed and high-impact actions. Despite these variations, sRPE ratings were similar among groups. Conclusions: In conclusion, age is a crucial regulator of training load and physical activity and should be considered by practitioners and coaches who design training and physical activity plans for master basketball athletes. Despite the age-related differences, participation in basketball matches provides a substantial opportunity for increasing daily MVPA. Full article

Caffeic acid phenethyl ester (CAPE) is a polyphenol produced by many plants and is found in red and green propolis. Here, we evaluated the antileishmanial activity of this natural product against Leishmania amazonensis. CAPE exhibited IC₅₀ values of 8.07 µM (95% CI, 6.79–9.62 µM) and 13.51 µM (95% CI, 10.71–17.16 µM) against L. amazonensis promastigotes and intracellular amastigotes, respectively. Additionally, CAPE inhibited L. amazonensis arginase in a non-competitive manner with a K_i value of 1.51 ± 0.04 µM. These results highlight the potential of CAPE as a promising lead compound for developing new therapies against leishmaniasis. Full article