Are common stocks issued by timberland companies a good investment? Portfolios of large US timberland corporations are compared to simultaneous investments in a diversified US common stock index. Over a 20-year sample period it turns out that the US timberland corporations, on average, perform about as well as the highly diversified US stock market index. It is surprising that the timberland companies do not outperform the stock market indexes because, in order to encourage tree planting, the US Congress has almost completely exempted timberland companies from paying federal income taxes. Furthermore, it is scientifically impossible to assess the value of the large amounts of photosynthesis that the timberland companies produce. As a result of these two ambiguities, it is difficult to state decisively that the timberland companies are better investments than a diversified portfolio of common stocks. However, valuing timberland companies is more practical than endeavoring to value the trees directly. Full article

Vanadium redox flow batteries are promising energy storage devices and are already ahead of lead–acid batteries in terms of installed capacity in energy systems due to their long service life and possibility of recycling. One of the crucial tasks today is the development of models for assessing battery performance and its residual resource based on the battery’s present state. A promising method for estimating battery capacity is based on analyzing present voltage and current values under various load conditions. This paper analyzes the discharge characteristics of a 10 kW all-vanadium redox flow battery at fixed load powers from 6 to 12 kW. A linear dependence of operating voltage and initial discharge voltage on load power is established. It is also determined that the slope of the discharge curve linear section does not increase linearly in absolute value, and the Box–Lucas model can be used to describe it. Models for predicting current VRFB capacity based on different curve fitting functions are proposed. These models can be used to roughly estimate battery capacity at different load powers. Full article

Unmanned aerial vehicles (UAVs) with folding wings can serve in multiple mission profiles, usually accompanied by sudden changes in flight speed. These bring great challenges to the aeroelastic design of UAVs, especially in the calculation of flutter characteristics. This paper developed a new non-intrusive aeroelastic model to quickly calculate the flutter characteristics of Z-shaped folding wings at different folding angles. First, the original Z-shaped folding wing was designed to be enhanced. Beams and ribs were arranged inside each wing segment to enhance the structural strength performance. Control surfaces were arranged in the middle-wing and outer-wing to enhance the aerodynamic control performance. Second, a parametric aeroelastic model at any folding angle was reconstructed based on the input file of Nastran software for the flutter calculation of the folding wing in the unfolded state. Finally, the effects of parameters such as folding angle, hinge stiffness between different wing segments, and hinge stiffness of the control surfaces on the flutter characteristics of the folding wing were investigated. The results show that the enhancement scheme could significantly increase the flutter speed and flutter frequency of the folding wing. The hinge stiffness between each wing segment had a significant impact on the flutter characteristics of the folding wing, but flutter at the control surface basically did not occur. Full article

Genipin polymers are self-forming tensile-load-carrying oligomers, derived from the gardenia fruit, that covalently bond to amines on collagen. The potential therapeutic mechanical benefits of a non-discrete in situ forming mesh of genipin oligomers for degraded spinal discs were first conceived in 1998. Over more than two decades, numerous studies have demonstrated the immediate mechanical effects of this injectable, intra-annular polymeric mesh including an early demonstration of an effect on clinical outcomes for chronic or recurrent discogenic low back pain. This literature review focused on articles investigating mechanical effects in cadaveric animal and human spinal discs, biochemical mechanism of action studies, articles describing the role of mechanical degradation in the pathogenesis of degenerative disc disease, initial clinical outcomes and articles describing current discogenic low back pain treatment algorithms. On the basis of these results, clinical indications that align with the capabilities of this novel injectable polymer-based treatment strategy are discussed. It is intended that this review of a novel nano-scale material-based solution for mechanical deficiencies in biologically limited tissues may provide a helpful example for other innovations in spinal diseases and similarly challenging musculoskeletal disorders. Full article

This study aimed to isolate and purify resveratrol and oxyresveratrol from the heartwoods of Maclura cochinchinensis, and to evaluate their inhibitory effects on melanogenesis in B16F10 murine melanoma cells. A methanol maceration process yielded a crude extract comprising 24.86% of the initial mass, which was subsequently analyzed through HPTLC, HPLC, and LC-MS/MS. These analyses revealed the presence of resveratrol and oxyresveratrol at concentrations of 4.32 mg/g and 33.6 mg/g in the extract, respectively. Initial purification employing food-grade silica gel column chromatography separated the extract into two fractions: FA, exhibiting potent inhibition of both tyrosinase activity and melanogenesis, and FM, showing no such inhibitory activity. Further purification processes led to the isolation of fractions Y11 and Gn12 with enhanced concentrations of resveratrol (94.9 and 110.21 mg/g, respectively) and fractions Gn15 and Gn16 with elevated levels of oxyresveratrol (321.93 and 274.59 mg/g, respectively), all of which significantly reduced melanin synthesis. These outcomes affirm the substantial presence of resveratrol and oxyresveratrol in the heartwood of M. cochinchinensis, indicating their promising role as natural agents for skin lightening. Full article

Hyaluronic acid (HA) hydrogels are commonly used for facial dermal filling and for alternative medical aesthetic purposes. High diversity exists in commercial formulations, notably for the optimization of finished product stability, functionality, and performance. Polyvalent ingredients such as calcium hydroxylapatite (CaHA) or vitamin B3 (niacinamide) are notably used as bio-stimulants to improve skin quality attributes at the administration site. The aim of the present study was to perform multi-parametric characterization of two novel cross-linked dermal filler formulas (HAR-1 “Instant Refine” and HAR-3 “Maxi Lift”) for elucidation of the various functional impacts of vitamin B3 incorporation. Therefore, the HAR products were firstly comparatively characterized in terms of in vitro rheology, cohesivity, injectability, and resistance to chemical or enzymatic degradation (exposition to H₂O₂, AAPH, hyaluronidases, or xanthine oxidase). Then, the HAR products were assessed for cytocompatibility and in vitro bio-stimulation attributes in a primary dermal fibroblast model. The results showed enhanced resilience of the cohesive HAR hydrogels as compared to JUVÉDERM^® VOLBELLA^® and VOLUMA^® reference products in a controlled degradation assay panel. Furthermore, significant induction of total collagen synthesis in primary dermal fibroblast cultures was recorded for HAR-1 and HAR-3, denoting intrinsic bio-stimulatory effects comparable or superior to those of the Radiesse^® and Sculptra^™ reference products. Original results of high translational relevance were generated herein using robust and orthogonal experimental methodologies (hydrogel degradation, functional benchmarking) and study designs. Overall, the reported results confirmed the dual functionalization role of vitamin B3 in cross-linked HA dermal fillers, with a significant enhancement of hydrogel system stability attributes and the deployment of potent bio-stimulatory capacities. Full article

The global issue of substance abuse demands ongoing initiatives aligned with the United Nations Sustainable Development Goals. With drug use remaining prevalent worldwide, interventions are critical to addressing the associated health challenges and societal implications. Exercise and physical activities have emerged as integral components of substance use disorder (SUD) treatment, offering promising avenues for prevention, intervention, and recovery. Recent research underscores the efficacy of exercise in reducing substance cravings, promoting abstinence, and improving overall well-being. However, integrating exercise into SUD recovery programs presents challenges such as dropout rates and cultural considerations. This paper synthesizes existing literature on exercise integration into SUD recovery, highlighting strategies for enhancing treatment outcomes and addressing barriers to exercise adherence. Drawing on cognitive–behavioral therapy, experiential learning, motivational interviewing, and goal-setting techniques, the holistic approach outlined in this paper aims to empower individuals both mentally and physically, fostering resilience and supporting long-term recovery. In conclusion, new initiatives need to be taken by advocating for inclusive policies, promoting community engagement, and fostering collaborations across sectors. By doing so, stakeholders can optimize the effectiveness of exercise programs and contribute to sustainable rehabilitation efforts for individuals with SUD. Full article

This study presents a sustainable digital transformation framework to integrate sustainable practices into digital transformation initiatives within Small and Medium Enterprises (SMEs). The methodology includes a literature review, a framework creation, and a case study with passive participation. The framework was structured to help industries implement responsible digitalization initiatives in five key stages: setting objectives, fostering stakeholder-focused engagement, defining sustainable objectives and dimensions, creating a sustainable model, and executing the project. Validating the proposal in the context of an SME allowed us to discern the tangible benefits of sustainability practices for the organization, further reinforcing the relevance and applicability of the framework. In conclusion, this research offers valuable information for SMEs considering starting a sustainable digitalization process. Full article

A relevant challenge for the treatment of patients with neoplasia is the development of resistance to chemo-, immune-, and radiotherapies. Although the causes of therapy resistance are poorly understood, evidence suggests it relies on compensatory mechanisms that cells develop to replace specific intracellular signaling that should be inactive after pharmacological inhibition. One such mechanism involves integrins, membrane receptors that connect cells to the extracellular matrix and have a crucial role in cell migration. The blockage of one specific type of integrin is frequently compensated by the overexpression of another integrin dimer, generally supporting cell adhesion and migration. In particular, integrin αvβ3 is a key receptor involved in tumor resistance to treatments with tyrosine kinase inhibitors, immune checkpoint inhibitors, and radiotherapy; however, the specific inhibition of the αvβ3 integrin is not enough to avoid tumor relapse. Here, we review the role of integrin αvβ3 in tumor resistance to therapy and the mechanisms that have been proposed thus far. Despite our focus on the αvβ3 integrin, it is important to note that other integrins have also been implicated in drug resistance and that the collaborative action between these receptors should not be neglected. Full article

This paper introduces an improved methodology designed to address a practical deficit of existing methodologies by incorporating circuit-level analysis in the assessment of building microgrid reliability. The scientific problem at hand involves devising a systematic approach that integrates circuit modeling, Probability Density Function (PDF) selection, formulation of reliability functions, and Fault Tree Analysis (FTA) tailored specifically for the distinctive features of building microgrids. This method entails analyzing inter-component relationships to gain comprehensive insights into system behavior. By harnessing the circuit models and theoretical framework proposed herein, precise estimations of microgrid failure rates can be attained. To complement this approach, we propose a thorough investigation utilizing reliability curves and importance measures, providing valuable insights into individual device failure probabilities over time. Such time-based analysis plays a crucial role in proactively identifying potential failures and facilitating efficient maintenance planning for microgrid devices. We demonstrate the application of this methodology to the University of Antioquia (UdeA) Microgrid, a low-voltage system comprising critical components such as solar panels, microinverters, inverters/chargers, batteries, and charge controllers. Full article

This work proposes a novel stochastic model describing the propagation dynamics of the hepatitis B virus. The model takes into account numerous disease characteristics, and environmental disturbances were collected using Lévy jumps and the conventional Brownian motions. Initially, the deterministic model is developed, and the asymptotic behavior of the model’s solution near the equilibria is examined. The deterministic model is transformed into a stochastic model while retaining the Lévy jumps and conventional Brownian motions. Under specific assumptions, the stochastic system is shown to have a unique solution. The study further investigates the conditions that ensure the extinction and persistence of the infection. The numerical solutions to both stochastic and deterministic systems were obtained using the well-known Milstein and RK4 techniques, and the analytical findings are theoretically confirmed. The simulation suggests that the noise intensities have a direct relationship with the amplitudes of the stochastic curves around the equilibria of the deterministic system. Smaller values of the intensities imply negligible fluctuations of trajectories around the equilibria and, hence, better describe the extinction and persistence of the infection. It has also been found that both Brownian motions and the Lévy jump had a significant influence on the oscillations of these curves. A discussion of the findings of the study reveals other important aspects as well as some future research guidelines. In short, this study proposes a novel stochastic model to describe the propagation dynamics of the hepatitis B virus. Full article

The adsorption characteristics of β-glucooligosaccharides on activated carbon and the purification were systematically investigated. The maximum adsorption capacity of activated carbon reached 0.419 g/g in the optimal conditions. The adsorption behavior was described to be monolayer, spontaneous, and exothermic based on several models’ fitting results. Five fractions with different degrees of polymerization (DPs) and structures of β-glucooligosaccharides were obtained by gradient ethanol elution. 10E mainly contained disaccharides with dp2a (G1→6G) and dp2b (G1→3G). 20E possessed trisaccharides with dp3a (G1→6G1→3G) and dp3b (G1→3G1→3G). 30E mainly consisted of dp3a and dp4a (G1→3G1→3(G1→6)G), dp4b (G1→6G1→3G1→3G), and dp4c (G1→3G1→3G1→3G). In addition to tetrasaccharides, 40E and 50E also contained pentasaccharides and hexasaccharides with β-(1→3)-linked or β-(1→6)-linked glucose residues. All fractions could inhibit the accumulation of intracellular reactive oxygen species (ROS) in H₂O₂-induced Caco-2 cells, and they could improve oxidative stress damage by increasing the activity of superoxide dismutase (SOD) and reduced glutathione (GSH), which were related to their DPs and structures. 50E with high DPs showed better anti-oxidative stress activity. Full article

Thanks to the publication of ChatGPT, Artificial Intelligence is now basically accessible and usable to all internet users. The technology behind it can be used in many chatbots, whereby the chatbots should be trained for the respective area of application. Depending on the application, the chatbot should react differently and thus, for example, also take on and embody personality traits to be able to help and answer people better and more personally. This raises the question of whether ChatGPT-4 is able to embody personality traits. Our study investigated whether ChatGPT-4’s personality can be analyzed using personality tests for humans. To test possible approaches to measuring the personality traits of ChatGPT-4, experiments were conducted with two of the most well-known personality tests: the Big Five and Myers–Briggs. The experiments also examine whether and how personality can be changed by user input and what influence this has on the results of the personality tests. Full article

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD. Full article

This study analyzes the eating behavior and factors associated with the presence of disordered eating attitudes in patients undergoing bariatric surgery. It is a cross-sectional, descriptive, and analytical study conducted at a hospital in the Amazon region of Brazil. The Disordered Eating Attitude Scale reduced version (DEAS-s) was used to assess the risk of eating disorders and the Three-Factor Eating Questionnaire (TFEQ-R21) was used to characterize eating behavior. A total of 205 patients participated, with a mean age of 37.5 ± 8.6 years. The majority of participants were female (93.7%; p < 0.001), and the mean BMI was 45.3 ± 6.7 kg/m². It was found that cognitive restraint had the highest mean (52.6 ± 19.9; p < 0.001). As for the DEAS-s, the question with the highest mean response was “spending one or more days without eating or consuming only liquids to lose weight” (2.80 ± 1.99). Female participants had a higher score for emotional eating (p = 0.016). Disordered eating attitudes showed a correlation with emotional eating and uncontrolled eating. These results suggest that candidates for bariatric surgery may have susceptibility to eating disorders. The importance of a multidisciplinary team conducting monitoring during the preoperative period is highlighted. Full article

Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to devise suitable applications. This article aims to propose for the first time an approach using quantum logic in image processing for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way, we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification, we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables us to initiate the principal component analysis. Through experiments, we explore the possibility of distinguishing between different geometrical objects and discuss the results in terms of quantum logical interpretation. Full article

Traditional branding approaches take too long to react to today’s dynamic environment. Agile branding offers a promising approach and might be the contemporary answer to the demands of our digitalized world. While scientists state that an agile approach to branding is essential nowadays, there is a lack of knowledge on how agile branding can be conceptualized and applied in practice. This article aims to provide a better understanding of agile branding by identifying its dimensions and antecedents. This study pursued a multi-method approach: a systematic literature review was conducted, followed by qualitative semi-structured interviews with eleven experts and a group discussion with five participants. Applying the dynamic capabilities perspective, the authors identify five dimensions, including 20 antecedents of agile branding: (a) leadership style and culture, (b) employee skills and competencies, (c) technology and methods, (d) stakeholder and feedback integration, and (e) ambidexterity. The critical challenge of agile branding is to balance brand continuity and adaptation. The study is the first to conceptualize agile branding, offering scientists and practitioners values and principles on how brands can be managed and further evolved in a constantly changing world to stay relevant in the market and ahead of the competition. Full article

People with generalized anxiety disorder tend to have sleep problems, and studies have found correlations between metacognition, rumination, and sleep, but it is unclear how metacognition and rumination work in people with a tendency towards generalized anxiety disorder. The goal of this paper is to investigate the correlation between metacognition, rumination, and sleep in university students with a tendency towards generalized anxiety disorder, and the mediating role of rumination in the effect of metacognition on sleep. The Generalized Anxiety Disorder Scale (GAD-7), the Meta-Cognition Questionnaire (MCQ-30), the Ruminative Responses Scale (RRS), and the Insomnia Severity Index (ISI) were used to investigate and psychometrically measure 566 university students in Anyang Normal College. The results of correlation analysis showed significant positive correlations between metacognition and sleep, ruminative thinking and sleep, and metacognition and rumination in university students with a tendency towards generalized anxiety disorder. Mediation analysis showed that rumination partially mediated the effect of metacognition on sleep, with the mediating effect accounting for 51.1% of the total effect. There is a strong correlation between metacognition, rumination, and sleep in university students with a tendency towards generalized anxiety disorder, and both metacognition and rumination can predict sleep, while metacognition can affect sleep through the mediating effect of rumination. Full article

During the mid-2000s to the early 2010s, the domestic ethanol industry witnessed substantial growth, with ethanol coproducts emerging as vital elements for plant profitability and livestock feeding. Initially serving as supplementary revenue streams, coproducts from ethanol production have evolved into diverse value-added offerings, bolstering revenue streams, and sustaining profit margins. This study reviews existing economic research on ethanol coproducts, detailing methodologies, product focus, and research locations. Initially gathering 972 articles from 9 databases, 110 articles were synthesized. We find that most studies primarily examined the growth and future of the ethanol industry with a limited focus on specific coproducts. Feed-use distillers’ grains, especially dried distillers’ grains, were the most widely published while newer coproducts like pelletized, de-oiled, and high-protein distillers’ grains were relatively understudied. Non-feed-use products were notably overlooked, highlighting the need for exploration beyond conventional applications. The evolving market landscape for ethanol co-products has surpassed published academic understanding of the economic tradeoffs necessitating further research into product dynamics, pricing, marketing, market structures, and regulatory frameworks. This highlights and underscores the importance of investigating value-added grains across diverse commodities and geographic contexts to inform strategic decision-making and policy formulation. Full article

The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection, and histogram comparison to separate vapor from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called U-net to extract meaningful features from video frames. Both approaches enabled the automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially CNN demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1% and classify the above flow regimes with an overall classification accuracy of 91%. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10⁻⁶. Full article

(1) Background: With the aging population, effective interventions are needed to enhance the health of older adults. This study investigated the combined effects of yoga and the Mediterranean diet on various health outcomes in community-dwelling older adults; (2) Methods: The study employed a randomized controlled trial design with a total of 116 older adults randomized to an experimental group (n = 57) that underwent a combined yoga and Mediterranean diet program and a control group (n = 59) that did not receive any intervention. Nutritional status was assessed using the Mini Nutritional Assessment, flexibility with the Back Scratch Test and the Chair Sit-and-Reach Test, balance, gait, and fall risk with the Tinetti Scale, and muscle strength with a dynamometer and the 30 s Chair Stand Test; (3) Results: Regarding nutritional status, there were significant differences between the experimental group and the control group (Cohen’s d = 0.02). The participants in the experimental group showed greater balance (11.12 ± 3.01 vs. 10.03 ± 2.35, Cohen’s d = 0.41 and gait (7.63 ± 1.96 vs. 6.69 ± 2.50, Cohen’s d = 0.44) with respect to the control group. In terms of flexibility, the experimental group showed statistically significant improvements in the right arm (Cohen’s d = 0.43), left arm (Cohen’s d = 0.64), right perineum (Cohen’s d = 0.42), and left leg (Cohen’s d = 0.37) Finally, in terms of strength, participants in the experimental group experienced statistically significant improvements in grip strength and lower body strength (Cohen’s d = 0.39 and 0.81, respectively); (4) Conclusions: The study highlights the potential benefits of a 12-week intervention combining yoga with a Mediterranean diet to improve the health and functional capacities of community-dwelling older adults. Full article

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R² of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands. Full article