This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids. Full article

Storing energy in hydrogen has been recognized by scientists as one of the most effective ways of storing energy for many reasons. The first of these reasons is the availability of technology for producing hydrogen from water using electrolytic methods. Another aspect is the availability of relatively cheap energy from renewable energy sources. Moreover, you can count on the availability of large amounts of this energy. The aim of this article is to support the decision-making processes related to the production of yellow hydrogen using a strategic model which exploits the metalog family of probability distributions. This model allows us to calculate, with accuracy regarding the probability distribution, the amount of energy produced by photovoltaic systems with a specific peak power. Using the model in question, it is possible to calculate the expected amount of electricity produced daily from the photovoltaic system and the corresponding amount of yellow hydrogen produced. Such a strategic model may be appropriate for renewable energy developers who build photovoltaic systems intended specifically for the production of yellow and green hydrogen. Based on our model, they can estimate the size of the photovoltaic system needed to produce the assumed hydrogen volume. The strategic model can also be adopted by producers of green and yellow hydrogen. Due to precise calculations, up to the probability distribution, the model allows us to calculate the probability of providing the required energy from a specific part of the energy mix. Full article

In recent years, an increasing number of viruses have triggered outbreaks that pose a severe threat to both human and animal life, as well as caused substantial economic losses. It is crucial to understand the genomic structure and epidemiology of these viruses to guide effective clinical prevention and treatment strategies. Nanopore sequencing, a third-generation sequencing technology, has been widely used in genomic research since 2014. This technology offers several advantages over traditional methods and next-generation sequencing (NGS), such as the ability to generate ultra-long reads, high efficiency, real-time monitoring and analysis, portability, and the ability to directly sequence RNA or DNA molecules. As a result, it exhibits excellent applicability and flexibility in virus research, including viral detection and surveillance, genome assembly, the discovery of new variants and novel viruses, and the identification of chemical modifications. In this paper, we provide a comprehensive review of the development, principles, advantages, and applications of nanopore sequencing technology in animal and human virus research, aiming to offer fresh perspectives for future studies in this field. Full article

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization. Full article

Building information modeling (BIM) and finite element method (FEM) models have a wide range of applications in underground engineering design, construction, and operation and maintenance. This study employs a BIM-FEM framework to numerically simulate the impact of excavation on existing subway stations, using the Yanjiang New City Station TOD project as a case study. This framework simplifies the smooth integration of BIM and FEM models, automating functions such as assigning material properties, conducting construction simulations, and generating high-quality meshes. Simulation results reveal significant horizontal and vertical displacements in diaphragm walls, support structures, and subway station structures, with the greatest impacts occurring closest to the excavation site. The BIM-FEM framework is validated as an effective tool for designing foundation pit support structures, enhancing numerical modeling accuracy and efficiency in underground engineering. The findings contribute to a better understanding of the dynamic interactions between excavation and underground structures, informing the development of construction strategies and protective measures to ensure structural safety. Full article

This paper presents a novel class of interior solutions for anisotropic stars under the imposition of a self-similar symmetry. This means proposing exact solutions to the Einstein field equations to describe charged matter distribution with radiation flow. The Einstein–Maxwell system by employing specific choices of mass function is formulated to describe the gravitational collapse of charged, anisotropic, spherically symmetric distributions using the Schwarzschild metric. Two ordinary differential equations governing the dynamics are derived by matching a straightforward solution of the symmetry equations to the charged exterior (Reissner–Nordström–Vaidya). Models with satisfactory physical behavior are constructed by extensively exploring self-similar solutions for a set of parameters and initial conditions. Finally, the paper presents the evolution of physical variables and the collapsing radius, demonstrating the inevitable collapse of the matter distribution. Full article

In the evolving landscape of portable electronics, there is a critical demand for components that meld stretchability with optical transparency, especially in supercapacitors. Traditional materials fall short in harmonizing conductivity, stretchability, transparency, and capacity. Although poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) stands out as an exemplary candidate, further performance enhancements are necessary to meet the demands of practical applications. This study presents an innovative and effective method for enhancing electrochemical properties by homogeneously incorporating Ru(III) into PEDOT:PSS. These Ru(III) PEDOT:PSS complexes are readily synthesized by dipping PEDOT:PSS films in RuCl₃ solution for no longer than one minute, leveraging the high specific capacitance of Ru(III) while minimizing interference with transmittance. The supercapacitor made with this Ru(III) PEDOT:PSS complex demonstrated an areal capacitance of 1.62 mF cm⁻² at a transmittance of 73.5%, which was 155% higher than that of the supercapacitor made with PEDOT:PSS under comparable transparency. Notably, the supercapacitor retained 87.8% of its initial capacitance even under 20% tensile strain across 20,000 cycles. This work presents a blueprint for developing stretchable and transparent supercapacitors, marking a significant stride toward next-generation wearable electronics. Full article

Background/Objectives: This study explores the impact of QMAC-DST, a rapid, fully automated phenotypic drug susceptibility test (pDST), on the treatment of tuberculosis (TB) patients. Methods: This pre–post comparative study, respectively, included pulmonary TB patients who began TB treatment between 1 December 2020 and 31 October 2021 (pre-period; pDST using the Löwenstein–Jensen (LJ) DST (M-kit DST)) and between 1 November 2021 and 30 September 2022 (post-period; pDST using the QMAC-DST) in five university-affiliated tertiary care hospitals in South Korea. We compared the turnaround times (TATs) of pDSTs and the time to appropriate treatment for patients whose anti-TB drugs were changed based on these tests between the groups. All patients were permitted to use molecular DSTs (mDSTs). Results: A total of 182 patients (135 in the M-kit DST group and 47 in the QMAC-DST group) were included. The median TAT was 36 days for M-kit DST (interquartile range (IQR), 30–39) and 12 days for QMAC-DST (IQR, 9–15), with the latter being significantly shorter (p < 0.001). Of the total patients, 10 (5.5%) changed their anti-TB drugs based on the mDST or pDST results after initiating TB treatment (8 in the M-kit DST group and 2 in the QMAC-DST group). In the M-kit DST group, three (37.5%) patients changed anti-TB drugs based on the pDST results. In the QMAC-DST group, all changes were due to mDST results; therefore, calculating the time to appropriate treatment for patients whose anti-TB drugs were changed based on pDST results was not feasible. In the QMAC-DST group, 46.8% of patients underwent the first-line line probe assay compared to 100.0% in the M-kit DST group (p < 0.001), indicating that rapid QMAC-DST results provide quicker assurance of the ongoing treatment by confirming susceptibility to the current anti-TB drugs. Conclusions: QMAC-DST delivers pDST results more rapidly than LJ-DST, ensuring faster confirmation for the current treatment regimen. Full article

The hepatobiliary system is vital for the biotransformation and disposition of endogenous molecules. Any impairment in the normal functioning of the hepatobiliary system leads to a spectrum of hepatobiliary diseases (HBDs), such as liver cirrhosis, fatty liver, biliary dyskinesia, gallbladder cancer, etc. Especially in pregnancy, HBD may result in increased maternal and fetal morbidity and mortality. Maternal HBD is a burden to the fetus’s growth, complicates fetal development, and risks the mother’s life. In fetal programming, the maternal mechanism is significantly disturbed by multiple factors (especially diet) that influence the development of the fetus and increase the frequency of metabolic diseases later in life. Additionally, maternal under-nutrition or over-nutrition (especially in high-fat, high-carbohydrate, or protein-rich diets) lead to dysregulation in gut hormones (CCK, GLP-1, etc.), microbiota metabolite production (SCFA, LPS, TMA, etc.), neurotransmitters (POMC, NPY, etc.), and hepatobiliary signaling (insulin resistance, TNF-a, SREBPs, etc.), which significantly impact fetal programming. Recently, biotherapeutics have provided a new horizon for treating HBD during fetal programming to save the lives of the mother and fetus. This review focuses on how maternal impaired hepatobiliary metabolic signaling leads to disease transmission to the fetus mediated through the gut–brain axis. Full article

Due to their efficient renewable energy consumption performance, AC/DC hybrid microgrids have become an important development form for future power grids. However, the fault response will be more complex due to the interconnected structure of AC/DC hybrid microgrids, which may have a serious influence on the safe operation of the system. Based on an AC/DC hybrid microgrid with an integrated bidirectional power converter, research on the interaction impact of faults was carried out with the purpose of enhancing the safe operation capability of the microgrid. The typical fault types of the DC sub-grid were selected to analyze the transient processes of fault circuits. Then, AC current expressions under the consideration of system interconnection structure were derived and, on this basis, we obtained the response results of non-fault subnets under the fault process, in order to reveal the mechanism of DC fault propagation. Subsequently, a current limitation control strategy based on virtual impedance control is proposed to address the rapid increase in the DC fault current. On the basis of constant DC voltage control in AC/DC hybrid microgrids, a virtual impedance control link was added. The proposed control strategy only needs to activate the control based on the change rate of the DC current, without additional fault detection systems. During normal operations, virtual impedance has a relatively small impact on the steady-state characteristics of the system. In the case of a fault, the virtual impedance resistance value is automatically adjusted to limit the change rate and amplitude of the fault current. Finally, a DC fault model of the AC/DC hybrid microgrid was built on the RTDS platform. The simulation and experimental results show that the control strategy proposed in this paper can reduce the instantaneous change rate of the fault state current from 19.1 kA/s to 2.73 kA/s, and the error between the calculated results of equivalent modeling and simulation results was within 5%. The obtained results verify the accuracy of the mathematical equivalent model and the effectiveness of the proposed current limitation control strategy. Full article

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage. Full article

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people. Full article

Over the last decade, researchers have developed a variety of new analytical and clinical diagnostic devices. These devices are predominantly based on microfluidic technologies, where biological samples can be processed and manipulated for the collection and detection of important biomolecules. Polydimethylsiloxane (PDMS) is the most commonly used material in the fabrication of these microfluidic devices. However, it has a hydrophobic nature (contact angle with water of 110°), leading to poor wetting behavior and issues related to the mixing of fluids, difficulties in obtaining uniform coatings, and reduced efficiency in processes such as plasma separation and molecule detection (protein adsorption). This work aimed to consider the fabrication aspects of PDMS microfluidic devices for biological applications, such as surface modification methods. Therefore, we studied and characterized two methods for obtaining hydrophilic PDMS surfaces: surface modification by bulk mixture and the surface immersion method. To modify the PDMS surface properties, three different surfactants were used in both methods (Pluronic^® F127, polyethylene glycol (PEG), and polyethylene oxide (PEO)) at different percentages. Water contact angle (WCA) measurements were performed to evaluate the surface wettability. Additionally, capillary flow studies were performed with microchannel molds, which were produced using stereolithography combined with PDMS double casting and replica molding procedures. A PDMS microfluidic device for blood plasma separation was also fabricated by soft lithography with PDMS modified by PEO surfactant at 2.5% (v/v), which proved to be the best method for making the PDMS hydrophilic, as the WCA was lower than 50° for several days without compromising the PDMS’s optical properties. Thus, this study indicates that PDMS surface modification shows great potential for enhancing blood plasma separation efficiency in microfluidic devices, as it facilitates fluid flow, reduces cell aggregations and the trapping of air bubbles, and achieves higher levels of sample purity. Full article

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope–epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope–epitope images derived from experimental structures of antibody–antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody–antigen docking poses. Full article

Dry-aged beef has been long favored by people due to its unique flavor and taste. However, the inner relationship between its overall quality formation and microbial changes during dry aging has not yet received much attention and research. To deeply reveal the forming mechanism of the unique flavor and taste of dry-aged beef, correlations between its three main quality indicators, i.e., texture, free amino acids (FAAs), volatile flavor compounds (VFCs), and microbial succession were analyzed in this study. The results showed that Staphylococcus spp. and Macrococcus spp. were key strains that influenced the total quality of dry-aged beef and strongly correlated with chewiness, hardness, and sweet FAAs (Ala), providing beef with unique palatability and taste. Additionally, among VFCs, Staphylococcus spp. and Macrococcus spp. showed a strong correlation with octanal and heptanal, and meanwhile, those highly correlated with nonanal, pentanol, and oct-1-en-3-ol were Debaryomyces spp., Psychrobacter spp., and Brochothrix spp., respectively, providing beef with a unique flavor. Staphylococcus spp. was proposed to be the dominant genus for dry-aged beef. This study provides valuable reference for the understanding of the role of microorganisms involved in dry aging. Full article

In this study, we develop a comprehensive mathematical model to analyze the dynamics of epidemic cholera, characterized by acute diarrhea due to pathogen overabundance in the human body. The model is first developed from a deterministic point of view, and then it is modified to include the randomness by stochastic differential equations. The study selected Lévy noise above other well-known types of noise, emphasizing its importance in epidemic modeling. Besides presenting a biological justification for the stochastic system, we demonstrate that the equivalent deterministic model exhibits possible equilibria. The introduction is followed by theoretical analysis of the model. Through rigorous analysis, we establish that the stochastic model ensures a unique global solution. Lyapunov function theory is applied to construct necessary conditions, which on average, guarantee the model’s stability for ${\mathbb{R}}_0^s>1$. Our findings suggest the likelihood of eradicating the disease when ${\mathbb{R}}^s$ is below one, a significant insight supported by graphical simulations of the model. Graphical illustrations were generated from simulating the model in order to increase the analytical results’ robustness. This work provides a strong theoretical framework for a thorough comprehension of a range of such diseases. This research not only provides a deeper understanding of cholera dynamics but also offers a robust theoretical framework applicable to a range of similar diseases, alongside a novel approach for constructing Lyapunov functions for nonlinear models with random disturbances. Full article

This study presents the successful synthesis of a cesium–nickel–vanadium fluoride (CsNiVF₆) pyrochlore nano-sheet catalyst via solid-phase synthesis and its electrochemical performance in green hydrogen production through urea electrolysis in alkaline media. The physicochemical characterizations revealed that the CsNiVF₆ exhibits a pyrochlore-type structure consisting of a disordered cubic corner-shared (Ni, V)F₆ octahedra structure and nano-sheet morphology with a thickness ranging from 10 to 20 nm. Using the CsNiVF₆ catalyst, the electrochemical analysis, conducted through cyclic voltammetry, demonstrates a current mass activity of ~1500 mA mg⁻¹, recorded at 1.8 V vs. RHE, along with low-resistance (3.25 ohm) charge transfer and good long-term stability for 0.33 M urea oxidation in an alkaline solution. Moreover, the volumetric hydrogen production rate at the cathode (bare nickel foam) is increased from 12.25 to 39.15 µmol/min upon the addition of 0.33 M urea to a 1.0 KOH solution and at a bias potential of 2.0 V. The addition of urea to the electrolyte solution enhances hydrogen production at the cathode, especially at lower voltages, surpassing the volumes produced in pure 1.0 M KOH solution. This utilization of a CsNiVF₆ pyrochlore nano-sheet catalyst and renewable urea as a feedstock contributes to the development of a green and sustainable hydrogen economy. Overall, this research underscores the potential use of CsNiVF₆ as a cost-effective nickel-based pyrochlore electrocatalyst for advancing renewable and sustainable urea electrolysis processes toward green hydrogen production. Full article

This paper introduces an algorithm to tackle the boundary condition (BC) problem, which has long persisted in the numerical and computational treatment of smoothed particle hydrodynamics (SPH). Central to the BC problem is a need for an effective method to reconcile a numerical representation of particles with 2D or 3D geometry. We describe and evaluate an algorithmic solution—boundary SPH (BSPH)—drawn from a novel twist on the mesh-based boundary method, allowing SPH particles to interact (directly and implicitly) with either convex or concave 3D meshes. The method draws inspiration from existing works in graphics, particularly discrete signed distance fields, to determine whether particles are intersecting or submerged with mesh triangles. We evaluate the efficacy of BSPH through application to several simulation environments of varying mesh complexity, showing practical real-time implementation in Unity3D and its high-level shader language (HLSL), which we test in the parallelization of particle operations. To examine robustness, we portray slip and no-slip conditions in simulation, and we separately evaluate convex and concave meshes. To demonstrate empirical utility, we show pressure gradients as measured in simulated still water tank implementations of hydrodynamics. Our results identify that BSPH, despite producing irregular pressure values among particles close to the boundary manifolds of the meshes, successfully prevents particles from intersecting or submerging into the boundary manifold. Average FPS calculations for each simulation scenario show that the mesh boundary method can still be used effectively with simple simulation scenarios. We additionally point the reader to future works that could investigate the effect of simulation parameters and scene complexity on simulation performance, resolve abnormal pressure values along the mesh boundary, and test the method’s robustness on a wider variety of simulation environments. Full article

Nowadays, there are many studies with a significant focus on affordable housing. The relevance of this theme, which is usually the central object of public housing policies, requires an updated review of the problems and challenges to be overcome, especially in terms of sustainability, in favor of an improvement in the quality of products delivered to beneficiaries. This research concentrates on applying the sustainability concept to affordable housing, emphasizing technical, social, and governance aspects. A novel classification framework is introduced, encompassing these aspects in the context of sustainability integrated with affordable housing. A systematic literature review is conducted and more than 100 articles are examined based on bibliometric and bibliographic analyses to highlight the main dimensions and topics involved in the housing public policy sphere. The study has been elaborated based on collecting relevant materials, building a descriptive analysis of the literature examined, highlighting the classification structure that categorizes the studies examined, and evaluating the material identified based on the classification structure. The outcomes aim to spotlight the diverse dimensions of sustainable affordable housing and associated research themes. Furthermore, the research outlines deficiencies in current approaches and outlines a future research agenda for implementing sustainability in affordable housing. It establishes a strong connection between technical, social, and governance aspects of affordable housing and sustainability, considering anticipated approaches, techniques, and challenges. Full article

The environmental crisis, growing levels of social inequalities and rising levels of noncommunicable diseases are all symptoms of economic systems that are failing to generate wellbeing. There is increasing support for the notion that addressing these crises requires shifting the focus from economic growth to a broader range of measures that reflect wellbeing, through more comprehensive, consistent and integrated policy approaches to deliver this. In 2019, the EU Finnish Council Presidency Council Conclusions called amongst other things for the development of a new long-term, post-2020 strategy to provide the framework for horizontal assessment and cross-sectoral collaboration, in particular through the European Semester process. This article contextualises this call and explores its follow-up. It draws from key policy documents to explore what Economies of Wellbeing are, why and how the concept has emerged and how they can be put in place. It then explores to what extent this concept is being applied at the EU level, by tracking changes in some of the EU’s key policies and strategies over the past 10 years and in the Semester process, as a mechanism to implement them. It concludes that while progress towards more comprehensive, consistent and integrated policy approaches has been made in the context of the Annual Sustainable Growth Strategy, underpinning the Semester processes, it is limited by the continuing emphasis on economic, over other policy, areas. It also argues that the process needs to be broadened even further, to include other dimensions of wellbeing, which intersect with the economy and impact wellbeing. To strengthen the European Semester process to achieve Economies of Wellbeing, it should be put at the service of an even more consistent and comprehensive EU Strategy that enables policy sectors to deliver wellbeing objectives in a more integrated and coordinated manner. This paper ends with recommendations for action. Full article

Organic agriculture based on the participatory guarantee system (PGS) is frequently touted as a tool for improving ecosystem sustainability and self-reliance and for alleviating the poverty of smallholder farmers in Thailand. However, specific criteria must be fulfilled for products to be certified organic. In this paper, we investigate the similarities and differences between three cases of organic agricultural production (based on the participatory guarantee system) in four provinces in northeastern Thailand: Nong Bua Lam Phu, Nakhon Phanom, Ubon Ratchathani, and Nakhon Ratchasima. A total of 135 smallholder farmers were selected to act as informants, and semi-structured interviews were held. The participatory guarantee system was utilized, considering the farmers’ diverse agricultural backgrounds and socio-economic conditions. For agriculture to be adapted with the ultimate aim of sustainability, policy support will be necessary in the form of financial measures and capacity building. Full article

The aim of the study was to characterize effects of a multi-strain synbiotic in patients with moderate to severe irritable bowel syndrome (IBS) of all stool form types. A total of 202 adult IBS patients were randomized (1:1) and after a four-week treatment-free run-in phase and were treated either with the synbiotic or a placebo for 12 weeks. The primary endpoints were the assessment of the severity of IBS symptoms (IBS-SSS) and the improvement of IBS global symptoms (IBS-GIS). Secondary endpoints comprised adequate relief (IBS-AR scale), stool form type (Bristol Stool Form Scale), bowel movements, severity of abdominal pain and bloating, stool pressure, feeling of incomplete stool evacuation, and adverse events. A total of 201 patients completed the study. Synbiotic treatment, in comparison to placebo, significantly improved IBS-SSS and IBS-GIS scores. At the end of the treatment, 70% of patients in the synbiotic group achieved adequate relief. After 12 weeks of treatment, the secondary endpoints were favorably differentiated in the synbiotic group when compared with the placebo group. Two patients in the synbiotic group reported transient adverse events (headache). The results indicate that treatment of IBS patients with the synbiotic significantly improved all major symptoms of IBS and was well-tolerated. The ClinicalTrials.gov registration was NCT05731232. Full article