Forensic entomology utilizes insect succession patterns to estimate the postmortem interval (PMI), a vital tool in criminal investigations, especially for corpses in advanced decomposition. This study explored how toxic substances—cocaine and carbamate ("chumbinho")—influence decomposition and entomofauna succession in pig carcasses (Sus scrofa domesticus) in an urban setting in São Paulo, Brazil. Three carcasses (two experimental, one control) were euthanized, intoxicated accordingly, and exposed in cages with interception traps from July to September 2016. Over this period, 15,870 adult insects (Diptera, Coleoptera, Hymenoptera, Lepidoptera) and 1,952 immature insects (Diptera, Hymenoptera) were collected and analyzed. Cocaine accelerated larval colonization within 24 hours, compared to Day 2 in the control, while carbamate delayed it until Day 15, suggesting differential toxicological impacts on insect behavior. The cocaine-exposed carcass showed the highest insect attraction, though species visitation did not differ significantly across models. Decomposition phases progressed synchronously in all carcasses, unaffected by the toxins. These substances altered PMI estimation: cocaine shortened it, while carbamate extended it, potentially skewing forensic timelines. The findings underscore the need to account for toxicological factors in PMI calculations, enhancing the reliability of entomological evidence in death investigations involving drug overdoses or poisoning. This research bridges applied biosciences and forensic science, offering practical insights for criminalistics.

In this work, the effect of the most relevant operational conditions on ferrous iron oxidation by a mixed culture of ferroxidans microorganisms was thoroughly investigated at the laboratory scale. The study focused on identifying the optimal values for key parameters such as pH and ferrous iron concentration, which are crucial for maximizing the efficiency of the biological oxidation processes taking place. Through a series of controlled experiments, it was determined that pH and ferrous iron concentration significantly affect the activity of the mixed culture of ferroxidans microorganisms, leading to more efficient iron oxidation.

To further understand and predict the behavior of the mixed culture of ferroxidans microorganisms under various operational conditions, a Monod-based mathematical model was developed. This matematical model incorporates the main biological process parameters and describes the kinetic behavior of the mixed culture of ferroxidans microorganisms in bioreactors. The predictions made by the model for the rate of ferrous iron oxidation were found to be in close agreement with the experimental data obtained in the laboratory experiments, demonstrating a very good correlation coefficient. This indicates that the model is robust and reliable for accurately predicting the performance of the mixed culture of ferroxidans microorganisms in different operational scenarios. The findings of this study provide valuable insights into optimizing bioreactor conditions for industrial applications, such as bioleaching and bioremediation, where efficient iron oxidation is essential.

Posterior fixation is usually performed to restore spinal stability and decompress the spinal canal for unstable thoracolumbar burst fractures. The purpose of this study was to compare trunk and lower extremity kinematics during gait between healthy adults and patients who underwent posterior fixation surgery after thoracolumbar fractures. Optical motion capture was used to record joint kinematics during walking. The angles and excursions of the trunk, hip, knee, and ankle joints in sagittal, frontal, and transverse planes were calculated, averaged, and compared between the patient and control groups. The patient group had significantly reduced hip extension and increased total excursion in the frontal plane, with mean differences of 7.0° in hip extension and 4.2° in total excursion. In the ankle joint, dorsiflexion was significantly reduced with increased plantarflexion, and internal rotation was also increased in the patient group. However, there were no differences in knee joint kinematics. The patient group exhibited a more upright trunk position during walking than the control group, where both peak trunk flexion and extension were significantly different, possibly indicating a stiffer trunk movement. This study provides fundamentals of the joint kinematics of the trunk and lower extremities after posterior surgical operation for thoracolumbar fracture, which may help to evaluate the surgical outcomes.

Modern agriculture's main challenge is increasing crop production while meeting the requirements of EU programs for sustainable agriculture, environmental protection, and human health. An innovative solution to this problem is the development of biopreparations based on microorganisms, which are responsible for improving the physiological processes of plants. The aim of this research was to select Trichoderma sp. fungi, which will form the basis of biofungicides and biostimulants.

The first step of the research was to isolate Trichoderma spp. from environmental samples and analyze their growth under varying environmental conditions. Next, the ability of fungi to produce metabolites and phytohormones that increase plant biological potential was determined. In addition, the antagonistic properties of Trichoderma sp. against fungal pathogens, which significantly reduce crop yields, were analyzed using a breeding method.

Based on the results, the 40 isolates had a high tolerance level to different temperatures, pH, and salinity of the medium. The results of spectrophotometric analyses showed that fungi produce dehydrogenases, cellulases, proteases, ureases, phosphatases, organic acids, and phytohormones, which are responsible for the transformation of carbon, nitrogen, and phosphorus compounds in the soil, as well as the improvement of physiological processes in crop plants. In addition, Trichoderma sp. showed a high degree of antagonism towards fungal pathogens.

Analyses of the properties of Trichoderma sp. allowed the selection of four isolates that will be tested in field studies for their effects on plant growth, which will allow the construction of biopreparations to assist in the protection and development of crop plants.

Human rhinoviruses (HRVs) are the primary cause of the common cold, a highly contagious upper respiratory tract infection characterized by nasal congestion, sneezing, and sore throat. HRV replication depends on its 3C protease (HRV-3Cpro), a key enzyme that cleaves the viral polyprotein into functional proteins essential for viral maturation. Currently, no FDA-approved inhibitors specifically target HRV-3Cpro. While rupintrivir, a synthetic inhibitor, advanced to clinical trials, it ultimately failed due to limited efficacy. This study investigated the potential of Vitex negundo (or lagundi)—a medicinal plant traditionally used in the Philippines for treating colds and respiratory ailments—as a source of natural HRV-3Cpro inhibitors through in silico molecular docking and pharmacokinetic (ADMET) evaluation. Fifteen phytochemicals reported from Philippine V. negundo were screened, with four compounds—agnuside (-6.9 kcal/mol), luteolin 7-O-glucoside (-6.7 kcal/mol), 2′-p-hydroxybenzoyl mussaenosidic acid (-6.6 kcal/mol), and 6′-(p-hydroxybenzoyl) mussaenosidic acid (-6.5 kcal/mol)—exhibiting stronger binding affinities than the reference inhibitor rupintrivir (-6.2 kcal/mol), suggesting potential for more effective enzyme inhibition. While agnuside had the strongest binding affinity, 6′-(p-hydroxybenzoyl) mussaenosidic acid exhibited the most favorable drug-likeness and ADMET profile, being predicted as non-mutagenic, non-hepatotoxic, and non-inhibitory to major drug-metabolizing enzymes. Nevertheless, their low oral bioavailability and gastrointestinal absorption suggest the need for alternative delivery methods to enhance therapeutic efficacy. Therefore, these findings highlight the antiviral potential of V. negundo phytochemicals as plant-based HRV-3Cpro inhibitors. Further in vitro and in vivo studies are recommended to validate their bioactivity.

Introduction: Soft knee braces can be used to improve stability and lower limb loads in various activities such as jogging. Support stays are installed in soft knee braces to maintain knee posture. However, support stays often make the wearing of soft knee braces uncomfortable. To wear a soft knee brace continuously, it is necessary to minimize the frequency of use of the support stays to maintain comfortability for wearing. Thus, activities that require support stays should be investigated and selected. This study aimed to investigate the effects of support stays on jogging with a soft knee brace. Methods: In the experiment, 10 young males as participants were asked to perform 10 s of jogging on a treadmill (5.7 km/h) with different brace conditions (with and without support stays). Muscle activities of quadriceps femoris and hamstrings during jogging were measured using an electromyograph. Results: The results showed that there were no significant differences in muscle activities between patients with and without support stays. Conclusions: These results indicate possibility that support stays are not necessary in short-term jogging to maintain the comfort of wearing. In the future, the effect of the support stays of soft knee braces should be investigated for more difficult or longer activities. Acknowledgements: The authors received only soft knee braces for the experiments from Nippon Sigmax Co. Ltd.

Molecularly imprinted polymers (MIPs) have been combined with plasmonic probes to realize optical chemical sensors. In particular, plasmonic optical fiber probes are used as transducers to monitor several kinds of MIPs, such as nanoMIPs, MIP layers, and microbeads of MIPs. Plastic Optical Fibers (POFs) can be used to develop several bio/chemical sensor configurations, exploiting their excellent flexibility, easy manipulation, great numerical aperture, large diameter, and large number of modes. Therefore, extrinsic and intrinsic optical fiber sensing schemes can be achieved using POFs' characteristics combined with cheap equipment, such as white light sources and spectrometers. In this work, we propose a low-cost sensing strategy to monitor MIPs without relying on plasmonic phenomena. Intensity-based sensor configurations can be implemented by exploiting MIPs as the core of sensitive optical waveguides. In this case, when the binding between the substance of interest and specific sites of MIPs occurs, the refractive index of the core in the sensitive waveguide changes, and the intensity of the transmitted light also changes. This sensing strategy can be implemented using an LED as the source, photodetectors as the receiver, and an Arduino system to record and process experimental data. Several configurations will be presented in this work to demonstrate the capability of this sensing approach. In particular, as a proof-of-concept, furfural (2-FAL) detection in water solutions in food applications is presented, demonstrating high performance by achieving an ultra-low detection limit at the pico- to nanomolar level and a wide detection range spanning approximately four orders of magnitude.

Three-dimensional-visualization technologies such as 360-degree virtual tours and 3D spatial modeling have significantly increased in the domains of architecture, engineering, construction (AEC), and real estate. These 3D models can be used for creating Building Information Models (BIMs), digital twins, or educational environments, among other things. As the demand for this data is increasing, plenty of reality-capture computer software and mobile applications have been developed. Most of the previous research focused on experiential outcomes rather than technical benchmarks. This study presents a comparative study of reality-capture applications and their capability for generating 3D virtual models. Several applications are being evaluated using a 360 camera and a phone camera as the main collection devices. This study primarily evaluates the technical features, usability, cost factors, and limitations. Some of the technical features that are examined include panoramic capture, 3D walkthrough generation, 2D floor plan extraction, LiDAR integration, BIM compatibility, and virtual staging. The usability of the applications focuses on the applications’ user interface, ease of use, and intuitiveness. The results show varying capabilities of the compared applications. In addition, various suggestions are provided to fill the gap. The next step of this study includes a comparative analysis of the applications’ accuracy and the precision of the generated models.

Discrete event simulation in construction research has been used extensively to evaluate the performance of construction operations. Obtaining an optimal configuration of the decision variables using simulation alone requires the evaluation of all possible combinations of the values of these variables, which is not feasible for problems with a large search space. To this end, simulation is often coupled with an optimization algorithm in order to optimize the construction operations. However, the major drawback of the current stochastic simulation optimization methods is that they require a long computational time and may present inferior solutions in the final Pareto front. This research presents a DES optimization framework that can be used by decision-makers to enhance and improve the current practice of decision-making in construction projects. The aim of the framework is to select a set of near-optimal resource combinations that minimize the total project duration and total project cost. The objective of this research is to investigate the benefits of incorporating variance reduction techniques in discrete event simulation optimization within the context of simulation optimization of construction operations. Three variance reduction techniques are studied using a case study, namely, common random numbers, antithetic variates, and a joint application of the previous two techniques. The incorporation resulted in an average time saving of 81.81% while improving the quality of Pareto solutions by 2.4% and reducing the presence of inferior solutions by 63.15%.

The "Industry 4.0", a technology revolution emphasized automation, connectivity, and data-driven decision-making. As the world transitions to Industry 5.0, the focus shifts to more human-centred, robust, sustainable, and intelligent industrial systems. Here, collaborative robots (cobots) emerge as key enablers, work in shared space, enhancing human capability without compromising safety and flexibility.

Computer vision plays a key role in enabling such integration by offering perception intelligence to cobots for tasks such as object detection, gesture identification, defect detection, and adaptive navigation. These capabilities are powered by artificial intelligence strategies: classical approaches—including feature extraction, template matching, and traditional machine learning continue to offer robust solutions for structured tasks, while new methodologies – deep learning, reinforcement learning, and transformer-based architectures facilitate adaptability in unstructured and dynamic industrial environments.

Software platforms are also critical for implementation and deployment. MATLAB remains an excellent choice for quick prototyping and algorithm validation, whereas Python-based frameworks (e.g. TensorFlow, PyTorch, OpenCV) provide scalability, open source flexibility and integration with edge and cloud platforms. Their comparison is critical to grasp the performance, accessibility and deployment readiness trade-offs.

Applications of AI-driven, vision-enabled cobots are assembly, quality inspection, adaptive manufacturing and safe human-robot collaboration. This paper surveys conventional and emerging computer vision approaches, identifies the gaps and presents the future research directions – edge AI deployment, multimodal sensor fusion and explainable vision systems – toward reliable and efficient adoption in Industry 5.0.