Patients with Multiple Myeloma have an increased susceptibility to severe infections due to several various factors, especially the high tumor burden. The use of the anti-CD38 monoclonal antibodies-based therapies has changed the frequency and epidemiology of infections. We report herein the safety profile of the pivotal clinical trials conducted with anti-CD38 mAbs (daratumumab and isatuximab) and anti-SLAMF7 mAbs (elotuzumab) that led to the approval of regimens employed today in NDMM and RRMM patients.

In this study we evaluate performance of two American surveillance systems for monitoring the spread of antimicrobial-resistant (AMR) gonorrhea: the Gonococcal Isolate Surveillance Project (GISP) and the enhanced Gonococcal Isolate Surveillance Project (eGISP) which includes the non-urethral isolates in addition to the urethral ones utilized in the original surveillance system. A continuous-time agent-based model of gonorrhea transmission among the US men who have sex with men (MSM) population was developed and used for this purpose. Our results show that the accuracy of eGISP was nearly 10% higher than the accuracy of GISP (97.8% (95% uncertainty interval: 83.5%, 100%) vs. 87.9% (55%, 100%)). Most of time, GISP detects the moment when the resistance to the first-line antibiotic reaches the switch threshold recommended by the WHO later than when it was actually reached. This leads to the additional spread of AMR gonorrhea. We also found that including the extragenital isolates in the surveillance is more important for the accurate monitoring than increasing the number of isolates. Finally, our results demonstrate that rectum and pharynx can serve as a niche for fostering antimicrobial resistance. These findings provide a scientific basis which can be used to inform the decision made by the policymakers on how many GISP and eGISP sites to establish each year.

This research paper presents our investigation of the evolving trends in the field of Learning Sciences (LS) from 2005 to 2021. LS is a multidisciplinary field that integrates various scientific disciplines to study and support learning in real-world contexts (Hoadley, 2018). Our paper aims to understand how LS practices, methods, and disciplines of interest have changed over time, considering factors like the COVID-19 pandemic and the call for more equity-based research. We collected data from the International Society of Learning Sciences (ISLS) repository, which serves as one major platform that showcases LS research. We analysed the proceedings of the ISLS annual meetings and conferences, focusing on variables such as title, abstract, issue date, publisher, and collection using web scraping tools and the RStudio software for data collection and analysis. Our findings indicate several trends in LS research. In terms of methods and practices, design-based research (DBR) and participatory methods were commonly used in LS studies. However, there was a decline in the adoption of DBR in 2021, possibly due to the limitations imposed by the COVID-19 pandemic. Project-based learning, embodied interaction, and game-based learning were the most frequently used techniques among LS researchers. When examining the population of interest and domain focus in LS research. Learners were the primary focus, with a significant increase in the number of studies focusing on students over the years. However, there was also a noticeable increase in studies focusing on teachers in more recent years. In terms of subject disciplines, science and mathematics were the primary areas of focus, but there has been increasing attention to the arts and technology in recent years. Concerning equity-based research in LS, the finding revealed a significant increase in this area from 2005 to 2021, with ICLS studies tending to focus more on equity compared to CSCL studies. These findings contribute to understanding the current status and future development of the learning sciences.

This article evaluates the reliability and consistency of nutrition- and supplementation-related advice for pregnant women provided by ten selected health-related Internet portals. The portals were chosen based on their perceived reliability and prominence in Google searches, with representation from both English and Polish language sources. The evaluation criteria included the adherence of the presented information to official recommendations and its evidence-based character based on specific items representing dietary aspects important in pregnancy. While the overall reliability was deemed acceptable, significant variations existed both among the portals and specific evaluated items. Notably, HealthLine, Medline Plus, and NCEZ emerged as the most evidence-based, while WebMD and Medycyna Praktyczna were identified as less reliable. Despite a number of issues, the analysed portals remain valuable sources of nutritional information for pregnant women, offering user-friendly accessibility superior to alternatives such as social media, on the one hand, and scientific articles, on the other. Improved consistency and attention to detail, especially in relation to vitamin intake and supplementation, would improve the overall quality of health portals.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

Approximately 20% of the world's population resides in coastal areas at risk of rising sea levels due to climate change. This could lead to severe weather events and ecological impact on critical infrastructure. To combat the effects of water-based environmental forces on the land, hard engineering methods such as seawalls have been utilized. However, a more sustainable solution is emerging in nature-based engineering, which involves growing vegetation. This approach can help extend the lifespan of existing technology and provide climate change adaptation and resilience for coastal and riverine communities. The following paper will review seaweed farms as an advanced nature-based mitigation approach. The paper also outlines the findings of experiments at RWTH Aachen University's hydraulic laboratory. These tests involved using seaweed as a natural solution to dampen waves in a model system to test a hypothesis. One result involving a system with two lines of seaweed revealed 15 percent wave damping. Using seaweed as a nature-based solution, a gentle engineering approach to designing future vegetated protection systems can extend the design life of existing coastal infrastructure. This can also provide protection against climate-induced sea level rise, promote coastal risk mitigation, restore ecosystems, and foster blue bio-economic development.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

The interest in virtual fencing, as opposed to traditional physical barriers, has increased since it makes it easier to move the boundaries of animal enclosures for agricultural and rewilding purposes. Despite this, the implementation of virtual fence systems, such as the Nofence© system, requires large financial investments. The necessity of individual collars also makes it more difficult to implement this system on a larger scale. If a herd of cattle tend to maintain cohesive social groups around dominant individuals, it might be possible to use fewer collars thereby making the system more cost efficient. This study investigates the pattern in spatial distribution in a herd of 17 Angus cows on Fanø in Denmark with GPS locations, using a Nofence© system. The aim of this paper is to determine how individuals position themselves in a herd, spatially, and identify a pattern in ranks. The method used in this study examines the distances between an individual to the rest of the herd mates using nearest neighbour and distribution plots. Contrary to the expectations, this study did not show a distinct pattern in herd distribution. While some tendencies in spatial distribution patterns were observed, only a low concordance could be found (W=0.15,p=3.02e−16). Therefore, interactions among individuals should be considered in future studies, rather than only investigating the herd as one unit. A cumulative curves of the ranks estimated over the entire periods allowed however a rough estimation of the hierarchy and allowed the identification of the highest ranked cows. Therefore it might be possible to use fewer collars if the high ranked cows are discovered, but the estimation of the hierarchy will require a period of observation overall several days as the hierarchical positions are fluctuating too much from days to days. Therefore the cumulative rank curves seems to be a valid solution.

Hybrid materials are a recent addition in the field of Restorative Dentistry for computer-assisted design/ computer-assisted manufacturing (CAD/CAM) indirect restorations. The long–term clinical success of modern dental restorative materials follows a multifactorial pattern. Among the characteristics, affecting the longevity of a restoration, mechanical properties and physicο – chemical interactions are of utmost importance. While numerous researchers constantly evaluate the mechanical properties, the biological background of resin–based CAD/CAM biomaterials is scarcely investigated and, therefore, less described in the literature. This review aims to analyze the biofilm formation on the surfaces of novel hybrid, resin–based CAD/CAM materials and evaluate the methodological protocols followed to assess microbial growth. It is demonstrated that the surface structure, the composition and the finishing and polishing procedures on the surface of a dental restorative material influence the initial bacterial adhesion; however most studies focus on in vitro protocols, whereas in vivo and/or in situ research of microbiomics in CAD/CAM restorative materials is lacking, obstructing in that manner the accurate understanding of the bioadhesion phenomenon in the oral cavity.

Apoptotic cells possess immunomodulatory effects that can be utilized to treat imbalanced immune conditions. Information on the preclinical safety of such treatment is sparse. In this study, the safety of apoptotic cells (Allocetra-OTS) was assessed in a GLP toxicological study on Sprague Dawley rats. Three doses of Allocetra-OTS or vehicle were administered intravenously (IV) for 3 consecutive days. Animals in the Main study were sacrificed on day 4, while animals from the Recovery groups were kept for 14 or 28 days. Allocetra-OTS was well tolerated, and no adverse effects were observed in body weight, clinical signs, food consumption or ophthalmologic observation. Thus, the No Observed Adverse Effect Level (NOAEL) dose was determined as the highest dose administered. An observed elevation in immune cells was suspected to be due to Allocetra-OTS; similarly to other clinical chemistry parameters, however, it was resolved in the Recovery phases. Splenomegaly and a dose-related Extramedullary Hematopoiesis (EMH) in the red pulp were observed, with no adverse events, and were considered to be a normal and expected reaction following IV administration of cell-based therapies. In conclusion, under the conditions of this study Allocetra-OTS was concluded to be safe, further supporting its potential candidacy for clinical studies

Dysmenorrhea symptoms are frequent and often self-treated using non-prescription medicines. To further characterize women with dysmenorrhea using a combination of hyoscine butylbromide and paracetamol (PLUS) for self-management of their complaints, we performed a secondary analysis of a published pharmacy-based patient survey. 314 women (mean age 32.3 years) with dysmenorrhea reported a pain and cramps intensity of 7.45 ± 2.13 on a 0-10 Likert scale, which was reduced to 2.86 ± 1.81 upon treatment. Associated impairments of work/daily chores, leisure activities, and sleep were improved by 64.6%, 62.2% and 70.4%, respectively. The onset of symptom relief was within 60 min in 84.7%. Tolerability was rated as very good or good by 97.2%. These findings confirm the efficacy and tolerability data on PLUS from randomized controlled trials in a larger group of women conducting self-management of their dysmenorrhea.

Technological advancements must keep pace with earth’s rising demand for energy, while minimizing the carbon footprint on earth. One such option is using space based solar (SBS) radio frequency (RF) microwave power beaming. This innovative and novel technology is currently being explored to address terrestrial photovoltaic (PV) intermittency and provide a method to transmit power to other spacecraft in various orbits. This modality of power generation and distribution presents a new way to power terrestrial off-grid applications and earth orbiting spacecraft. In 1968, Dr. Peter Glaser published "Power from the Sun: Its Future", which serves as the foundation of this paper to revitalize scientific interest and efforts in this topic. This paper will primarily focus on the reliance of SBS initiatives on reusable launch vehicles (RLV) to place SBS spacecraft in a designated orbit, as well as the technological, economic, and operational challenges associated with power beaming to earth and other spacecraft. This includes a discussion of the variables from the operational environment that contribute or accelerate spacecraft end of life.

“Anti-stripping Agents" or "adhesion promoters” can enhance the chemical affinity between as-phalt and aggregate by increasing their mutual attraction. Various forms of anti-stripping agents have been proposed to mitigate pavement stripping, and siloxane-based Zychotherm is one of them. Choosing the appropriate type and dose of anti-stripping additives is no doubt vital to the intended performance. Therefore, it is critically important to determine the dose of the additives used in the modification of asphalt binders. This research developed a feasible detection method that can closely measure the dose (0.05% and 0.1%) of siloxane-based anti-stripping liquid agents. Related test methods including heat combustion test, residue visualization, burning, and ignition were implemented. Heat combustion results showed that with the addition of Zychotherm anti-stripping additive, average heat combustion value decreased by 1.34% and 1.72% for 0.05% and 0.1% Zychotherm modified binder respectively. In the burning and ignition process, the modified binder leaves yellowish substances in the residue, which is an indication of the presence of Zy-chotherm. The weight of the yellowish residue relates more to the quantity of Zychotherm in as-phalt binder.

We have successfully synthesized a series of Bi doped BaFe2As2 high quality single crystals for the first time. The x-ray diffraction (XRD) patterns show an expansion of lattice parameter c with Bi doping, indicating a negative pressure effect. By investigating the resistivity, a Fermi liquid (FL) to non-Fermi liquid (NFL) crossover is observed from normal state to antiferromagnetic order state, accompanied by three superconducting transitions labeled as SC I, SC II and SC III which are supposed to be induced from three superconducting realms with various Bi concentration. Thus, the NFL behavior is proposed to be closely related with the presence of superconductivity. The magnetic susceptibility measurements further speculates that the SC I and SC III phases should be filamentary superconductivity while the SC II be a possible bulk superconductivity with TC 7 K.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

This review explores the field of retreatment strategies for the cases filled with calcium silicate-based root canal sealers. Since the introduction of calcium silicate-based materials in dentistry, calcium silicate-based root canal sealers have become popular among dentists because of their biocom- patibility, bioactivity, and sealing ability. Therefore, effective retreatment strategies are indispensable. This article aims to identify the challenges associated with the removal of calcium silicate-based sealers itself and gutta-percha with the sealers during retreatment, evaluate current techniques and materials, and provide future directions for research in this field. Regarding the strategies of removal of root canal sealers, calcium silicate-based sealers are still relatively new materials for clinicians compared with the traditional sealers such as epoxy- or eugenol-based sealers. First, no clinically established solvents have been reported. Second, calcium silicate-based sealers are currently utilized by clinicians in either cold sealer-based technique or warm vertical condensation technique. Third, the setting process of calcium silicate-based sealers generates byproducts primarily calcium hydroxide and secondarily hydroxyapatite, which could interact with dentine. Lastly, there is a lack of clinical studies evaluating the efficacy of retreatment protocols for teeth filled with calcium silicate-based sealers. Therefore, it is important to investigate chemo-mechanical properties of calcium silicate- based sealers itself and their reactions to solvents and/or mechanical instruments, and identify the interfacial properties of calcium silicate-based sealers in respect to dentine and gutta-percha. In addition, researchers in clinical field need to actively gather and report data on retreatments of teeth filled with calcium silicate-based sealers.

Road pavement monitoring represents the baseline to the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a network of roads. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black box equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of these vehicles. In this study, a statistical method was to analyze the effects of speed on road pavement conditions at different severity levels through the data recorded by taxi vehicles. Likewise introduced was a process that overcomes the speed effect when considering road surface conditions. The methodology proposed has succeeded in predicting the right damage severity level based on only two recorded parameters: speed and pavement deterioration index.

Rural areas of advanced industrial countries have frequently experienced a dualistic set of policies: one designed for the land-use sector, especially agriculture; and another dealing with rural development [1]. As problems of industrialised agriculture and forestry emerged, a more joined up approach to policy was advocated. Since the 1980s, at national and international level, new elements of rural policy have emerged to connect these two policy fields, including environmental payments, farm diversification grants, woodland creation within the farm sector and support for community-led local development. Rural land is also closely connected to the biodiversity and climate crises which have become important policy drivers. Policy towards community-led local development has moved further in Scotland than almost anywhere else as a result of policies for community-based land reform, community empowerment and community asset transfer. These communitarian approaches connect closely to collaborative landscape management as it addresses environmental challenges. As collaborative place-based and area-based approaches emerge as the guiding stars of the new rural development, this paper explores the challenges in the governance of sustainability transitions in moving from a sectoral rural policy hinging around support for land use to one based much more on communitarian values and place-based development.

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

In industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects to the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named Video Multimethod Assessment Fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain the consistent quality while still achieving the high compression performance. First of all, to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of Rate-Distortion optimization (RDO) mechanism is adaptively computed under Rate-QP and Quality-QP models. Experimental results show that the proposed PVC has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and relevant benchmark, notably with around 10% bitrate saving in average.

In recent years, the field of drug delivery has seen a significant shift towards the exploration and utilisation of nanoparticles (NPs) as versatile carriers for therapeutic agents. With its ability to provide exact control over NPs' characteristics, microfluidics has emerged as a potent platform for the efficient and controlled synthesis of NPs. Microfluidic devices designed for precise fluid ma-nipulation at the micro-scale offer a unique platform for tailoring NP properties, enabling en-hanced control over NP properties such as size, morphology and size distribution while ensuring high batch-to-batch reproducibility. Microfluidics can be used to produce liposomes, solid lipid nanoparticles, polymer-based NPs and lipid-polymer hybrid NPs, as well as a variety of inorganic NPs such as silica, metal, metal oxide, quantum dots and carbon-based NPs, offering precise control over composition and surface properties. Its unique precision in tailoring NP properties holds great promise for advancing NP-based drug delivery systems in both clinical and industrial set-tings. Although challenges with large-scale production still remain, microfluidics offers a trans-formative approach to NP synthesis. In this review, starting from the historical development of microfluidic systems, the materials used to create the systems, microfabrication methods and system components will be discussed in order to provide the reader with an overview of micro-fluidic systems. In the following, studies on the fabrication of nanoparticles such as lipid NPs, polymeric NPs and inorganic NPs in microfluidic devices are included.

Considering higher stability and lower cost of nanozymes than native enzymes, the nanozymatic systems had been utilized for several practical application, especially for sensing and detection. Most of common nanozymatic sensors are single-nanozyme based systems, however, recently a new generation of nanozyme-based systems called “multinanozyme system’ was introduced by Hormozi Jangi et al. (2020). Since the first report of multinanozyme systems, several multinaozyme systems have been developed and utilized for highly sensitive and selective sensing aims. The main advantages of multinaozyme systems compared of common nanozymatic sensors are their impact on simultaneous enhancing selectivity and sensitivity of sensor in a well-designed detection process. Since, the principles of design and detection mechanism of this new generation is not well-described in the literature, the aim of this article is the fast review of the principles of design of this new generation of nanozyme-based sensing and detection.

Massification in higher education has made the satisfaction of students’ needs a difficult conundrum among university lecturers. In contrast, the use of innovative design for collaborative learning enhances student engagement in the populous student’s context. Moreover, this paper aims to extensively explore innovative designs for fostering student engagement and collaborative learning among first-year students at the University of Venda. The study employed a qualitative research method with the purposive sampling technique. Subsequently, a group of 200 students was the population of this study. Participant Observation and narrative inquiry were used as data collection instruments. The students in their respective groups were assigned topics from the module content to use their talents to demonstrate their understanding of certain content in the module. The non-surprising findings of this paper elucidated that collaborative learning expedites students’ mastery of key concepts and subject content. The module lecturers introduced students to these innovative designs to ensure collaborative learning and effective student engagement. The key findings elucidated these aspects namely, role-playing exercises, group projects, peer-to-peer learning, use of talents, and peer feedback. The implication of this study is that students learn to work together, delegate responsibilities and communicate effectively to attain a common goal. Using these strategies, the lecturers promote collaboration among students and foster a more engaging and interactive learning experience. This paper further recommends efficient and effective techniques and strategies to foster student engagement and collaboration to track and monitor at-risk students timeously.

Advancements in computing technology have resulted in significant changes in education, healthcare, and manufacturing fields. Thus, personnel training in computer-related fields is directly related to national competitiveness. Therefore, the importance of programming education has been emphasized worldwide. Programming education has been conducted since the 1980s, however beginners often find programming tedious and difficult because of the cognitive burden of using text commands. Therefore, block-based programming environments, such as Scratch and Code.org, and beginner-oriented programming environments, such as Blockly and Pencil Code, have been de-veloped. However, they have limitations when transitioning from block to text-based programming. In this study, we conducted one semester of classes for 128 middle school, high school, and uni-versity students to determine whether an environment that allows using a text-based programming language in a block-based programming environment aids beginners’ understanding of program-ming instructions, command usage confidence, and programming usefulness. The results confirm that the usability of a block-based environment positively influences programming perception. This study is significant because it verifies the necessity and effectiveness of a block-based environment that employs a text-based programming language in programming education for beginners.

For women giving birth, every moment of delay in receiving skilled care significantly increases the risks of stillbirth, neonatal and maternal death. More than half of all births in developing countries including South Africa, take place outside a health facility and without skilled birth attendants. This has therefore, made it difficult to achieve the Sustainable Development Goals of global reduction in maternal mortality, which is a key health challenge globally, especially in developing countries and sub-Saharan Africa in particular. The aim of this study was to explore and describe the views of pregnant women regarding facility-based delivery. Focus group discussions were used to gather information from the pregnant women. Information was collected from six groups of pregnant women who had delivered babies at the antenatal care facilities in the past years. Results showed several factors associated with the failure to use institutional delivery service, such as long distance from the health care facility, lack of transport, lack of transport fare, shortages of skilled staff, failure to disclose pregnancy, cultural and religious beliefs, and staff attitudes.

Model-based approaches form the basis of the majority of damage detection and localization studies; however, built-in online detection technique can offer an alternative for future structural health monitoring technologies. Thus, in this paper, we present a dynamic localization algorithm for damage localization in functionally graded plates. The method depends on the creation of a grid matrix that contains the dynamic response of the structure over time. Subsequently, an optimization process is carried out via a linear equation representing the information contained within the grid to achieve accurate localization of damage. Due to the inherent sparsity of the localization nature, the FISTA algorithm has been employed as a problem solver. The approach is tested in the case of the functionally graded plate under clamped free boundary conditions. Several, damage scenarios were investigated for the case of damage signals on and off the grid. The results show that the proposed approach is capable of accurately predicting damage position and can be suitable for use in low size data systems.

Diabetes mellitus (DM) comprises a range of metabolic disorders characterized by high blood glucose levels caused by defects in insulin release, insulin action, or both. DM is a widespread condition that affects a substantial portion of the global population, causing high morbidity and mortality rates. The prevalence of this major public health crisis is predicted to increase in the forthcoming years. Although, several drugs are available to manage DM, these are associated with adverse side effects, which limits their use. In underdeveloped countries, where such drugs are often costly and not widely available, many people continue to rely on alternative traditional medicine, including medicinal plants. The latter serve as a source of primary healthcare and plant-based foods in many low and middle-income countries. Interestingly, many of the phytochemicals they contain have been demonstrated to possess antidiabetic activity such as lowering blood glucose levels, stimulating insulin secretion and alleviating diabetic complications. Therefore, such plants may provide protective effects that could be used in the management of DM. The purpose of this article was to review the medicinal plant-based foods traditionally used for the management of DM, including their therapeutic effects, pharmacologically-active phytoconstituents and antidiabetic mode of action at the molecular level. It also presents future avenues for research in this field.

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.

Integrating artificial intelligence (AI) and natural language processing (NLP) technologies with project-based learning experiences in Science,Technology, Reading, Engineering, Arts, and Mathematics (STREAM) education offers the potential to enhance student engagement, critical thinking,problem-solving, and interdisciplinary learning. This paper presents a comprehensive approach to implementing AI and NLP technologies, such as ChatGPT, in STREAM project-based learning experiences, highlighting the potential advantages and challenges of this innovative educational approach. The proposed method encompasses personalized learning pathways, AI-powered research assistance, collaborative AI tools, real-time feedback, virtual mentoring, advanced simulation, and objective assessment. We also discuss the implications of this integration for educators, institutions, and students, along with recommendations for future research and development in this emerging field.

China promotes nature-based solutions (NbS) as key approaches to addressing climate change, ecosystem damage, and biodiversity loss. However, indigenous and local knowledge (ILK), which is recognized by international scholars as an essential element of successful NbS, has not been thoroughly studied in the Chinese context. By conducting a comprehensive review of Chinese central governmental policies and semi-structured interviews with typical cases, this study finds out diversified sources of ILK and five pathways of ILK integration, including education, supervision, participation, knowledge preservation, and knowledge adoption. An increasing emphasis on community engagement has been driven by central policies such as the ecological civilization, carbon peaking and carbon neutrality goals. From a practical perspective, consistent core ideas of ILK exist in selected typical Chinese cases and studies in other countries, namely co-existence and reciprocity with nature. While supportive central policies can set the scene, ILK integration could not be realized without local governmental endorsement, context-based implementation, and long-term multi-stakeholder participation. Furthermore, this study also figures out a potential deficiency in terms of realizing effective ILK integration in mainstream and conventional practices, which can be further investigated by future studies.

In this paper is shown the dynamic modeling and design of a passivity based controller for the RV-3SB robot. Firstly, the dynamic modeling of the of the Mitsubishi Rv-3SB robot is done by the Euler-Lagrange formulation in order to obtain a decoupled dynamic model considering the actuators orientation besides the position of this analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is done based in the kinematic model obtention in which is developed by the implementation of screw theory. Then the passivity based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity based controller is obtained by selecting an appropriate storage function and by the Lyapunov theory the passivity based control law is obtained in order to drive the error variaable, which is the difference between the measured end effector position variable and the end effector desired position variable. The passivity based controller makes that the error variable reach the origin in finite time taking into consideration the dissipation properties of the proposed controller in order to stabilizes the desired end effector position. A numerical simulation experiement is performed in order to validate the theoretical results obtained in this research study. It is verified by numerical experimentation that the proposed control strategy is efficient and effective in order to drive the error variable to the origin in comparison with other modified techniques found in the literature. Finally, the appropriate discussion and conclusion of this research study are provided.

Understanding of the correlation between the magnetomechancial coupling factors (k) and damping factors (Q^-1) is a key pathway to enhance the magnetomechanical power conversion ef-ficiency in laminated magnetoelectric (ME) composites by engineeringly manipulating the magnetic and mechanical properties of Fe-based amorphous metals. The k and Q^-1 factors of FeSiB amorphous ribbons annealed in air at different temperatures are investigated. It is found that k and Q^-1 factors are affected by both magnetic and elastic properties. The magnetic and elastic properties are equalized in terms of the magnetomechanical power efficiency for the low temperature annealing. The k and Q^-1 of FeSiB-based epoxied laminates with different stacking numbers show that the -3 dB bandwidth and Young’s modulus are equalized in terms of the magnetomechanical power efficiency for the high lamination stacking.

A bijective analysis is confirming, the expansion of universal space has never been directly observed, and this assumption is unproven thus far. The measurement of the gravitational redshift, which was confirmed using the Mossbauer experiment, proves only the gravitational redshift and nothing more because there is no causality between the gravitational redshift and hypothetical expansion. Thus, even if the universal space is assumed to expand, the gravitational redshift cannot be assumed to be proof of the expansion. In addition, the Doppler effect was never observed in an expanding space; thus, the claim that the cosmological redshift is partially caused by the Doppler effect, which is caused by the expansion of space, is an unproven assumption. Furthermore, the discovery of cosmic microwave background (CMB) radiation simply proves that the radiation is emitted by the entire universal space, but does not prove the existence of a recombination period. In evidence-based cosmology, every element in the model has a corresponding element in physical reality that is observed and measured. The evidence-based cosmology model is related to the real universe by a bijective function of set theory. Supermassive black holes in the centre of galaxies are rejuvenating systems of the universe. They rotate their local superfluid space which is the physical origin of galactic rotation curves.

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

Considering the current energy environment, both efficient and environmentally friendly solutions have to be developed for building construction. Bio-based building materials offer new perspectives through their insulating and natural humidity regulation capacities. Nevertheless, these materials are as complex as they are promising and grey areas still remain regarding their behavior. Their water sorption and desorption curves recorded in experimental work demonstrate a hysteresis phenomenon and, although plausible hypotheses have been formulated in the literature, there is currently no consensus on its causes. Furthermore, it is important to emphasize that no reference considers the hydrophilic nature of the resource. Yet this is a specificity of raw material coming from the plant world. In this context, this paper explores the microstructure and chemical composition of plant aggregates to propose a new explanation for the hysteresis. It is based on recent work demonstrating the existence of differentiated hydrogen bonds between the water sorption and desorption phase in cellulose. Obviously, hysteresis also has an origin at the molecular scale. Lastly, the hypothesis put forward here is supported by the swelling of bio-based materials that has been observed at high relative humidity and this study aims to identify a link between the mechanical (swelling/shrinking) of bio-based materials and their hygroscopic behavior. This leads to a better understanding of the hydro-mechanical coupling of these materials.

One-fifth of the world’s population and critical infrastructures are near the coast and regions at high risk of sea level elevation. Climate change is expected to increase coastal extreme events, rising sea levels, and impact on ecosystem. This paper reviews coastal physical processes, wave and impacts, and the introduction of nature-based models for the mitigation of flooding, erosion, and recovery. Hard engineering like seawalls has been used to prevent, protect, and control water-based environmental forces with an extended impact on the land. A nature-based engineering solution, such as growing vegetation, is being adopted as a sustainable solution to help make existing technology live its design life and provide climate change adaptation and resilience for coastal and riverine communities. This paper presents applications of seaweed farms as an advanced nature-based mitigation approach. The result of the experiments conducted at RWTH Aachen University on wave damping of seaweed types and farming structures to validate the hypothesis will be presented in part B. A soft engineering approach to designing future vegetated protection systems using seaweed as a nature-based solution can help existing coastal infrastructure design life and protect against climate-induced SLR rise and adaptation, coastal risk mitigation, ecosystem restoration, and blue bio-economic development.

The rapid development of quantum computing devices promises powerful machines with capabilities that solve a wide range of problems that traditional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is considered to be the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, like security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is presented also, and focuses on their strengths and weaknesses.

Many assets of clinical medicine, such as clinical chemistry and diagnostic imaging, make clinical diagnosis outstanding compared to postmortem diagnosis. An assessment of functional status certainly has priority over the postmortem, cross-sectional use of diagnostic tests and laboratory equipment. In addition, the cost of these tools is sometimes steep, and their use does not always fit into a reasonable cost–benefit ratio. However, sometimes postmortem observations, such as inflammation, pulmonary edema, or infiltration and cerebral swelling, cannot be explained without implementing immunohistochemical markers for postmortem diagnosis. Introducing blood-based biomarkers in postmortem care could significantly reduce the rates of inconclusive postmortems and discrepancies in autopsy findings and clinical diagnoses. This is particularly relevant in the scope of vascular pathology, considering the great burden of vascular diseases in overall mortality. Expanding traditional autopsy with blood-based (circulating) biomarkers to avoid invasive postmortem examination would have cultural, religious, and potential economic advantages. All of the target molecules are discussed in the context of the process they up-regulate or down-regulate and which turns out to be the final cause of death. Ultimately, it is evident that further studies are needed to provide concrete validation for a combination of markers on a specific case order to reach a postmortem diagnosis with or without clinical records.

Fe rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. These alloys can also be added to hard magnetic compounds to obtain spring magnets. In this work we produce two nanocrystalline Fe-Zr-B-Cu alloys by the powder metallurgy powder technology of mechanical alloying. The increase of the boron content favors the reduction of the crystalline size. Thermal analysis (by differential scanning calorimetry) shows at temperatures between 450 and 650 K wide exothermic processes, associated with the relaxation of tensions of the alloys produced by milling. At high temperatures, a main crystallization peak is found. The apparent activation of this process was determined by Kissinger and isoconversional methods. The values are compared with those found in the scientific literature. Likewise, adapted thermogravimetry permits to determine the Curie temperature. The functional response has been analyzed by hysteresis loop cycles. According to the composition, the decrease of the Fe/B ratio diminishes the magnetic soft behavior.

The oncologic disease is a big global health issue that causes thousands of deaths annually, and it has a significant impact in the life quality of patients. Prostate cancer (PCa) is the second most diagnosed cancer and the fourth leading cause of cancer-related death in men in the western world. Delineation of pathogenetic pathways and key driver molecular alterations involved in PCa development has provided a roadmap for the evaluation of biomarkers in predicting disease outcome and to identify potential therapeutic targets. Chemotherapeutic agents introduced from the 1990s include the taxanes (paclitaxel, docetaxel and cabazitaxel), which are the most anticancer drugs used for PCa treatment. This review presents the current knowledge about the onset and development of PCa, state-of-art on the use of taxane-based therapy, and their combination with targeting different transmembrane oncoproteins in PCa. The silencing of some transmembrane proteins can improve taxane sensitivity, and therefore, may be a mechanism to improve the ef-fectiveness of these drugs in PCa treatment. This combined therapy needs to be explored as po-tential therapeutic agent for reducing cell proliferation, migration, and invasiveness in PCa.

Objective: To investigate the feasibility and preliminary effects of a 12-week home-based moderate intensity cycling programme in stroke survivors. Design: Single-blinded, parallel group, Randomised Controlled Trial Setting: Home-based Participants: Participants will be survivors of first-ever stroke and randomly assigned to an Active Cycling Group (ACG) or control group. Intervention: The ACG performed a 12-week home-based cycling programme, three sessions per week. Work intervals were executed at 75% of the maximal heart rate (HR_max), recovery intervals at 50% of HR_max. Work interval duration increased from three ten-minute intervals to 30 minutes continuous cycling. The control group completed a 12-week neurocognitive exercise programme. Main outcome measurements: Primary outcome measures are compliance with training, adherence to protocol and safety as measured by adverse events. Secondary outcome measurements included a: simplified modified Rankin Scale questionnaire (stroke severity), Six-Minute Walking Test (walking ability), Ten-meter Walk Test (walking ability), Rivermead Mobility Index (walking ability), graded submaximal cycling test (exercise capacity), Physical Activity for Individuals with Physical Disability questionnaire (physical activity), EQ-5D-5L (quality of life), Stroke Specific Quality of Life questionnaire (quality of life), Montreal Cognitive Assessment (cognition), General Self-Efficacy Scale (self-efficacy), and Exercise Self-Regulation Questionnaire (self-regulation).

Close to half (45.4%) of 2.3 million breast cancers (BC) diagnosed in 2020 were from Asia. While the burden of breast cancer has been examined on the level of broad geographic regions, literature on more in-depth coverage of the individual countries and subregions of the Asian continent is lacking. This review examines the breast cancer burden in 47 Asian countries. Breast cancer screening guidelines and risk-based screening initiatives are discussed.

ASEAN SME has a role as the regional socioeconomic stabilizer. This particular role is inseparable from endogenous multi-sector collaboration. Although, Indonesian SMEs were struggled in adopting Industry 4.0 correspond to digital infrastructure and digital literacy problems. This study evaluates Indonesian SME collaboration dynamics with government and technology startup (TS). The integration of agent-based model and causal loop simulation were employed to assess the TS collaboration impact on SME Industry 4.0 adoption and SME competition with larger competitors. The simulation results imply the SME collaboration with TS can lead to early adoption of Industry 4.0 which balances the business competition environment. The model also shows rising the government aid exponentially can help the SME to late adoption of Industry 4.0 which unable to sustain the SME in business competition. Thus, the developed integrative simulation model is a state-action planning model with each state result bounded to the previous state result that determined by initial input parameters. Conclusively, the model can be used as a resiliency planner for SME Industry 4.0 adoption.

After a long limbo, RNA has gained its credibility as a druggable target, fully earning its de-served role in the next-generation area of pharmaceutical R&D. We have recently probed the Trans-Activation Response element (TAR), a RNA stem–bulge–loop domain of the HIV-1 genome with bis-3-chloropiperidines (B-CePs), and revealed the compounds unique behavior in stabiliz-ing TAR structure, thus impairing in vitro the chaperone activity of the HIV-1 nucleocapsid (NC) protein. Seeking to elucidate the determinants of B-CePs inhibition, we have further characterized here their effects on the target TAR and its NC recognition, while developing quantitative analyti-cal approaches for the study of multicomponent RNA-based interactions.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

Due to the continuing high suicide rates among young men, there is a need to understand help-seeking behaviour and engagement with tailored suicide prevention interventions. The aim of this study was to compare help-seeking among younger and older men who attended a therapeutic centre for men in a suicidal crisis. In this case series study, data were collected from 546 men who were referred into a community-based therapeutic service in North West England. Of the 546 men, 337 (52%) received therapy; 161 (48%) were aged between 18 and 30 years (mean age 24 years, SD=3.4). Analyses included baseline differences, symptom trajectories for the CORE-34 Clinical Outcome Measure (CORE-OM) and engagement with the therapy. For the CORE-OM there was a clinically significant reduction in mean scores between assessment and discharge (p<0.001) for both younger and older men. At initial assessment, younger men were less affected by entrapment (46% v 62%; p=.02), defeat (33% v 52%; p=.01), not engaging in new goals (38% v 47%; p=.02), and positive attitudes towards suicide (14% v 18%; p=.001) than older men. At discharge assessment, older men were significantly more likely to have an absence of positive future thinking (15% v 8%; p=0.03), have less social support (45% v 33%; p=.02) and feelings of entrapment (17% v 14%; p=.02) than younger men. Future research needs to assess the long-term effects of help-seeking using a brief psychological intervention for young men in order to understand whether the effects of the therapy are sustainable over a period of time following discharge from the service.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfillment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

Due to the continuing high suicide rates among young men, there is a need to understand help-seeking behaviour and engagement with tailored suicide prevention interventions. The aim of this study was to explore help-seeking behaviour and engagement for young men aged 18 to 30 years who attended a therapeutic centre for men in a suicidal crisis. In this prospective cohort study, data were collected from 546 men who were referred into a community-based therapeutic service in North West England. Of the 546 men, 337 (52%) received therapy; 161 (48%) were aged between 18 and 30 years (mean age 24 years, SD=3.4). One third (n=54; 34%) of the men were seen within 48 hours of their referral. Analyses included baseline differences, symptom trajectories for the CORE-34 Clinical Outcome Measure (CORE-OM) and engagement with the therapy. For the CORE 34 there was a clinically significant reduction in mean scores between assessment and discharge (p<0.001), with all outcomes demonstrating a large effect size. Future research needs to assess the long-term effects of help-seeking using a brief psychological intervention for young men in order to understand whether the effects of the therapy are sustainable over a period of time following discharge from the service.

It’s always a challenge for a teacher to get their students to be more engaged or active in the classroom. Engagement happens when students are interested in the subject, have fun, and feel welcome in the classroom. But how do we make students more engaged? To make this happen, Educational escape rooms were introduced into studies. In this paper, we will discuss about the creation and evaluation of educational escape rooms within an engineering learning context. As part of our research project, four educational escape rooms were created for students and surveys were conducted among them to evaluate the success of our project. Our findings indicate that the escape room learning model is well accepted by the students. As a result of the activity, high levels of dedication and enthusiasm are recorded and students shows the eagerness to do more escape room activities.

In some parts of Nigeria, many girls do not attend school, and among those sent to school, many still drop out early. This and other socio-cultural factors affect girls psychologically. There is no doubt that girls need consistent love and tutoring to guide them through the turbulent teen years and beyond. They need a mentor who acts as a friend and a role model. The Mobile-based Mentoring Platform seeks to leverage on mobile technology's affordances to focus on the needs of the girl-child, such as improvement in academic achievement, guidance in career choice, development of self-concept, and esteem. The girl-mentees comments revealed that using the platform provided them frequent access to mentors and access to learning opportunities. The challenges they faced include epileptic internet network, intrusions by parents, and others. Therefore, this paper examined the challenges and benefits of mentoring girls via a mentoring platform.

Course-based undergraduate research experiences (CUREs) provide students with opportunities for the same gains that apprenticed research with faculty members offer. As their popularity increases, it is important that critical elements of CUREs are supported by thoughtful design. Student experiences in CUREs can provide important insights into why CUREs are so effective. We present evidence from students who participated in CUREs at the introductory, intermediate, and advanced levels, as well as from graduate teaching assistants for an introductory lab course that included a CURE. Students and teaching assistants describe repetition as a valuable element in CUREs and other laboratory experiences. We used student work and open-ended interviews to identify which of five previously described elements of CUREs students found important. Because repetition was particularly salient, we characterized how students described repetition as they experienced it in courses that contained full-length or “micro”-CUREs. In prompted interviews, students described how repetition in CUREs provided cognitive (learning concepts) and practical (learning technical skills) value. Recent graduates who had participated in CUREs at each level of their Biology education were particularly aware that they placed value in repetition and acknowledged it as motivational in their own learning. Many students described repetition in metacognitive terms, which also suggests that as students advance through laboratory and CURE curricula, their understanding of how repetition supports their learning becomes more sophisticated. Finally, we integrated student descriptions to suggest ways in which repetition can be designed into CUREs or other laboratory courses to support scientific learning and enhance students’ sense of scientific identity.

Starting from the importance of risk perception for taking certain preventive measures to protect people and their property from disasters, the subject of the research is to examine the factors influencing public perception of mythically-based human behavior in disaster conditions. Using the random sampling method, 250 adult respondents were surveyed in the city of Belgrade, using a specially created and adapted survey questionnaire. The results of the research show that there is no statistically significant influence of gender, age, educational and economic factors on the public perception of human behavior in disaster conditions. The results of the research can be used to improve strategies and campaigns based on risk assessment, aimed at improving the safety of people in disasters.

Latin binomials, popularised in the eighteenth century by the Swedish naturalist Linnaeus, have stood the test of time in providing a stable, clear and memorable system of nomenclature across biology. However, relentless and ever-deeper exploration and analysis of the microbial world has created an urgent unmet need for huge numbers of new names for Archaea and Bacteria. Manual creation of such names remains difficult and slow and typically relies on expert-driven nomenclatural quality control. Keen to ensure the legacy of Linnaeus lives on in the age of microbial genomics and metagenomics, we propose an automated approach, employing combinatorial concatenation of roots from Latin and Greek to create linguistically correct names for genera and species that can be used off the shelf as needed. As proof of principle, we document over a million new names for Bacteria and Archaea. We are confident that our approach provides a road map for how to create new names for decades to come.

Universitas Sebelas Maret (UNS) through SMART UNS Company has conducted research and development of e-motorcycle conversion using Li-ion battery pack as a substitute for ICE energy source from the conventional motorcycle. Currently, the battery-pack that used for e-motorcycle conversion is in the development phase towards commercialization. The challenge of estimating production costs is the complicated production process and storing hidden expenses that can be a problem. This hidden cost is often a missing or varied factor that costs less or more expensive. This study presents an integrated parametric cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each activity. Activity-based costs break the production process into a specific cost element for each step. Each activity's cost is put into a parametric cost estimation model to calculate the cost of each activity into the total cost of production. Cost estimation results will be analyzed using a regression method to determine which variables most affect the production cost of Li-ion battery packs for the conversion of e-motorcycles in the SMART UNS company.

The complexity of the design in high-rise residential projects is a challenge for the construction industry in completing projects that fit the needs of users. Performance-Based Building Design (PBBD) appears as a design concept that can describe these needs into performance requirements. In this case designing a building can be considered as an iterative process of exploration, where desired functional properties can be created, the shapes are suggested, and evaluation processes is used, so as to bring together the shapes and functions of the building. This concept is a container for designers to produce high-performance buildings. This study aimed to identify the performance-based building design factors applied by architect designers and engineers in high-rise residential building in Surabaya. As part of this study, primary data was collected based on surveys conducted through observation and questionnaire distributed to designers who had or were involved in the high-rise residential design process in Surabaya. A total of sixty-eight respondents were included in this study. Descriptive analysis through a mean and standard deviation scatter plot was used to rank the application of PBBD. Meanwhile, factor analysis was used in the analysis of PBBD application factors. From the results of the analysis, four factors were obtained for the application of PBBD in high-rise residential buildings in Surabaya, namely; the interests of occupants, the sustainability of building operations, the design collaboration process, and the risk of loss. Future research is the influence relationships and measure the success model of PBBD at a higher level into BIM (Building Information Modeling) interoperability.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

Visualization of the seabed terrain is one of the key functions of marine geographic information system. The major challenge here is that the obtained data for seabed terrain usually consists of elevation and sediment only but does not include remote sensing images, which is important for ground terrain visualization, as they can be used as textures to reveal detailed information and achieve realistic visual results. Existing seabed terrain visualizing methods (including annotations, color-mapping, and texture-mapping) have limitations in reality and intuition, as they are inadequate to express the physical characteristics of sediment accurately. This paper presents a novel and advanced 3D visualization method of seabed terrain, which introduces the Physically Based Rendering (PBR) theory into the field of geographic visualization. We analyze the main categories of seabed sediments and their optical features respectively, then develop a procedural method to generate physically based rendering materials for different sediments. Then an enhanced bidirectional reflectance distribution function is employed to achieve accurate and intuitive terrain rendering. We also refine the texture sampling method and propose a procedural seabed objects generation method to construct a more natural and realistic undersea environment. Experimental results reveal our method can make good use of the limited seabed terrain data and get significant improvements in visual effect, which can help users to cognize and analyze the seabed geographic environment more accurately and intuitively.

The microstructure and solidification behavior of nickel based GTD222 superalloy at different cooling rates are studied. The solidification of the GTD222 superalloy proceeds as follows: L→L+γ, L→L+γ+MC, L→L+(γ/γ ′)-Eutectic and L→η phase. The temperature of liquidus of GTD222 superalloy is 1360 °C while the solidus is slightly lower at 1310 °C, which due to the alloying elements redistribution. It was found that the dendrite arm spacing of the alloy decreased with the increase of cooling rate (From 200 μm at 2.5 K/min to 100 μm at 20 K/min).

Previously, we described the initial use of Flipped Team‐Based learning (FTBL) defined as TBL approach combined with flipped classroom learning methodology, in which students previewed online lectures and applied their knowledge in different in-class activities. The purpose of the present study is to review the progress within this approach and to investigate how constant changes in team allocation can affect student’s perception regarding this modified FTBL approach. Although students showed reluctance initially to get out of their ‘comfort zone’, our findings show that learners perceived the adoption of the continued random allocation, and became accustomed to this learning approach, which finally assisted them to enhance their team-work skills and classroom performance, to develop their reflective capabilities as well as improving their rapport building skills, learning and academic performance. Learners also believed that this learning strategy that creates critical incidents can simulate their future work environment as they might be expected to work in unfamiliar situations. Therefore, the present study indicated strong support for the modified FTBL method and was seen to work exceptionally well, despite some minor problems that students can experience working in a team and/or with different teammates in every session.

The effect of microstructure and chemistry of passive films on the kinetics of passive layer growth and passivity breakdown of some Ti-based alloys, namely Ti-6Al-4V, Ti-6Al-7Nb and TC21 alloys was studied. The rate of pitting corrosion was evaluated using cyclic polarization measurements. Chronoamperometry was applied to assess the passive layer growth kinetics and breakdown. Microstructure influence on the uniform corrosion rate of these alloys was also investigated employing Tafel extrapolation and dynamic electrochemical impedance spectroscopy. Corrosion studies were performed in 0.9% NaCl solution at 37 oC, and the obtained results were compared with ultrapure Ti (99.99%). The different phases of the microstructure were characterized by X-ray diffraction and scanning electron microscopy. Chemical composition and chemistry of the corroded surfaces were studied using X-ray photoelectron analysis. For all studied alloys, the microstructure consisted of α matrix, which was strengthened by β phase. The highest and the lowest values of the β phase’s volume fraction were recorded for TC21 and Ti-Al-Nb alloys, respectively. The uniform corrosion rate and pitting corrosion resistance (R_pit) of the studied alloys were enhanced following the sequence: Ti-6Al-7Nb < Ti-6Al-4V << TC21. The corrosion resistance of Ti-Al-Nb alloy approached that of pure Ti. The obvious changes in the microstructure of these alloys, together with XPS findings, were adopted to interpret the pronounced variation in their corrosion rates.

Bangladeshi construction industry suffers a lot of safety and accidental issues than other developing countries in the world. Among many of these, accident of construction project goes far beyond and shape a horrific figure of death for every year. The aims of this study is that analysis and discussion of causes of accident at construction project in Bangladesh. A widespread statistical data collection and data analysis take place to identify the causes and design the questionnaire. The questionnaire-based survey was used to elicit the attitude of four stakeholders as workers, owners, consultants, and contractors towards passive causes of fatal accident at construction site. These study also identify 77 passive causes under 14 major groups and ranked them based on Relative Importance Index (RII). The top 5 major group of causes are (1) Management related, (2) Consultant related, (3) Technology related, (4) Labour related and (5) Contractor related. The top 5 passive causes are: (1) Unaware of safety-related issue, (2) Lack of personal protective equipment, (3) Lack of safety eliminating/ avoiding design, (4) Unfit equipment, (5) Lack of knowledge and training on equipment.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

Some contaminants of emerging concern (CECs) are known to survive conventional wastewater treatment plants, which introduce them back to the environment and can potentially cycle up in drinking water supplies. This is especially concerning because of the inherent ability of some CECs to induce physiological effects in humans at very low doses. Advanced oxidation processes (AOPs) such as TiO2 based photocatalysis are of prominent interest for addressing CECs in aqueous environments. Natural water resources often contain dissolved metal cations concentrations in excess of targeted CEC concentrations. These cations may significantly, adversely impact degradation of CECs by scavenging TiO2 surface generated electrons. Consequently, simple pseudo first order or Langmuir-Hinshelwood kinetics are not sufficient for reactor design and process analysis in some scenarios. Rhodamine B dye and dissolved copper cations were studied as reaction surrogates to demonstrate that TiO₂ catalyzed degradation for very dilute solutions is very nearly completely due to homogeneous reaction with hydroxyl radicals and that in this scenario the hole trapping pathway has negligible impact. Chemical reaction kinetic studies were then carried out to develop a robust model for RB/metal reactions that is exact in the electron pathways for hydroxyl radical production and metal scavenging.

To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

This paper outlines evidence-based practice in the context of professionalism, and highlights the contribution evidence-based practice can make to the professional practice of higher education course managers. Implications of the changing HE landscape for the status of academics as professionals are reviewed, and evidence-based practice is proposed as a solution for both enhanced course management and to remedy perceived deprofessionalisation. Finally, questions regarding researching professional practice within one’s own institution are addressed.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

This study explored the intricate nexus between public service provision and land use planning, with a specific focus on the conservation of local plant species in Tambon Khao Phanom, Amphoe Khao Phanom, Krabi Province. Rooted in commitment. The research framework encompasses three fundamental activities: resource survey and collection, resource conservation and utilization, and resource development planning for public services, emphasizing land use planning for local plant species conservation. This multidisciplinary approach integrates the one-map method and draws data from the Department of Land Development to formulate a comprehensive land utilization plan that optimizes land use for biodiversity conservation. This study underscores the critical synergy between public service delivery and land use planning, highlighting their collective potential to advance community well-being and sustainable development.

Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.

This study, conducted with meticulous attention to detail, comprehensively examines the state of plants and their adaptation to Kazakhstan's extra-arid climatic zone of the Mangistau. It uses the collections of woody plants from the Mangyshlak Experimental Botanical Garden in Aktau as a case study and extrapolates the results to the entire region. The study area is arid and unsuitable for many plants, so examining physiological adaptation mechanisms and strategies for preserving plants in unfavourable conditions is crucial. The study employed a range of methods, including the measurement of the intensity of transpiration, leaf hydration, and the content of chlorophyll A, B, and carotenoids. These methods were chosen for their proven effectiveness in similar studies and their ability to provide accurate and reliable data. The samples were also assessed using a point system for resistance to drought, phytophagy, gas tolerance, salt tolerance, winter hardiness, and soil fertility. The results obtained and entered into the IEBG platform “DinTseR” to assess the value of the introduction using a diagnostic scale revealed a high variability (up to 51.7-52.2%) of physiological indicators of growth and development. The highest values of transpiration intensity were observed in deciduous trees and shrubs, rose varieties (205-243 mg/g fresh leaf mass per hour), leaf water content - in climbing plants (68.9%), chlorophyll A and B content - in deciduous plants, and plants - old trees, fruit trees and varietal roses (3.46-3.93 and 1.34-1.84%). Their concentration in coniferous species is almost two times lower (0.63%). A correlation was also made between various significant factors. Twenty regression equations and forecasts have been identified that can be used to diagnose and assess resistance, thereby reducing the cost of determining the introduction value in the conditions of Mangistau. These findings are essential in understanding and preserving the unique flora of the Mangistau region.

Conventional fertilization practices can lead to many ecological problems, such as nutrient imbalance, soil acidity, and reduced soil fertility in natural rubber plantations. To address these challenges, a field investigation was strategically carried out to substitute inorganic fertilizer with organic fertilizers consisted of six treatments, i.e., no fertilization (CK), inorganic fertilizer (NPK), 25 % replacement of inorganic through organic (25%M), 50 % replacement of inorganic through organic (50%M), 75 % replacement of inorganic through organic (75%M), and 100% organic fertilizer (100%M). The soil physicochemical properties (SOC, TN, AN, C:N, pH, NH4+-N, and NO3--N) and carbon and nitrogen converting enzymes (BG, NAG, and LAP) were all determined. The partial substitution of inorganic fertilizers with organic fertilizers (i.e., 75%M at surface soil layer) showed higher SOC (14.52 g.kg-1), TN (1.06 g.kg-1), AN (20.07 mg.kg-1), C:N (14.63), NH4+-N (10.63 mgkg-1), and NO3--N(11.06 mg.kg-1) than NPK and CK. Such increase in physicochemical properties in partial replacement of inorganic with organic fertilizers plots resulted from higher carbon and nitrogen enzymes activities (BG (143.17 nmol.g-1.h-1), NAG (153.96 nmol.g-1.h-1), and LAP (153.48 nmol.g-1.h-1)) compared to NPK and CK, and further the person correlation and RDA analysis confirmed a significant positive correlation between SOC, AN, and soil enzymes. This study presents a new strategy for assessing the impact of partially replacing inorganic fertilizers with organic fertilizers in rubber plantations, mainly by modifying the soil nutrient composition in tropical regions.

Bacteria belonging to the genus Micromonospora are recognized as microorganisms with potential to be used in biotechnology processes, including their beneficial influence on plant growth and biocontrol of phytopathogens. In this study, nineteen Micromonospora isolates originating from root nodules of white clover plants were taxonomically assigned based on the phylogenetic analysis of the 16S rRNA gene and four housekeeping genes. The antifungal properties of the bacteria against phytopathogenic Botrytis cinerea, Fusarium oxysporum, Fusarium equiseti, Sclerotinia sclerotiorum, and Verticillium albo-atrum were tested with the agar plug test and the dual culture test. The ability to produce various metallophores was determined with the agar plug diffusion test on modified CAS agar medium. ISP2 broth amended with 0.2% L-tryptophan was used to indicate the bacterial ability to produce auxins. Strains belonging to M. tulbaghiae, M. inaquosa, and M. violae showed in vitro potential as antimicrobial agents against the tested fungi. M. inaquosa strain 152, M. violae strain 126, M. violae strain 66 and M. violae strain 45 were recognized as the most efficiency metallophores producers. M. alfalfae strain 55 and M. lupini strain 5052 were identified as the most promising auxin compound producers and, therefore, as potential plant growth promoting bacteria.

Mitochondria are semiautonomous organelles, which are pivotal for the energy metabolism of eukaryotic cells. These organelles contain their own genetic system (mtDNA, mitogenome) that encodes to some structural RNAs and organellar proteins. The biogenesis and function of mitochondria is tightly regulated by nuclear-encoded protein cofactors. The mtDNAs of land plants are characteristically large, with a complex mode of gene organization and expression, in particularly at the post-transcriptional level. To become functional, the primary organellar transcripts undergo extensive maturation steps, which include endo- and exo-nucleolytic cleavage, RNA-base editing, and both ‘cis’- and ‘trans’-splicing events. These essential processing steps rely on the activities of a large set of nuclear-encoded factors. Among these, the RNA helicases serve as key players in organellar (mt-)RNA metabolism. This review summarizes the important roles played by these factors in regulating the highly dynamic processes of transcription, RNA processing, and translation within the mitochondria of land plants. We further discuss recent advancements in understanding how dysregulation of mitochondrial RNA helicases affects the organellar gene expression, respiratory functions, and hence the growth, development and physiology of land plants.

Salinity is one of the major factors responsible for the deterioration of soils, making them unsuitable for agriculture. The application of halotolerant and halophilic plant growth promoting bacteria (PGPR: Plant Growth Promoting Rhizobacteria) with biological control activities as an inoculant of cultivated plants offers a biological alternative to the use of agrochemicals, particularly when subjected to salt stress. In this perspective, 70 bacterial strains were isolated from saline soils (sebkha) in arid and semi-arid areas of eastern Algeria. Three isolates were selected based on their ability to produce bioactive molecules allowing them to promote plant growth, such as hydrolytic enzymes, indole acetic acid, HCN, NH3, etc. Two of these isolates belong to the genus Serratia and the third is a halophilic Halomonas bacteria. These bacteria were identified based on the 16S rDNA sequence. Antagonism tests against phytopathogenic fungi were carried out. The identification of the antifungal molecules produced by these bacteria was done by high performance liquid chromatography. These bacteria are capable of inhibiting mycelial development against phytopathogenic fungi with rates reaching 80.67% against Botrytis cinerea, 76.22% against Aspergillus niger and 66.67% against Fusarium culmorum for Serratia sp. The strain Halomonas sp. inhibited mycelial growth by production of volatile substances of Aspergillus niger at 71.29%, Aspergillus flavus at 75.49% and Penicillium glabrum at a rate of 72.22%. The identification by HPLC of the antifungal molecules produced by these three bacteria reveals that they are polyphenols, which makes these strains the first rhizobacteria capable of producing phenolic compounds. Finally, pot tests to determine the effectiveness of these strains in promoting wheat growth under salinity stress (125 mM, 150 mM and 200 mM) were carried out. The results revealed that a consortium of two isolates (Serratia sp. and Halomonas sp.) performed best at 125 mM. But, at higher concentrations it was the halophilic bacteria Halomonas sp which gave the best result. In all cases, there was a significant improvement in the growth of wheat seedlings inoculated with the bacteria, compared to non-inoculated controls.

The increased cultivation of high THC-containing Cannabis sativa L. (cannabis), particularly in greenhouses, has resulted in a greater incidence of diseases and molds that can negatively affect the growth and quality of the crop. Among them, the most important diseases are root rots (Fusarium and Pythium spp.), Botrytis bud rot (Botrytis cinerea), powdery mildew (Golovinomyces ambrosiae), cannabis stunt disease (caused by Hop latent viroid), and a range of microbes that reduce post-harvest quality. An integrated management approach to reduce the impact of these diseases/microbes requires combining different approaches that target the reproduction, spread and survival of the associated pathogens, many of which can occur on the same plant simultaneously. These approaches will be discussed in the context of developing an integrated plan to manage the important pathogens of greenhouse-grown cannabis at different stages of plant development. These stages include maintenance of stock plants, propagation through cuttings, vegetative growth of plants, and flowering. The cultivation of cannabis genotypes with tolerance or resistance to various pathogens is a very important approach, followed by the maintenance of pathogen-free stock plants. When combined with cultural approaches (sanitation, management of irrigation, and monitoring for diseases) and environmental approaches (greenhouse climate modification), a significant reduction in pathogen development and spread can be achieved. The use of preventive applications of microbial biological control agents and reduced risk biorational products can also reduce disease development at all stages of production in jurisdictions where they are registered for use. The combined use of promising strategies for integrated disease management on cannabis plants during greenhouse production will be reviewed. Future areas for research are identified.

Fruit cracking or splitting, a severe physiological disease that highly affects appearance and quality, compromise the commodity value of fruit and also causes huge economical losses to the producers of several fleshy fruit crops. The growth-promoting phytohormone gibberellic acid (GA) and growth-inhibiting phytohormone abscisic acid (ABA) antagonistically regulate numerous developmental processes throughout the plant life cycle. Homeostasis of endogenous GA and ABA play a significant role in the normal growth of fruits, and its imbalance may lead to the occurrence of cracking or splitting during the process of fruit growth, development, ripening and postharvest storage. The pathways of GA and ABA metabolism and signaling have been studied widely, and the major components were well characterized, which including the genes encoding major biosynthesis and catabolism enzymes and the key signaling components. Nevertheless, our knowledge of the mechanisms of GA and ABA governing fruit cracking is not comprehensive enough. In this review, we not only focus on the scenarios of effects of endogenous GA and ABA contents in the fruit and exogenous plant growth regulators GA and ABA treatments, but also endeavor to provide some genetic cues on the function of GA and ABA responsible for fruit cracking modulation. Information from this review aims to strengthen the understanding of potential molecular bases underlying GA and ABA in fruit cracking control and provide guidance in breeding of fruit cultivars with cracking-resistant ideotypes, and also make great theoretical significance in guiding the establishment of integrated prevention and control measures to fruit cracking or splitting.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

To investigate the effects of plant polysaccharides combined with boric acid on digestive function, immune function, and harmful gas and heavy metal contents in faeces of finishing pigs, for this study, 90 healthy three-way cross-bred pigs were selected and randomly divided into five groups: Con) A control, fed basic feed; BA) fed basic feed supplemented with 40 mg/kg of boric acid; BA+APS) fed basic feed supplemented with 40 mg/kg of boric acid and 400 mg/kg of Astragalus polysaccharides; BA+GLP) fed basic feed supplemented with 40 mg/kg of boric acid with 200 mg/kg of Ganoderma lucidum polysaccharides; and BA+EPS) fed basic feed supplemented with 40 mg/kg of boric acid and 500 mg/kg of Echinacea polysaccharides. Compared with the Con, the average daily gain (ADG) and trypsin activity were higher in BA, along with there being increased glutathione peroxidase (p < 0.05); furthermore, lower malondialdehyde content and harmful substance emissions from manure were observed (p < 0.05). Compared with BA, increases in ADG, gain-to-feed ratio (G/F), trypsin, and maltase activities were observed in BA+EPS (p < 0.05), as well as a lower NH3 emissions (p < 0.05). Compared with BA, BA+APS exhibited higher ADG, average daily feed intake (ADFI), G/F, trypsin, and maltase activities (p < 0.05), while H2S emissions were lower (p < 0.05). And Taken together, our findings reveal that supplementation with boric acid and plant polysaccharides in the diet of finishing pigs synergistically improved their growth performance, trypsin and maltase activities, and immune function, while also discovered the intestinal digestive enzymes, serum inflammatory factors, oxidative stress responseand emissions of harmful gases and heavy metals from feces and urine were tightly related, further reducing the impact of fecal pollution generated by modern animal husbandry on the environment.

This review compiles research findings pertaining to Nuclear Distribution C (NudC) genes and their involvement in various cellular processes, including nuclear migration, intracellular transport, nuclear positioning and cell cycle progression. Structural and functional similarities of NudC pro-teins across diverse organisms highlight their evolutionary conserved nature and pivotal roles in upholding cellular functions. Special attention is given to the growing importance of NudC family members in plants, elucidating their contributions to plant growth, development and responses to environmental challenges. Within this framework, we spotlight two Arabidopsis NudC genes, BOB1 and NMig1, for their indispensable roles in embryo and postembryonic development, and involvement in stress responses and tolerance mechanisms. Furthermore, we accentuate the chaperone activity of plant NudC family members, a crucial function in mitigating the adverse effects of stress and bolstering plant resilience in the face of environmental challenges. These insights underscore the promising potential of NudC members as valuable targets for enhancing crop performance, particularly when directed towards optimizing plant morphology and fortifying en-vironmental resistance to enhance overall crop productivity.

Cut chrysanthemum, renowned as a highly favored floral choice globally, experiences a significant decline in production due to the practice of continuous cropping. The adverse physiological effects on cut chrysanthemums have been observed as a result of the degradation of soil physical and chemical properties, coupled with the proliferation of pathogens. It is noteworthy that the 'Guangyu' cultivar in Xinxiang, Henan province, China, has been specifically impacted by these effects. This study effectively identified and validated the precise pathogen accountable for wilt disease initially. Subsequently, an analysis was conducted to examine the invasion pattern of the pathogen and the physiological response of the chrysanthemum. Finally, PacBio platform was employed to investigate the dynamic alterations in the microbial community within the soil rhizosphere, comparing the effects of seven years of monocropping with the first year. The findings indicated that Fusarium solani was the primary causative agent responsible for wilt disease, as it possessed the ability to invade and establish colonies in plant roots, leading to alterations in various physiological parameters of the plants. Furthermore, the practice of continuous cropping had been observed to significantly disturb the microbial community composition, potentially acting as an additional influential factor in the advancement of wilt.

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

The results of the most recent investigation of triterpenoid-based antiviral agents effective namely in the HIV-1 and HSV-1 treatment were reviewed and summarized. Several key historical achievements are included to stress consequences and continuity in this research. Most of the agents studied belong into a series of compound derived from betulin or betulinic acid, and their synthetic derivative called bevirimat. A termination of clinical trials of bevirimat in the Phase IIb initiated a search for more successful compounds partly derived from bevirimat or designed independently of bevirimat structure. Surprisingly, a majority of bevirimat mimics are derivatives of betulinic acid, while other plant triterpenoids, such as ursolic acid, oleanolic acid, glycyrrhetinic acid or other miscellaneous triterpenoids, are relatively rarely involved in a search for novel antiviral agent. Therefore, this review article is divided into three parts based on the leading triterpenoid core structure.

Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

The intricacies of translational strategies make this stage of the implementation of genetic information one of the most difficult to understand and, at the same time, perhaps the most interesting. It also becomes clear that all this variety of strategies is not just a consequence of a long evolutionary history but is of paramount importance for fine-tuning gene expression and metabolic modulation. This is especially true for those organisms that demonstrate predominantly biochemical and physiological reactions with a lack of behavioural ones. Plants are just such a group of organisms. Overcoming unfavourable environmental conditions occupies a key place in plant physiology. This is especially true with the changing conditions of global warming and the irretrievable loss or depletion of natural ecosystems. Conceptually, the response of plants to abiotic stress is a set of complex and intricate strategies. This is dictated both by a variety of abiotic factors that provoke stressful conditions and by a variety of molecular genetic mechanisms that make it possible to fine-tune the metabolic pathways through which the plant organism overcomes non-standard and non-optimal conditions. In the presented review, we tried to focus on the state-of-the-art in the field of translational regulation in plants under abiotic stress conditions. Various regulatory elements and patterns are considered in relative chronological order. We also considered it necessary to pay attention to key high-performance methods for studying the dynamics of genetic information at the stage of translation.

Human disturbance, such as trampling, is an integral component of global change, yet we lack a comprehensive understanding of its effects on alpine ecosystems. Many alpine systems are seeing a rapid increase in recreation, and in understudied regions, such as the Coast Mountains of British Columbia, disturbance impacts on alpine plants remain unclear. We surveyed disturbed (trail-side) and undisturbed (off-trail) transects along elevational gradients of popular hiking trails in the T’aḵ’t’aḵ’múy̓in tl’a In̓inyáx̱a7n region (Garibaldi Provincial Park), Canada, focusing on dominant shrubs (Phyllodoce empetriformis, Cassiope mertensiana, Vaccinium ovalifolium) and graminoids (Carex spp). We used a hierarchical Bayesian framework to test for disturbance by elevation effects on total plant percent cover, maximum plant height and diameter (growth proxies), and buds, flowers, and fruits (reproduction proxies). We found that trampling reduces plant cover and impacts all species, but that effects vary by species and trait, and disturbance effects only vary with elevation for one species’ trait. Growth traits are more sensitive to trampling than reproductive traits, which may lead to differential impacts on population persistence and species level fitness outcomes. Our study highlights that disturbance responses are species-specific, and this knowledge can help land managers minimize disturbance impacts on sensitive vegetation types.

Colon cancer is a common malignancy of digestive tract and has high incidence and mortality worldwide. As a normal flora in the human gastrointestinal tract, lactic acid bacteria have been proved to have health benefits, such as immune-regulation, anti-infection, and anti-tumor. In the present study, the inhibitory effects of 19 heat-inactivated plant-derived lactic acid bacteria on human colon cancer cell HCT116 were investigated. The results showed that heat-inactivated plant-derived lactic acid bacteria strain 729 had the strongest inhibitory effect on HCT116 cells among all tested bacteria. Treatment with heat-inactivated bacteria strain 729 could inhibit the proliferation of HCT-116 cells in dose-dependent manner, with 54.52% inhibition at a final concentration of 1×10⁸ CFU/mL. Moreover, the heat-inactivated bacteria strain 729 could block the cell cycle, induce apoptosis, and promote cellular ferroptosis by downregulating the expression of GPX4 and xCT at protein level. According to the 16S rDNA sequencing, the plant-derived lactic acid bacteria strain 729 was Lactobacillus plantarum. Collectively, the results indicates that lactic acid bacteria strain 729 may be developed as special ingredients targeting colon cancer, but further in vivo studies are required.

The fall armyworm (FAW), Spodoptera frugiperda, has become one of the most important pests on corn in China since it invaded in 2019. Although FAW has not been reported to cause widespread damage to rice plants in China, it has been sporadically found feeding in the field. If FAW infests rice in China, the fitness of other insect pests on rice may be influenced. However, how FAW and other insect pests on rice interact remains unknown. In this study, we found that the infestation of FAW larvae on rice plants prolonged the developmental duration of the brown planthopper (BPH, Nilaparvata lugens (Stål)) eggs, and plants damaged by gravid BPH females did not induce defenses that influenced the growth of FAW larvae. Moreover, co-infestation by FAW larvae on rice plants did not influence the attractiveness of volatiles emitted from BPH-infested plants to Anagrus nilaparvatae, an egg parasitoid of rice planthoppers. FAW larvae were able to prey on BPH eggs laid on rice plants and grew faster compared to those larvae that lacked available eggs. Studies revealed that the delay in development of BPH eggs on FAW-infested plants was probably related to the increase in levels of jasmonoyl-isoleucine, abscisic acid and the defensive compounds in the rice leaf sheaths on which BPH eggs were laid. These findings indicate that if FAW invades rice plants in China, the population density of BPH may be decreased by intraguild predation and induced plant defenses, whereas the population density of FAW may be increased.

This work is the first scientific study on the development of the composition and technology of a new antioxidant agent based on a combination of extracts of tree medicinal plants and the study of the technological parameters of a dry extract. Further draw up technological regulations for their production and their introduction into production. This study aims to the antioxidant activity of dry extract "Antioxim" was determined. In order to create this herbal collection of plant, first of all, to study the technological parameters of raw materials, such as mass size, porosity, dispersibility, free volume of layers of raw materials, water absorption coefficient of extractives, and technological process efficiency to study the effect of parameters, developed at the Department of "Industrial Technology of Medicines" of the Tashkent Pharmaceutical Institute:

Maize (Zea mays) is one of the most important staple food and primary source of livestock feed in the world. As the consumption of maize grown on the selenium-enriched soils of Naore Valley is one of the apparent causes of selenosis in the area, this work collected and analyzed total Se, Se fractions, and Se species distributions in maize plant samples, including grains, leaves, stalks, roots, rhizosphere soils, and the most representative parent rock materials from Naore Valley, Ziyang County, China. The Se distribution in soils markedly correlated with the weathered Se-enriched bedrocks, but most of the Se in the analyzed soils is enclosed as recalcitrant residual Se and organic-sulfide bound Se. In contrast, Se in rocks had a comparatively higher bioavailability and is bounded mainly to organic matter and sulfides minerals, with very few of the Se enclosed in the residual fraction. Maize plants might take a large amount of Se from the organic-sulfide bound Se fraction in the Se-rich soils, the weathered products from bedrocks or plant litters. Total Se concentrations in the collected samples were observed in descending order soil>leaf>root>grain>stalk. The predominant Se species detected in maize plants was SeMet. Se inorganic forms, mainly Se(VI), decreased from root to grain and were possibly assimilated into organic forms. Se (IV) was barely present. The natural increases in Se concentration affected mainly leaf and root dry-weight biomass as they are the organs that coped with the highest Se accumulation. This paper offers an insight into the uptake, accumulation, and distribution of Se forms in natural Se-rich maize crops and an opportunity for shifting Se-rich soils from menaces to valuable resources for growing Se-rich agricultural products.

Entomopathogenic fungi perform important functions in the ecosystem as natural antagonists of insects, which can be used in agriculture. Interestingly, recent studies showed a significant promotion of tested plants growth in the presence of fungi. We hypothesize that some of various compounds produced by entomopathogenic fungi can positively affect plant development. To test this hypothesis, fungal extracts of entomopathogenic fungus Beauveria bassiana were prepared at different conditions of temperature and pH. In addition to determination of the ammonium nitrogen content, the composition of extracts was analyzed by elemental ICP-OES. Then, their effect on the wheat germ growth was studied using various extract concentrations. After experiments, tested plants were measured, weighed, and the chlorophyll content was determined. Finally, the impact of extracts on the selected G+ and G- bacteria growth was examined to exclude the possibility of interference with soil microorganisms. The highest length of the wheat shoot was obtained for the use of 10-times diluted extract (10%) at pH 10 obtained at 20°C. In contrast, addition of 10% extract (pH 10) obtained at 75°C resulted in the shortest shoot. Generally, the extracts obtained at 75°C showed phytotoxic properties leading to lower values of shoot length and fresh weight in comparison to the control group. Our preliminary results are the first confirming the potential of fungal water extracts as factors promoting plant growth. Further detailed study should be carried out to confirm the effects in real environment conditions. Also, the consistency of the plant growth stimulation across different entomopathogenic fungi, and agriculturally used plant species should be tested.

Background: Drought, N deficiency and herbivory are considered the most important stressors caused by climate change in the agro- and eco-systems and varied in space and time shaping a highly dynamic and heterogeneous stressful environments. This study aims to evaluate the to-mato morpho-physiological and metabolic responses to combined abiotic and herbivory at dif-ferent within-plant spatial levels and temporal scales. Methods: Leaf-level morphological, gas exchange traits and VOC profiles were measured in to-mato plants exposed to N deficiency and drought, T. absoluta larvae and their combination. Ad-ditive, synergistic or antagonistic effects of the single stress when combined were also evaluat-ed. Morpho-physiological traits and VOC profile were also measured on leaves located at three different positions along the shoot axes. Results: The combination of the abiotic and biotic stress has been more harmful than single stress with antagonistic and synergistic but non-additive effects for the morpho-physiological and VOC tomato responses, respectively. Combined stress also determined a high within-plant phenotypic plasticity of the morpho-physiological responses. Conclusions: These results suggest that the combined stress in tomato determined a “new stress state” and a higher within-plant phenotypic plasticity which could permit an efficient use of the growth and defence resources in the heterogeneous and multiple stressful environmental condi-tions.

The article is devoted to solving the problem of searching for universal explainable features that can remain explainable for a wide class of objects and phenomena and become an integral part of Explainable AI (XAI). The study is implemented on the example of an applied problem of early diagnostics of plant stress, using Thermal IR (TIR) and HSI, presented by 8 vegetation indices/channels. Each such index was presented by 5 statistical values. A Single-Layer-Perceptron classifier was used as the main instrument. TIR turned out to be the best of the indices in terms of efficiency in the field and sufficient to detect all 7 key days with 100% accuracy. Our study shows also that there are a number of indices, inluding NDVI, and usual color channels Red, Green, Blue, which are close to TIR possibilities in early plant stress detection with 100% accurasy or near, and can be used for wide class of plants and in different conditions their treatment. The stability of the stress classification in our study was maintained when the training set was reduced up to 10% of the dataset volume. The entropy-like feature of (max-min) for any indices/channels have determined as a leadersheep universal high-level explainable feature for the plant stress detection, which used in interaction with some of other statistical features.

Developing countries are confronted with general issues of municipal wastewater management and treatment. Untreated faecal sludges and wastewaters from septic tanks and traditional toilet are rejected into rivers and sometimes using for urban agriculture without any treatment to reduce the biorisk. Consequently, there are potential environmental and public health risks. In this study, a wastewater treatment plant prototype coupled with Moringa Oleifera seeds treatment was developed to evaluate their effectiveness for the reduction of faecal indicator bacteria and antibiotics resistant bacteria in domestic wastewater. Results indicate that our performed prototype system presents high capacity to reduce bacteria with abatements up to 99.34%. High reductions of bacteria load were obtained after add of Moringa Oleifera seeds into waters, with reductions varied from 36.6-78.8% for E. coli, 28.3-84.6% for Faecal coliform, 35.3-95.6% for Vibrio cholerae and 32.1-92.4% for total flora. Same effects of Moringa Oleifera seeds were noted for reducing antibiotic resistant bacteria, Extended Spectrum Beta-lactamases and Carbapenem-Resistant Enterobacteriaceae with abatements up to 98% for E. coli and faecal coliform, 100% for Vibrio cholerae and 91.96% for total flora. Our results supported high capacity of Moringa Oleifera seeds as an excellent alternative for pathogens and antibiotics resistant bacteria reduction/purification from domestic wastewater

Insect herbivores have a wide range of life cycles and feeding habits, making them extremely diverse. With their host plants, they form close relationships and suppress their defense mecha-nisms. Molecular elicitors are the key bio-elements in detection and recognition of attacking enemies in tissue consumption. Insect oral secretion, frass, and fluid of egg deposition contain bio-logical active molecules called herbivore-associated elicitors (HAEs) are recognized by pattern recognition receptors (PRRs). However, in insect herbivores, little is known about the molecular basis of signal transduction and regulation of plant resistance. Many plants distinguish insect feeding from wounding by HAEs presenting in their oral secretions (OS) and induce local and systemic responses against arthropod feeding. PRRs perceive HAEs in the oral secretion of cater-pillars in a species-specific manner to elicit exclusive defense responses. HAEs-PRRs interactions induce plant resistance by reprogramming plant metabolism and transcriptional machinery. Quantitative, timely, and coordinated plant response initiate early signalling events including Ca+2, reactive oxygen species (ROS) and mitogen-activated protein kinases (MAPKs). We have discussed how early signalling cascades converge into the accumulation of phytohormones that regulate down-stream special metabolites against herbivores. In this review, we have drawn a hypothetical model of PPRs-HAEs mediated induced responses in plants and discussed how PRRs-HAEs interactions based on molecular mechanism that elicit short- and long-term induced defenses in plants. The identification of plant target insect herbivore PRRs-HAEs interactions will help to explore the fundamental molecular mechanisms of host manipulation and may generate prospects to develop novel pest resistance strategies.

Two once-through steam generators and a combination of a steam generator and a gas preheater for supplying feed gases to solid-oxide electrolysis stacks were evaluated for their carryover characteristics of contaminants from the feed-water into the steam phase. The concentrations of various trace impurities in the steam were determined by sampling the steam condensates and screening them with inductively coupled plasma - mass spectrometry for 19 elements and liquid ion chromatography and continuous flow analysis for chloride and ammonium. Steam soluble species like boric acid undergo complete volatilization and transfer into the steam phase. During unstable evaporation in the steam generators an extensive physical carryover of alloying metal species was observed. At realistic operation conditions for steam electrolysis, the gas preheater caused a considerable release of silicon into the steam phase. Two stack experiments were performed with common preheater temperatures and showed largely increased cell voltage degradation at higher operation temperatures. The post-test chemical analysis of cell samples revealed significant concentrations of silicon in the samples that are regarded as primary cause for increased degradation. These findings could partially explain the wide spread of degradation rates reported for solid-oxide steam electrolysis experiments.

Plants characterized by a soft or weak steam, such as climbing plants, need to find a potential support (e.g., wooden trunk) to reach greater light exposure. Since Darwin’s research on climbing plants, several studies on their searching and attachment behaviors have demonstrated their unique ability to process different support features to modulate their movements accordingly. Nevertheless, the strategies underlying this ability are yet to be uncovered. The present research tries to fill this gap by investigating how the interaction between above- (i.e., stem, tendril, …) and belowground (i.e., the root system) plant organs influence the kinematics of the approach-to-grasp movement. With three-dimensional (3D) kinematical analysis, we characterized the movement of pea plants (Pisum sativum L.) towards a support with different thicknesses above and belowground (i.e., thin below, thick aboveground, or the opposite). As a control condition, the plants were presented to supports with the same thickness below- and aboveground (i.e., either entirely thin or thick). The results suggest an integration between the information from below- and aboveground for driving the reach-to-grasp behavior of the aerial plant organs. Information about the support conveyed by the root system seems particularly important to fulfil the end-goal of the movement.

In the current scenario of changing climatic conditions and rising global population, there is always a need to explore novel, efficient, and economical natural products for the benefit of human kind. Biosurfactant is one of the latest explored microbial biomolecules that have been used in numerous fields, including agriculture, pharmaceuticals, cosmetics, food processing, and environment-cleaning industries as raw material, lubrication, wetting, foaming softening, making emulsions, and stabilizing dispersions. The amphiphilic nature of biosurfactant biomolecules showed great advantage, distributing themselves into two immiscible surfaces by reducing interfacial surface tension and increasing the solubility of hydrophobic compounds. Furthermore, their eco-friendly nature, low or even no toxic nature, durability at higher temperatures, and wide range of pH fluctuations making the microbial surfactants preferable compared to their chemical counterparts. Additionally, the biosurfactants can obviate the oxidation flow by eliciting the antioxidant property, antimicrobial, anticancer activity, and drug delivery system, further broadening their applicability in the food, pharmaceutical, and pharma industries. Nowadays, biosurfactant has been broadly utilized to improve the soil quality by improving the concentration of trace elements and mixed with pesticides or applied singly on the plant surfaces for plant disease management. In the present review, we summarise the latest aspect of microbial synthesized biosurfactant compounds, limiting factors of biosurfactant production, and their application in improving soil quality, plant disease management.

Biogenic-Silver nanoparticles emerge as new nanosilver platfmorm that allow to obtain silver nanoparticles via “green chemistry”. In our study we obtained biogenic-Silver nanoparticles from Iris tuberosa leaves extract. Nanoparticles were characterized by UV-vis spectroscopy, dinnamical light scattering technique and Transmission electron microscope, showing spheric and irregular nanoparticles with 5 to 50 nm in diameter. Antioxidant and antimicrobial properties were evaluated against typical microbial contaminants found in cosmetic products, showing high antioxidant and antimicrobial properties. We formulated a natural moisturizing cream with biogenic-silver nanoparticles to evaluate the preservative efficiency trought challenge test, confirming that our nanoparticles are a promising alternative to use as cosmetic preservative.

Salvia ceratophylloides (Ard.) is an endemic, rare, threatened plant species recently rediscovered in very few individuals in two different sites of South Italy. The study of within-plant variation more than among-plant one is fundamental to understand the plant adaptation to the local conditions, especially in rare species, and consequently to preserve plant biodiversity. Here, we reported the variation of the morpho-ecophysiological and metabolic traits between the sessile and petiolate leaf of S. ceratophylloides plants in two different sites for understanding the adaptation strategies for surviving in these habitats. The S. ceratophylloides individuals exhibited different net photosynthetic rate, maximum quantum yield, light intensity for the saturation of the photosynthetic machinery, stomatal conductance, transpiration rate, leaf area, fractal dimension and some VOCs between the different leaf types. This within-plant morpho-physiological and metabolic variation was depended on the site. These results provide empirical evidence of sharply within-plant variation of the morpho-physiological traits and VOCs profiles in S. ceratophylloides which could be because of adaptation to the local conditions.