Daylight usage in buildings improves visual comfort and lowers the final energy demand for artificial lighting. The question always occurs: how much conservation can you achieve? New upcoming or rare materials and constructions have a lack of information about their application. Therefore, the current work investigates the daylight performance of a multi-layer textile membrane roof with 2 300 m² on top of a sports hall. A translucent, thermal insulation with a glass fibre fleece between the roof membranes combines daylight usage and heating demand reduction. A sports hall with built year 2017 is selected as the case study building. The optical properties of the roof construction are measured. The (visual) light transmittance amounts to 0.72 % with a clean surface. An accordingly parametrized climate-based annual daylight modeling delivers daylight indicators for different construction scenarios. The results show that in comparison to only one glass facade, the additional translucent and thermally insulated membrane construction increases the annual daylight autonomy700/ continuous DA700 from 0/ 15 % to 1.5/ 38 %. In the roof covered areas of the sport field, this results in a reduction from 19.7 to 13.8 kWhel/m²/a electricity for the artificial lighting with dim control (30 % savings). Also, the influence of soiling on the light transmittance was determined with a relevant reduction of one layer about a factor 0.81. The novel results are of great value as a comparison and benchmark for planners and future buildings of similar type.

Four methods in two different families have been constructed to derive the exact solutions to Benjamin-Bona-Mahony equation in two space dimensions. Simply defined hyperbolic tangent, hyperbolic secant and hyperbolic cosecant ansatzes and the expansion method based on the Sine-Gordon equation in two dimensions are directly substituted into the governing ODE reduced from the two dimensional BBM equation. Classical algebraic method is used to find the relations among the target parameters representing the nonzero coefficients in the predicted solutions and the wave transform parameters. Some complex and real solutions have been constructed in explicit forms.

Background: Evaluation of nutrition programs is essential to guarantee the effectiveness of community-based management of acute malnutrition (CMAM). Methods: The Rainbow Project Supplementary Feeding Programs (SFPs) in Zambia were evaluated between years 2015-17, following implementation of new recommendations based on previous evaluations (years 2012-14). Outcomes of the program were compared with International Standards and with those of 2012-14. Cox proportional risk regression analysis was performed to identify predictors of mortality and defaulting. Results: Data for 900 under age 5 years malnourished children (48.8% male; mean age 19.7months ±9.9) were analyzed. Rainbow 2015-17 program outcomes met International Standards, for general malnutrition or stratified moderate acute malnutrition (MAM) and severe acute malnutrition (SAM). When comparing with 2012-14 outcomes, better performance was noted: mortality rates were reduced by half (5.6% vs 3.1%, p = 0.01; for SAM: 12.4% vs 6.7%, p = 0.006), with significant improvement in average weight gain and mean length of stay (p<0.001), and increased awareness of HIV status (+30%; p < 0.001). HIV infection (5.5; 1.9–15.9), WAZ < −3 at baseline (4.6; 1.3–16.1) and kwashiorkor (3.5; 1.2–9.5) remained the major predictors of mortality. Conclusion: The effectiveness of the Rainbow SFPs for child malnutrition treatment and prevention in Zambia has significantly improved after evaluation and implementation activities, with impressive outcomes which resulted in a 50% reduction in mortality.

This study investigated vegetable farmers’ willingness-to-pay (WTP) for private advisory services in northwestern Ethiopia. Understanding farmers' preferences for modernizing agricultural advisory services is essential for enhancing the productivity and profitability of smallholder farming. The study assessed WTP for improved advisory services using data from 393 farm households in northwestern Ethiopia. Data from discrete choice experiments were analysed using a random parameter logit model. The results revealed the existence of heterogeneity in farmers’ WTP for vegetable advisory services. Household educational status and age influenced preferences for advisory service features. Farmers preferred enhanced services with frequent visits from agricultural experts and hands-on, field assistance over digital short message service (SMS) and voice-based advice. Additionally, they were more willing to invest in services that focused on leafy vegetables than on fruity vegetables or roots and tubers. The results suggest that the push for commercializing agricultural advisory services should consider contextual differences in smallholders’ preferences.

Rapid urbanization has facilitated socioeconomic development but has also exacerbated land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in Xiong’an New Area exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which have emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area.

Requirements elicitation from multiple human sources involves information validation and conflicts resolution to deal with uncertainty inherent in the process. Most requirements analysis methods focus on expressing the requirements and ignore the uncertainty inherent in the process of requirements elicitation. This paper build on the work presented by the author in [1,10], where a method for requirements elicitation from multiple sources was presented. This paper gives an overview of the method and presents detailed case studies. The cases studies were designed to illustrate different aspects of the Source Control Method and to determine its feasibility and its practical utility as a problem investigation and information validation technique.

The increasing urbanization of cities worldwide has driven a parallel rise in resource consumption, waste generation, and environmental degradation. The transition to a circular economy (CE) offers a sustainable alternative, shifting from traditional linear systems of resource use and disposal to regenerative systems that prioritize material reuse, resource efficiency, and ecological restoration. Urban design and planning play an essential role in facilitating this shift by creating the physical and regulatory infrastructure necessary to implement CE principles in cities. This paper explores the role of urban design in promoting circular economy strategies, with a focus on adaptive reuse of buildings, green architecture, and the integration of nature-based solutions (NBS) into urban environments. Through detailed analysis of these strategies and case studies from cities around the world, this paper demonstrates that regenerative design is essential for closing material and resource loops and creating more sustainable, resilient cities.

This manuscript reviews three case studies from Brazil, Panama, and Portugal that illustrate Nature-based Solutions. The Tijuca Forest in Rio de Janeiro is a remarkable story of centuries of forest management that today is recognized as a UNESCO World Heritage Site. The Panama Canal Watershed produces water for Canal operations, electricity generation, and drinking water for half the country´s population. Traditional windmills and weirs near streams in the Alentejo Region, Portugal, have largely been abandoned due to the damming of the Guadiana River. Yet today, weirs are increasingly recognized for their important contribution to water provisioning in this dry region. All have a primary goal related to water provisioning, yet their ecosystem benefits are multiple. The cases offer important lessons for adaptation to climate change, cultural benefits from traditional human activities, and concerns about social equity.

Acute myeloid leukemias (AMLs) are heterogeneous hematologic cancers that occur prevalently in older patients as the result of a complex pathobiology. The clinical outcomes of older patients with AML, often unsuitable for intensive chemotherapy and allogeneic stem cell transplantation, are generally disappointing. However, recent advances in understanding molecular pathogenesis and other biological mechanisms recognized in AML development and progression have changed the treatment approach, especially for this particularly vulnerable category of patients. Indeed, non-intensive biologically tailored approaches, such as combining venetoclax with hypomethylating agents, have changed the therapeutic paradigm in this field. Moreover, other promising compounds and treatment strategies are currently under advanced clinical development. This article delves into the latest biological understanding and therapeutic advances in the clinical management of older AML patients into new conceptual frameworks that progressively transform the clinical practice in this challenging setting.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring SARS-CoV-2 prevalence in a city in Northern Italy from October 2021 to May 2023. Starting from a widely applied deterministic model, this study proposed a variation to take into account population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated with healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred in the considered period, demonstrating the model’s capacity to predict relevant fluctuations in the number of cases. Population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied for comparing virus load values across cities with varying population demographics and sewer network structures, enhancing the comparability of WBE data for effective surveillance and intervention strategies.

The rapid advancement of technology has revolutionized language learning, introducing innovative methods that depart from traditional instructional approaches. This study employs a mixed-methods research design to examine the impact of chatbot-based language learning support, adaptive learning algorithms, and virtual reality language immersion on the language learning proficiency and self-regulated learning skills of English as a Foreign Language (EFL) learners. The research design includes quantitative analysis of language proficiency scores and qualitative exploration of learner experiences with the technological interventions. The theoretical implications of this study are rooted in constructivist and socio-cultural learning theories, which underpin the design and implementation of the technological interventions. The triangulation of qualitative and quantitative data revealed that the participants' positive perceptions of the effectiveness of chatbot-based language learning support were supported by the quantitative results, with the variable "chat-bot based support" demonstrating a substantial mean difference compared to other groups. This convergence of findings reinforces the positive influence of chatbot-based support on language learning outcomes and highlights the importance of integrating theoretical perspectives with empirical evidence to gain a comprehensive understanding of the impact of technological interventions on language learning outcomes for EFL learners. The study's findings provide insights into the potential of these technological interventions to optimize language learning outcomes for EFL learners and promote autonomous learning behaviors.

Nanomaterials based on metal oxides (MexOy) are employed as catalysts and/or sorbents in processes undergoing conditions in supercritical water (scH2O), the “green” reaction medium used to obtain energy relevant products. Their properties are greatly affected by the synthesis method used to produce the active MexOy. Moreover, in order to maintain their performance, it is more practical and cost-effective to use supported MexOy nanoparticles. Within this context, the present work deals with the preparation of carbon-supported ZnO and CuO composites by an innovative method based on impregnation under scH2O conditions. Metal oxides were impregnated on a carbon (C) support using a continuous flow tubular reactor. The results show that impregnation in scH2O is a promising approach for the preparation of ZnO/C and CuO/C composite materials. This one-step synthesis method, in a continuous flow, uses neither seed layer nor mineralizer, and it needs substantially lower preparation times than the conventional impregnation methods.

Usually, the vector control of the induction machine (IM) is done by using cascade control structures with conventional linear PI controllers, the inner loop being designed for current control, and the outer loop for rotor flux and speed control. In this paper, starting from the dq model of the IM with the rotor magnetic flux space vector aligned along the d axis, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling control of the two dq currents, predictive algorithms with constraints are used to control and limit the currents. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a non-singular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation of the homotopy parameter, the model of the plant is variable, in which case the model free algorithms are properly used to control the flux and speed of the IM. The comparative analysis obtained for the cascade structure with conventional PI controllers and advanced algorithms, respectively, shows that the last lead to improve the dynamical performance of the IM.

The loss or dysfunction of skeletal tissue often necessitates surgical intervention, particularly in cases involving trauma, tumors, or abnormal development leading to critical-sized defects. Despite autografts being considered the gold standard for bone grafts, their limitations and complications prompt the exploration of alternative approaches for bone repair and regeneration. Current advancements in bone tissue engineering have led to innovative strategies aiming to regenerate injured bone structures, providing viable alternatives to traditional autografts or allografts. Among these strategies, Hydrogel Microparticles (HMPs) have emerged as promising scaffolds due to their notable characteristics, including high porosity and mechanical tunability. HMPs play an important role in facilitating vasculature formation, mineral deposition, and overall bone tissue regeneration. HMPs, fabricated through various techniques, exhibit versatility in functions such as drug and cell delivery, structural scaffolding, and bioinks for 3D printing. In addition, these microgels can be injected for minimally invasive delivery and can display modular properties with different designs and configurations. This review exclusively focuses on the advancements in HMPs for bone regeneration, delving into synthesis and functionalization techniques while highlighting their diverse applications documented in the literature. Our aim is to shed light on the unique benefits and potential of HMPs in the intricate field of engineering bone tissue.

This article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it can’t get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like), solving a given cognitive problem. DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, it is shown that DBC-1 can solve the following cognitive problems: “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” Smart manufacturing-based systems (digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and, thereby, can enhance its biologicalization.

Resource conflicts represent a significant global challenge in regions abundant with natural re-sources. Modelling the myriad factors driving natural resource-based conflicts (NRBCs), spanning environmental, health, socio-economic, and political dimensions, is a complex endeavor exacer-bated by data scarcity. Furthermore, existing quantitative studies often focus solely on large-scale conflicts. This article introduces a novel algorithm, the Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management (SEFLAME-CM), which integrates the local knowledge of stakeholders into spatial decision-making technologies to support sustainable peace efforts. The results are validated with spatial multi-criteria evaluation (SMCE) using spatial statistics. The Moran’s I scatter plots for the overall conflicts reveal significant values of 0.99 and 0.98 for both the SEFLAME-CM and SMCE, respectively, with significant spatial autocorrelation. While there re-mains room for improvement in enhancing the model's quality, SEFLAME-CM demonstrates its capacity to transparently model complex real-world problems. The findings underscore the im-perative for a holistic approach to addressing environmental degradation, socio-economic, and political drivers of resource conflicts at the community level. Our paper demonstrates the signif-icance of spatial information technologies and knowledge exchange between experts and local stakeholders in effectively managing resource conflicts. These insights should inform national policies and international interventions, ensuring that the complex underlying issues are ad-dressed while prioritizing the knowledge and needs of affected communities.

The diagnosis and management of cow's milk allergy (CMA) is a topic of debate and controversy. We aimed to compare the opinions of expert groups from the Middle East (n=14) and the European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) (n=13). These Expert groups voted on statements developed by the ESPGHAN group and published in a recent position paper. The voting outcome was compared. Overall, there was consensus amongst both groups of experts. Most experts agreed that symptoms of crying, irritability and colic, as single manifestations, do not suggest CMA. They agreed that amino-acid-based formula (AAF) should be reserved for severe cases (e.g. malnutrition and anaphylaxis) and that there is insufficient evidence to recommend a step-down approach. There was no consensus on the statement that a cow’s milk-based extensively hydrolysed formula (eHF) should be the first choice as a diagnostic elimination diet in mild/moderate cases. Although the statements regarding the role of hydrolysed rice formula as a diagnostic and therapeutic elimination diet were accepted, 3/27 disagreed. The votes regarding soy formula highlight the differences in opinion in the role of soy protein in CMA dietary treatment. Generally, soy-based formula is seldom available in the Middle East region. All ESPGHAN experts agreed that there was no added value of probiotics, prebiotics, and synbiotics to the efficacy of elimination diets, whereas 3/14 of the Middle East group thought there was sufficient evidence. Therefore, there is not a consensus on the optimal diagnostic and therapeutic elimination diet for CMA, but the first-choice elimination diet and the stepwise management approach reveal some differences.

Background: Carers (or care partners) of adults in LTC contribute substantially to the health and well-being of their loved ones by providing physical care, emotional support, and companionship. Despite their critical role, little is known about how caregivers have been impacted by the pandemic. The purpose of this study was to explore the lived experiences of caregivers of people living in long-term care (LTC) homes during the initial phases of the COVID-19 pandemic and potential supports and resources needed to improve caregivers’ quality of life.Design: An interpretive descriptive approach that incorporated photovoice method was used. Methods: Using purposive sampling strategy, six family carers in Ontario, Canada were recruited between September and December 2021. Over a four-week period, caregivers took pictures depicting their experience of the pandemic that were shared in a virtual focus group. Visual and text data were analyzed using thematic analysis with an inductive approach.Findings: Caregivers expressed feelings of frustration, confusion, and joy. Emerging themes included: (i) feeling like a “criminal” amidst visitor restrictions and rules; (ii) experiencing uncertainty and disappointment in the quality of care of long-term care homes; (iii) going through burnout; and (iv) focusing on small joys and cherished memories.Conclusions: The combination of visual and textual methods provided unique insight into the mental distress, isolation, and intense emotional burdens experienced by caregivers during the pandemic. Impact: Our findings underscore the need for LTC organizations to work in unison with caregivers to optimize the care of residents and support mental health of caregivers.

Herein, we report on a versatile system composed of two Ni(II)-based coordination compounds: a 3D metal-organic framework of {[Ni(μ-3isoani)2]·DMF}n (1) formula and a 0D monomeric [Ni(3isoani)2(H2O)4] (2) complex (where 3isoani = 3-aminoisonicotinato and DMF = dimethylformamide) which show mutual solvent-induced reversible transformations into each other. These 1 ↔ 2 transformations are triggered by the exposure of one compound to the solvent present in the other, that is, the MOF (1) in water and the monomer (2) in DMF. The process is easily followed by the naked eye because it involves a colour change from green (1)-to-brownish green (2), making the system potentially interesting to act as a sensor of those solvents. Moreover, these compounds present very different magnetic properties since 1 shows field-induced single-molecule magnet (SMM) behaviour, which corresponds to one of the few Ni(II)-based compounds showing this property, whereas 2 behaves as a regular paramagnet, all of which is explained according to a careful study of the magnetic properties by means of experimental direct-current (dc) and alternating-current (ac) measurements as well as complete active space self-consistent field (CASSCF) calculations.

The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

Viral infections can endanger public health by causing serious illness, leading to pandemics and burdening healthcare systems. Moreover, in the situation of a global spread, disruptions occur in every aspect of life including business, education, and social life. Fast and accurate diagnosis of viral infections has huge implications for saving people’s lives, preventing spread of the diseases, and minimizing social and economic damages. In the last decades, polymerase chain reaction (PCR) based techniques have been frequently used to detect viruses in the clinic. However, in a situation where rapid virus detection is the primary measure in preventing the spread, as in the case of ongoing COVID-19 pandemic, disadvantages of PCR, such as long processing times and requirement of sophisticated laboratory instruments, have been faced. Due to the urgent need for accurate techniques for virus detection, biosensor systems involved in many applications in biological detection are being developed for rapid, real-time, and high-throughput detection of viruses. Among various sensing platforms, optical devices are of great interest due to their advantages such as high sensitivity and direct readout. In the current review, usability of sensing techniques depending on optical phenomena, such as fluorescence-based sensors, surface plasmon resonance (SPR), surface enhanced Raman scattering (SERS), optical resonators and interferometry-based platforms, is discussed for virus diagnostics applications. Then, we focus on an interferometric biosensor developed by our group, single-particle interferometric reflectance imaging sensor (SP-IRIS), which has the capability to visualize single nanoparticles, to demonstrate its application for digital virus detection.

The adoption of digital health technologies accelerated during Covid-19, with concerns over the equity of access due to digital exclusion. Using data from a text-based online mental health service for children and young people we explore the impact of the pandemic on service access and presenting concerns and whether differences were observed by sociodemographic characteristics in terms of access (gender, ethnicity and deprivation). We used interrupted time-series models to assess whether there was a change in the level and rate of service use during the Covid-19 pandemic (April 2020-April 2021) compared to pre-pandemic trends (June 2019-March 2020). Routinely collected data from 61221 service users were extracted for observation, those represented half of the service population as only those with consent to share their data were used. The majority of users identified as female (74%) and White (80%), with an age range between 13 and 20 years of age,. There was evidence of a sudden increase (13%) in service access at the start of the pandemic (RR 1.13 95% CI 1.02, 1.25), followed by a reduced rate (from 25% to 21%) of engagement during the pandemic compared to pre-pandemic trends (RR 0.97 95% CI 0.95,0.98). There was a sudden increase in almost all presenting issues apart from physical complaints. There was evidence of a step increase in the number of contacts for Black/African/Caribbean/Black British (38% increase; 95% CI: 1%-90%) and White ethnic groups (14% increase; 95% CI: 2%-27%) ), the sudden increase in service use at the start of the pandemic for the most (58% increase; 95% CI: 1%-247%) and least (47% increase; 95% CI: 6%-204%) deprived areas. During the pandemic, contact rates decreased, and referral sources change at the start. Findings on access and service activity align with other studies observing reduced service utilisation. The lack of differences in deprivation levels and ethnicity at lockdown suggests exploring equity of access to the anonymous service. The study provides unique insights into changes in digital mental health use during Covid-19 in the UK.

In the European Union (EU), cannabidiol products require pre-marketing authorisation under the novel food regulation. Currently, 19 CBD applications are under assessment at the European Food Safety Authority (EFSA). During the initial assessment of the application files, the EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) located several knowledge gaps in their 07 June 2022 statement on safety of cannabidiol as a novel food that need to be addressed before the evaluation of CBD can be concluded. Namely, the effect of CBD on the liver, gastrointestinal tract, endocrine system, nervous system, psychological function, and reproductive system needs to be clarified. Nevertheless, the available literature allows a benchmark dose (BMD)-response modelling of several bioassays, resulting in a BMD lower confidence limit (BMDL) of 20 mg/kg bw/day for liver toxicity in rats. Human data in healthy volunteers found increases in the liver enzymes alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in a study at 4.3 mg/kg bw/day, which was defined by EFSA NDA panel as a lowest observed adverse effect level (LOAEL). The EFSA NDA panel currently concluded that the safety of CBD as a novel food cannot be evaluated, leading to a so-called clock stop of the applications until the applicants provide the required data. Meanwhile, the authors suggest that CBD products still available as food supplements on the EU market despite the lack of authorisation must be considered as “unsafe”. Products exceeding a health-based guidance value of 10 mg/day must be considered as being “unfit for consumption” (Article 14(1) and (2) (b) of Regulation No 178/2002), while the ones in exceedance of the human LOAEL must be considered “injurious to health” (Article 14(1) and (2) (a) of Regulation No 178/2002).

The present review is dedicated to using of peat on the example of fen peat in high added-value products and applications. Mainly, but not limited, last decade literature was analysed from the perspective of innovation potential of peat applications in environmental technologies on examples of studies from the Baltic Sea region. Paper covers a wide range of applications of peat products started from agriculture to medicine and cosmetics. A separate chapter is devoted to the deep-processed peat product - humic substances (humate, humic and fulvic salts and acids). Generalised dependence of product extraction rate and its properties, depending on the process parameters are provided. Widely are described applications of the humic sub-stance applications covering economic aspects, such as production costs. In the last chapter of the review are given recommendations for applications considering the most recent trends of peat-based product developments. This accumulated information could be useful for future production, development of new, environmentally friendly products and start-ups establishment.

Most oral squamous cell carcinomas (OSCCs) arise from oral epithelial dysplasia; however, there is no useful marker for early OSCC detection, likely owing to the inability to continuously observe the carcinoma sequence. We aimed to establish an experimental model to observe changes in the sequential expression pattern of mRNA and protein in the same rat using liquid-based cytology techniques. Cytology specimens were collected from a 4-nitroquinoline 1-oxide-induced rat tongue cancer model at 2, 5, 8, 11, 14, 17, and 21 weeks. We examined candidate biomarker expression using immunocytochemistry and quantitative real-time PCR. The percentage of positively stained nuclei was calculated as the labeling index (LI). All rats had OSCC of the tongue at 21 weeks. Brd4 (Brd4), Myc (c-Myc), and Tp53 (p53) mRNA levels were upregulated during progression from negative for intraepithelial lesion or malignancy to SCC. Brd4- and c-Myc-LI were increased in low- and high-grade squamous intraepithelial lesions and SCC specimens. p53-LI was significantly increased in SCC specimens. Our experimental model allowed the observation of sequential morphological changes and mRNA and protein expression patterns in the same rat during carcinogenesis. By reducing the false negative rate, BRD4 and c-Myc can be useful markers for the early detection of OSCC.

Abstract: The promotion of sustainable packaging is part of the European Green Deal and plays a key role in the EU’s social and political strategy. One option is the use of renewable resources and biomass waste as raw materials for polymer production. Lignocellulose biomass from annual and perennial industrial crops and agricultural residues are a major source of polysaccharides, proteins, and lignin, and can also be used to obtain plant-based extracts and essential oils. Therefore, these biomasses are considered as potential substitute for fossil-based resources. Here, the status quo of bio-based polymers is discussed and evaluated in terms of properties related to packaging applications such as gas and water vapor permeability as well as mechanical properties. So far, their practical use is still restricted due to lower performance in fundamental packaging functions that directly influence food quality and safety, the length of shelf life and thus the amount of food waste. Besides bio-based polymers, this review focuses on plant extracts as active packaging agents. Incorporating extracts of herbs, flowers, trees, and their fruits is inevitable to achieve desired material properties that are capable to prolong the food shelf life. Finally, the adoption potential of packaging based on polymers from renewable resources is discussed from a bioeconomy perspective.

Researchers have developed and used several three-dimensional (3D) culture systems, including spheroids, organoids, and tumoroids. Drug resistance is a crucial issue involving recurrence in cancer patients. Many studies on anticancer drugs have been done in 2D culture systems, where-as 3D cultured tumoroids have many advantages for assessing drug sensitivity and resistance. Here, we aim to investigate whether Cisplatin (a DNA crosslinker), Imatinib (a multiple tyro-sine kinase inhibitor), and 5-Fluorouracil (5-FU: an antimetabolite) alter tumoroid growth of metastatic colorectal cancer (mCRC). To establish a liquid-based 3D multiplexing reporter assay system, LuM1 (a murine mCRC cell line) was stably transfected with the Mmp9 promoter-driven ZsGreen reporter gene, which was designated as LuM1/m9 cells and cultured in NanoCulture Plate (NCP), a 3D culture device. The larger tumoroids were not sensitive to Cisplatin and ex-pressed ABCG2 (a marker of cancer stem cells, a.k.a. a drug efflux transporter), whereas smaller cell-aggregates were more sensitive to Cisplatin. Both Imatinib and Cisplatin significantly in-creased tumoroid growth (larger than 300 μm2) and Mmp9 promoter activity and were not cytotoxic to the mCRC tumoroids. On the other hand, 5-FU was cytotoxic to the tumoroids and significantly inhibited tumoroid growth, although not completely. Thus, platinum resistance and imatinib resistance in mCRC were modeled using the liquid-based 3D cultured tumoroid system. The tumoroid culture is useful and easily accessible for the assessment of drug sensitivity and resistance.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Large lithium-ion batteries (LIBs) in electric vehicles and energy storage systems demonstrate different performance and lifetime compared to small LIB cells, owing to the size effects generated by the electrical configuration and property imbalance. However, the calculation time for performing life predictions with three-dimensional (3D) cell models is undesirably long. In this paper, a lumped cell model with equivalent resistances (LER cell model) is proposed as a reduced order model of the 3D cell model, which enables accurate and fast life predictions of large LIBs. The developed LER cell model is validated via the comparisons with results of the 3D cell models by simulating a 20-Ah commercial pouch cell (NCM/graphite) and the experimental values. In addition, the LER cell models are applied to different cell types and sizes, such as a 20-Ah cylindrical cell and a 60-Ah pouch cell.

This article shows the numerical results and the analysis of households' degree of knowledge in an intermediary city such as Huelva (Andalusia, Spain) about the sustainable use of urban water. It analyzes the needs and values regarding water and the attitudes that households maintain regarding the acceptance of reclaimed water and the use of new technologies to achieve more efficient and sustainable consumption. These results are part of the stages of needfinding and synthesis of Design Thinking methodology, adopted as a framework to improve the efficiency and sustainability of urban water among households in this city. Different statistical analysis techniques of surveys sent to households and the use of clustering are the mathematical tools used to draw conclusions and recommendations that allow the design of a web-based prototype grounded on Product Service Systems methodology, as a tool to improve the engagement of households concerning water and align citizens with the sustainability of their city. Strategies of customization and technological facilitators will be the means to improve the hydrosocial contract among households in Huelva in future later stages of the project.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

This paper proposes a Case-Based Reasoning (CBR) system to contribute to reinforce the sustainable performance of an environmental management system. The CBR system aims to support the decision-making process to select environmental management actions aimed at reducing risky trends of the environmental state of a region. The CBR system takes advantage of a set of situation-solution pairs called cases, which are stored in a memory and then retrieved as candidates to solve new problems. Situations in this work are represented by a set of risky trends of the following key environmental variables: CO2 emissions, Air-Quality, Loss of Vegetation Cover, Water Availability, and Solid Waste, whose combination damage the environmental state quality of a region. Meanwhile, solutions are represented by a set of environmental management actions. Similar situations to a given current situation are retrieved from the memory of cases and then their solutions are combined, through an adaptation mechanism, until the solution of the current problem is obtained. We used risky trends derived from real data related to the environmental states of a Mexican region to test the proposed CBR system. The results obtained provided insights into the potential of CBR systems to support the decision-making process to select environmental management actions aimed at reducing risky trends of current environmental states.

The paper is aimed to develop an improved acoustic-based Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) techniques, which provide the waves directivity emitting by the angle-beam wedge actuators in the thin-walled structures made of plastic materials and polymeric composites. Our investigation includes the dispersive analysis of the waves that can be excited in the studied plastic panel. Its results allowed to find two kinds of the generated acoustic waves - anti-symmetric Lamb waves A0 and shear horizontally polarized SH waves SS0. The bounds of the chosen frequency range for the experimental and numerical studies were accepted as a compromise between the desire to obtain high defects resolution by generating short waves, their adjustable directivity and maximum propagation length. The finite element model for the transducer was built by using the results of actuator structure experimental study. The frequency response functions for the actuator current and oscillation amplitude of the footprint surface demonstrated good agreement. The found eigenfrequencies of actuator's structure were used for the numerical and experimental study of the Lamb and SH wave generation and propagation in a thin-walled plastic panel. Our results convincingly demonstrated the satisfactory directivity of the actuated waves at their excitation on the frequencies that corresponded to the natural modes of the actuator oscillation. The authors assume that an efficient use of the proposed technique for other analyzed quasi-isotropic materials and applied actuators can be provided by a preliminary research using the similar approach and methods presented in this article.

Structure-based design and synthesis of two biphenyl-N-acyl-β-D-glucopyranosylamine derivatives as well as their assessment as inhibitors of human liver glycogen phosphorylase (hlGPa, a pharmaceutical target for type 2 diabetes) is presented. X-ray crystallography revealed the importance of structural water molecules and that the inhibitory efficacy correlates with the degree of disturbance caused by the inhibitor binding to a loop crucial for the catalytic mechanism. The in silico derived models of the binding mode generated during the design process corresponded very well with the crystallographic data.

Several depth image based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility, and vulnerability to signal or geometric distortion. This paper proposes a template based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.

In our study we investigated the additive manufacturing (AM) of ceramic-based Functionally Graded Materials (FGM) by the direct AM technology Thermoplastic 3D-Printing (T3DP). Zirconia components with a varying microstructure were additively manufactured by using thermoplastic suspensions with different contents of pore forming agents (PFA) and were co-sintered defect-free. Different materials were investigated concerning their suitability as PFA for the T3DP process. Different zirconia-based suspensions were prepared and used for AM of single- and multi-material test components. All samples were sintered defect-free and in the end we could realize a brick wall-like component consisting of dense (<1% porosity) and porous (approx. 5% porosity) zirconia areas to combine different properties in one component. The T3DP opens the door to AM of further ceramic-based 4D-components like multi-color or multi-material, especially multi-functional components.

Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web- or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients’ vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options

Context: The impact of forced migration on mental health is far reaching and compounded by post- migration contexts in which social determinants of health, such as resettlement, employment and gender, play a key role. Research on refugee men has shown that employment and life transitions are key determinants of mental health. However, a gap in knowledge exists on refugee men’s perspectives on the factors that impact their mental health. Methodology: This study used community-based participatory action research and the arts-based method of Photovoice to understand Syrian refugee men’s (n = 11) experiences of forced migration, resettlement, and employment in a Canadian context, as well as the impacts of these experiences on their mental health. Analysis and Results: Drawing on the critical theoretical perspective of intersectionality, we analyzed photographs taken by research participants, which showed that language and literacy barriers, time and stage of life, isolation and loneliness, belonging and identity and gender-based stress intersected to shape their mental health. Conclusion: Meaningful employment was central to the men’s identities; however, they engaged in low wage, precarious work due to both discriminatory policies that favored Canadian experience and credentialing assessment processes that devalued their experience and knowledge. Adopting gender-responsive and caring policies and practices could shift dominant discourses on masculinity and support the mental health of refugee men in resettlement contexts.

Outlier detection is a critical technique across various domains, including statistics, data science, machine learning, and finance. Outliers, data points that differ significantly from the majority, can indicate errors, anomalies, or even new insights. This article provides an in-depth exploration of the primary techniques used to detect outliers, categorized into statistical methods, machine learning-based approaches, and proximity-based methods. We discuss the advantages, limitations, and specific use cases of each technique, highlighting their applicability to different types of datasets. The goal is to equip practitioners with a better understanding of how to identify and handle outliers effectively in real-world data analysis.

Wind-solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out that was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers, which locally perform the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multiphysical systems.

Copper (Cu)-based fungicides are widely used in agriculture, but excessive Cu accumulation in soils threatens crop performance, particularly in perennial fruit crops like apples. This study investigated the effects of varying Cu concentrations on the physiological responses of three apple varieties—Braeburn, Fuji, and Cripps Pink—using a callus-media system. Callus cultures were exposed to Cu concentrations ranging from 0 to 20 mg Cu kg⁻¹ to assess traits such as growth, fresh and dry weight, copper accumulation, and bioconcentration factor (BCF). Results revealed that media Cu concentration had a stronger effect than variety on most traits, especially above 10 mg Cu kg⁻¹, where copper stress reduced genetic variability. At lower Cu concentrations, varietal differences were more apparent, with Cripps Pink showing greater copper tolerance. These findings suggest that high Cu levels suppress varietal differences, leading to a uniform stress response. This study provides valuable insights into copper toxicity in apple varieties and highlights the need to maintain soil Cu levels below critical thresholds to preserve varietal performance, with potential implications for soil-plant systems.

The RSA cryptosystem has long been a fundamental component of contemporary public key in- frastructure. However, recent developments in quantum computing and mathematical theory have introduced signicant challenges to its security. A fully operational quantum computer would allow for the application of Shor's algorithm, enabling the ecient factoring of large integers, thereby com- promising the integrity of RSA and other cryptographic methods dependent on discrete logarithms. Although Grover's algorithm poses a comparatively smaller threat to symmetric encryption systems, it still presents a vulnerability by accelerating key search processes. The looming threat from quan- tum technologies has driven a surge in research aimed at developing quantum-resistant cryptography. These eorts focus on cryptographic techniques grounded in error-correcting codes, lattice structures, and multivariate public key systems, all of which leverage the complexity of NP-hard problemssuch as solving multivariate quadratic equationsto preserve security in a post-quantum world. This pa- per reviews the current progress in quantum-resistant encryption methods, particularly emphasizing the role of robust trapdoor functions. Additionally, it provides a comprehensive analysis of critical multivariate cryptosystem frameworks, such as Matsumoto-Imai, Oil and Vinegar, and Polly Cracker schemes. It also examines advances in lattice-based systems like Kyber and Crystals-Dilithium, which are undergoing assessment by NIST for potential standardization. As quantum computing continues to evolve, the demand for cutting-edge cryptographic solutions to safeguard digital communications grows increasingly pressing.

Following the economic and political transformations after 1991, including the autonomy of the Kurdistan Region and the establishment of Iraq's new federal government in 2003, extensive construction, including mosques, has reshaped the region. This requires examinations of evolving architectural features in new mosques and questioning their alignment with religious and aesthetic design objectives. This research focuses on the transformative impact of modern architectural styles on mosque evolution in Sulaymaniyah Governorate. Acknowledging the interplay of cultural evolution, technology, and artistic innovation, the study employs an in-depth case study approach to analyze selected mosques in Sulaymaniyah Governorate that blend traditional Islamic architecture with modern design principles. This synthesis of traditional and modern elements raises questions about religious legitimacy, historical symbolism, and architecture's role in reflecting and shaping religious values. Using quantitative methods including indicator-based impact assessment, the study identifies modern architectural indicators, determines religious objectives of mosque design through Islamic legitimacy references, and analyzes the case study data. By employing person coefficient correlation, the research aims to identify the causal relationship between modern architectural indicators and achieved design objectives. The results reveal that the overall influence of the modern architecture style tends to be more negative on the evol¬ution of mosques.

(1) Background The SKT is a short cognitive performance test designed to assess impairments in memory and cognitive abilities such as attention and speed of information processing. In 2019, new regression-based norms for the English version of the SKT were calculated applying data from older cognitively healthy adults living in the US, Ireland and Australia (the norming sample) The study has two aims: to establish valid cut-offs for distinguishing between no cognitive impairment, mild cognitive impairment (MCI) and dementia (1), and to cross-validate the new norms for detecting MCI and dementia (2) in a community sample of older adults with clinical diagnoses; (2)Methods: The validation sample included 142 older adults (mean age=87.7, SD=3.55) from the Sydney Memory and Aging Study (MAS Study). Participants were classified as having normal cognition, MCI, or dementia through a consensus diagnosis as well as by the SKT; (3)Results: The sensitivity of the SKT for the differentiation of cognitive impairment (MCI or dementia) from normal cognition was 80.6%. The convergence between the SKT and the consensus diagnoses was 60.1% for all three diagnostic groups. The correlation between the SKT and the Addenbrooke’s Cognitive Examination III (ACE-III), a well validated measure of cognitive ability in older adults, was r = 0.3;. (4) Conclusions: In sum, it can be stated that the SKT is a valid tool for detecting early stages of cognitive impairment, performing very well in discriminating between no cognitive impairment and cognitive impairment (MCI or dementia).

(1) Background: Youths with multiple risks and severe disadvantages experience poorer health and educational outcomes than less disadvantaged peers. To address problems with coping and self-regulation in this group, mental skills training (MST) approaches more commonly used in sport are an emerging intervention approach. (2) Methods and Results: This narrative review synthesizes literature to explain the need for MST, how it works, and evidence to support it works by focusing on two well evaluated programmes: LifeMatters and My Strengths Training for Life™. (3) Conclusions: To support positive youth development, MST is a strengths-based, flexible and adaptable approach to help fill the shortage of available evidence-based programs for those youths facing multiple disadvantages. The findings of this review may facilitate policy makers, commissioners, program planners, and researchers in the uptake of MST or similar psychoeducational approaches in future.

Traditional Romanian food products are an integral part of local culture and spirituality. These food traditions have been formed over the centuries and represent a particularly important part for the development of a circular economy in rural areas. In order to contribute to the development of this sector of activity; we conducted a study on consumer perception of traditional Romanian food products. The purpose of this work was to identify the structure of the studies and the future directions of research related to the image of traditional food products through the bibliometric study as well as the identification of consumer trends of these products through a survey based on the questionnaire. The results obtained indicate that traditional Romanian food products are consumed by the majority of respondents participating in the survey and that this sector of activity has continuity in its development and presents a clear interest among citizens.

European strategic policy directions towards a sustainable blue economy have strengthened interest in maritime investments, thus increasing sectoral competition for marine space. Emerging repercussions out of such a rising interest need to be handled by Marine Spatial Planning (MSP) as a means for: properly allocating marine space to diverse uses; managing conflicts and promoting synergies among them; and pursuing a multi-use perspective of this space. A critical stage of each MSP exercise is the exploration of land-based spatial/developmental directions and their current or potential repercussions in the marine environment in order for constraints but also perspectives in policy choices/maritime uses in the MSP context to be featured; and attainment of a successful coherence or coordination between land and marine spatial/developmental policies to be ensured, by analyzing Land-Sea-Interaction (LSI) and embedding it in the MSP. The latter is the focus of this paper aiming to illuminate critical issues emerging from land-based (terrestrial) policy frameworks for feeding MSP endeavors; and is accomplished in the Northern Aegean Sea, Greece. Towards this end, a multilevel approach is adopted, delving into diverse policy contexts at the local, regional and national/European level and aiming to attain more informed MSP decisions in the specific study region.

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.

This article presents a description of a new method for solving the factorization problem, based on the gradient descent algorithm. This approach demonstrates significant improvements in efficiency compared to traditional methods proposed in previous research. This article describes a new method for solving the factorization problem, based on the gradient descent algorithm, and demonstrates the transition from algebraic methods to approaches based on functional analysis. The proposed approach not only improves the efficiency of solving the problem but also allows the application of computational algorithms of functional analysis, opening new possibilities for research and optimization.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on October 27, 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1 respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerises. The differences between ephemeris DE440 and DE430 are smaller than those between DE440 and DE421.

Droplet-based microfluidics has revolutionized numerous fields such as biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics. This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic On-Chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing, simultaneously. The microsensor, designed with Interdigitated-Ring-Shaped Electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplets length in the range 1.0-6.0 mm, velocity 0.66-2.51 mm/s, droplet volume 1.07nL-113.46nL. Experimental results demonstrated that the microsensor's has a great performance in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance, and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip.

Every firm is devoted to polishing and fine-tuning its maintenance strategy. More exactly, when we develop robust and efficient preventive and conditional maintenance approaches, our objective is to combine performance and robustness to increase maintenance decision-making. This, in turn, leads to a reduction in system failures and a subsequent drop in related expenses. This impulse drives the origin of this essay: to provide fresh criteria that allow us to improve and optimize maintenance techniques. Furthermore, our study has been realistically utilized via the deployment of condition-based maintenance (CBM) techniques, which are commonly regarded as a critical lever that allows firms to gain a competitive advantage in the context of the fourth industrial revolution. Combining both perfect and imperfect maintenance actions into CBM strategies appears to be an effective technique to obtain high economic performance and robustness in the maintenance strategies of the companies. Following this idea, we propose and optimize in this paper a set of criteria enabling the combined assessment of the mean economic performance and the resilience of various kinds of maintenance techniques. The advantage of the proposed criterion is that it adapts to different types of maintenance strategies and provides access to a simple and relevant evaluation model. Thanks to this new criterion, we will be able to choose the better maintenance strategies that have more performance and robustness between these strategies for a system with more or less stable behavior. The Monte Carlo Method is incorporated into the comparative assessment of maintenance methods, adding a layer to the evaluation of the performance and robustness of each adaptation in decision-making.

The paper delineates the developed vision system of the UR5 cobot and the operating algorithm of the robotic quality control station. The hardware-software architecture of the developed robotic station was shown, consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system. Image processing and analysis algorithms were described, the method of control and communication between the station components was discussed, and two operating scenarios were presented as a single robotic station and a robotic line. Based on the results obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated.

Non-coding RNA regions have long been considered the "dark matter" of the genome, with their significance in gene regulation remaining enigmatic. However, recent advances in genomics and molecular biology have illuminated the pivotal role that non-coding RNA regions play in the fine-tuned orchestration of hemoglobin expression. Hemoglobin, the oxygen-carrying protein vital for life, is tightly regulated, and any disruption in its production can lead to severe medical conditions, such as hemoglobinopathies. This review delves into the intricate world of non-coding RNA regions, exploring how they act as master conductors in controlling hemoglobin gene expression. We will discuss various classes of non-coding RNAs, including long non-coding RNAs, microRNAs, and circular RNAs, and their specific mechanisms in fine-tuning hemoglobin levels. Additionally, we will highlight emerging therapeutic approaches that target non-coding RNA regions to ameliorate hemoglobin-related disorders. Understanding the critical role of non-coding RNA regions in hemoglobin regulation opens new avenues for precision medicine and gene therapies, offering hope for those affected by hemoglobinopathies and related conditions. This comprehensive review sheds light on the significance of these non-coding RNA regions, demonstrating that the "silent" players in the genome are, in fact, the conductors of harmony in the symphony of hemoglobin regulation.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

The paper presents an implementation and numerical study of the nonlinear optimal-based vibration control solutions for the full-scale TLP – NREL 5MW wind turbine tower-nacelle model with an MR-damper-based 10 ton tuned vibration absorber (TVA) located at the nacelle, under excessive wave/wind polyperiodic excitations yielding continual transient vibration states. The MR damper operates simultaneously with an electromagnetic force actuator (forming a hybrid TVA), or independently (a semiactive TVA). The study includes both actuators’ nonlinearities and dynamics, whereby the former are embedded in the Hamilton-principle-based nonlinear control solutions. As a reference, the structure model with a passive TVA, with a modified ground-hook control law, and without any vibration reduction solutions is used. The TVA was tuned either to the NREL 5MW tower-nacelle 1st bending mode frequency (TVA-TN) or to the TLP surge frequency (TVA-TLP). The optimal control task, redeveloped here with regard to the TVA stroke amplitude minimisation, including the implementation of the protected structure’s acceleration (control case III / III-H) and relative displacement terms as well as nonzero velocity term in the quality index, is stated and solved, yielding optimal-based control propositions for both the MR damper current and the actuator force. On the basis of the obtained results, the TVA-TN solution is by far superior to the TVA-TLP one regarding the tower deflection amplitude / root-mean-square (RMS) and TVA stroke amplitude values. All the regarded TVA-TN solutions provide tower deflection safety factor of ca. 2, while a structure without any vibration reduction solutions or with a TVA tuned to the TLP surge frequency (especially passive and hybrid TVA-TLPs) are at risk of tower structural failure. The obtained TVA stroke amplitudes are reduced with regard to the previously developed approaches. Moreover, these reductions are obtained due to the sole control algorithm enhancement; thus, no additional resources are necessary, while this attainment is accompanied with a reduction in the MR damper force that is necessary for the TVA operation. The protected structure’s acceleration term utilisation produce the TVA stroke amplitude reduction more efficiently than the TVA relative displacement term usage, as in the previously developed approach (the latter being ineffective for the semiactive TVA implementation) which, in turn, efficiently reduces TVA stroke RMS values and the required mean actuator power for the hybrid TVA implementation. Concerning the limited TVA stroke, the most favourable vibration control solutions are the newly introduced semiactive control case III, providing favourable cumulative primary structure deflection indexes at the lowest obtained TVA travel distance, and possibly the hybrid control case III-H, providing even more favourable primary structure deflection qualities and reduced nacelle acceleration levels thanks to the utilisation of the force actuator of the relatively low power (ca. 6 kW); however, side effect of the latter is the increased TVA stroke amplitude as for all hybrid solutions tested (comparing to semiactive solutions). The analysed passive TVA systems along with the modified ground-hook hybrid TVA solution, utilising the assumed absorber mass, can hardly be implemented in the nacelle, especially along the demanding side-side direction, without enforcing end-stop collision bumpers, which result in the efficiency deterioration. The ground-hook control, well proven for the primary structure steady-state deflection minimisation, yielded unsatisfactory results, comparing to the newly implemented solutions. The results of the current study may be used during the design of the full-scale vibration reduction system for the real-world NREL 5MW-class floating, TLP-based, wind turbine structure.

Intestinal failure (IF) is characterized by a critical reduction of functional gut mass below the minimum needed for optimal growth in children. It requires parenteral nutrition (PN) and home-PN (HPN) which is challenging in terms of meeting nutritional needs according to age, growth velocity, clinical situation, and rapid changes in fluid and electrolyte requirements. Due to these complex requirements, age-adapted multi-chamber bags (MCBs) are important additions to the nutrition armamentarium. The launch of composite fish oil (FO)-containing intravenous lipid emulsions (ILEs) heralded the development of MCBs containing these ILEs in combination with a crystalline amino acids solution (CAAS) adapted for pediatric use. The safety and efficacy of lipid and amino acid components in this context have been widely documented in numerous published studies. This manuscript includes a review of the articles published in PubMed, Embase, and Google Scholar until June 2022, exploring studies in the age groups from term infants to children and adolescents. Preterm infants with their highly specific demands are not included. It aims to offer an overview of the clinical experience regarding the use of a composite FO-based ILE and a specific CAAS developed

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a Specular-Light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the Multi-Scale Normalized Cross Correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Today, numerous studies have shown that the physical environment in hospitals can significantly influence patients' well-being, comfort, and recovery. However, this is currently neglected in hospitals in the Global South. Therefore, there is an urgent need to increase the awareness to make it more applicable worldwide. Thus, this study focuses on improving the healing environment standards, by exploring the impact of evidence-based design and patient-centred care in hospitals for cancer patients; particularly the architectural space quality, on patient health outcomes as well as hospital staff health and wellbeing. In Global North countries, such as the UK, the Achieving Excellence Design Evaluation Toolkit (AEDET), is used by its National Health Services, to assess the effectiveness of various environmental attributes. However, these toolkits have not been designed for and do not work well within Global South countries, such as Northern Cyprus. To examine and compare the effectiveness of different physical environmental attributes and to evaluate user responses, the Post-Occupancy Evaluation Method and the AEDET toolkit have been used in the study. These were applied to both public and private hospitals in Northern Cyprus, involving cancer patients, staff, and professionals (n=220). The findings reveal the strength and weaknesses in terms of environmental comfort based on the aspects of evidence-based design of the hospitals such as natural light, air quality, noise, view, infection control, etc., to create a more optimal physical environment for better psychological outcomes. This is the first study to propose an adaptation of the AEDET toolkit to assist architects in designing healthcare facilities that are responsive to the requirements of hospital patients and staff; and to promote the quality of healing environment for improved health and well-being outcomes.

This hypothesis demonstrates that the efficiency of Loop-Mediated Isothermal Amplification (LAMP) for nucleic acid detection can be positively influenced by preconcentration of microbial cells onto hydrophobic paper surfaces. The mechanism of this model is based on the high attraction of microbes towards hydrophobic surfaces. Extensive studies have demonstrated that hydrophobic surfaces exhibit enhanced bacterial and fungal adhesion. The preconcentration approach by exploiting this inherent affinity of hydrophobic paper substrates enables the adherence of a greater number of target cells, resulting in a higher concentration of target templates for amplification directly from urine samples. In contrast to conventional methods, which often involve complex procedures, this approach offers a simpler, cost-effective, and user-friendly alternative. Moreover, the integration of cell adhesion, LAMP amplification, and signal readout within paper origami-based devices can provide a portable, robust, and highly efficient platform for rapid nucleic acid detection. This innovative hypothesis holds significant potential for point-of-care (POC) diagnostics and field surveillance applications. Further research and development in this field will advance the implementation of this technology, contributing to improved healthcare systems and public health outcomes.

The present article describes a one-pot and cascade mode process using biocompatible/biodegradable reagents, for directly and simply obtaining surfactant compositions of mixtures of D-mannuronic acid and L-guluronic acid directly from oligoalginates or semi-refined alginates (mixtures of alginate, cellulose, hemicellulose, laminaran and fucan). Simple treatments of partial purification of the reaction crudes (elimination of the salts and/or the residual fatty alcohols) or isolation of the surfactant compositions result in derived compounds and in compositions having performance levels appropriate to applications in detergency. In addition, the challenging extension of this cascading one-pot synthesis technology to crude milled brown seaweeds was successfully carried out to provide promising surface-active compositions made up of alkyl uronate and alkyl glycoside monosaccharides.

The aim of the current study was to develop and evaluate the therapeutic potential of apocynin (APO) loaded pH-sensitive nanoparticles (NPs) based transdermal hydrogel for management of rheumatoid arthritis (RA). Slightly modified nanoprecipitation technique was used for preparation of polymeric nanoparticles. Optimization was done through design expert software. Optimized APO-NPs were loaded into carbopol-934 based hydrogel as final dosage form and further studied for physicochemical properties. Optimized APO-NPs formulation had a minimum particle size 63.44 nm, polydisperibility index 0.161, and zeta potential -15mV with a maximum encapsulation efficiency of 89%. In-vitro and ex-vivo studies of APO-NPs based hydrogel was performed at pH 5.5 (pH of normal skin) and 6.8 (pH of inflammed joint) showed a pH-responsive sustained drug release and increased penetration in comparison to free APO based hydrogel. The stability studies of APO-NPs based hydrogel were done to strengthen the potential use of the prepared formulation through transdermal route. Assessment and therapeutic efficacy of the prepared pH-sensitive nanocarriers system was evaluated in chronic inflammatory RA mice model. Parameters associated with chronic inflammation were investigated including behavioral changes and histopathological, and radiological x-rays images of joints of mice paws. In-vivo study depicts improvement in behavioral parameter, decline in synovial hyperplasia and bone structure restoration. In conclusion, APO loaded pH-sensitive NPs based transdermal is a promising carrier system that can effectively manage RA.

Currently, an interactive environmental planning analysis system platform based on " Three Lines One Permit " (TLOP) is being developed to support environmental planning, construction project approval, and the application of TLOP outcome data in Guangzhou. The main objective is to provide governments, businesses and the public with environmental planning analysis tools to determine the site of construction projects. The platform is using the system architecture of the browser and server. Its core functions are interactive environmental planning analysis tool for construction project and the results display tool supporting map viewing. It provides users with a large number of detailed geospatial data and TLOP results data access and environmental planning analysis functions. This article describes the system architecture and implementation of the system platform and has a case study illustrating the system functionality. At present, the platform has been deployed and trial-operated. The content of the analysis framework is constantly expanding. This promotes the matching of environmental planning and analysis with local conditions. This will implement the application of TLOP and improve the efficiency of project construction and the level of ecological environment planning and management.

Functional gastro-intestinal disorders (FGID) including irritable bowel syndrome (IBS) are frequently handled by self-management with over the counter (OTC) products such as hyoscine butylbromide (HBB), alone or in combination with paracetamol, and natural products such as peppermint oil. To obtain real-world information, we have performed an anonymous pharmacy-based patient survey among 1686 users of HBB, HBB + paracetamol and peppermint oil. Based on the distinct but overlapping indications for the three OTC products, multiple logistic regression was applied to compare them in users reporting gastrointestinal cramps and pain, bloating, flatulence, or IBS as cardinal symptom. All three treatments reduced symptoms and associated impairments of work/daily chores, leisure activities, and sleep by approximately 50%. Based on the four cardinal symptoms and the four dependent continuous variables of interest (change of intensity of symptoms and of the three impairment domains) a total of 16 logistic regression models were applied. HBB, HBB + paracetamol, and peppermint oil had similar reported overall effectiveness in those models. Gender, age, baseline symptom severity and impairment in one of three domains had small and inconsistent effects on perceived treatment success. We provide evidence that HBB, HBB + paracetamol, and peppermint oil have comparable effectiveness in their approved indications under real-world conditions in an OTC setting.

The three-armed cluster-randomized trial ARena (Sustainable reduction of antibiotic-induced antimicrobial resistance) aimed to foster appropriate antibiotic use and reduce overprescribing in German ambulatory care to counter antibiotic resistance. Multi-faceted interventions targeted primary care physicians, teams and patients. This study examined effectiveness of the implementation program. ARena was conducted in 14 primary care networks with 196 practices. All arms received data-based feedback on antibiotics prescribing and quality circles. Arms II and III received different add-on components each. Primary outcome examined is the prescribing rate for systemic antibiotics for cases with non-complicated acute infections (upper respiratory tract, bronchitis, sinusitis, tonsillitis, otitis media). Secondary outcomes refer to prescribing of quinolones and guideline-recommended antibiotics. Based on pseudonymized quarterly claims data, mixed logistic regression models examined pre-post intervention antibiotic prescribing rate changes and compared to matched standard care. A significant rate reduction (arm I 11.7%; arm II 9.9%; arm III 12.7%) and significantly lower prescribing rates were observed for all arms (20.1%, 18.9% and 23.6%) compared to matched standard care (29.4%). Fluoroquinolone prescribing was reduced in all intervention arms and rates for recommended substances generally increased. No significant post-interventional difference between intervention arms was detected. Findings indicate implementation program impact compared to standard care. Trial registration: ISRCTN, ISRCTN58150046

In the context of global changes, Nature-Based Solutions (NBSs) increasingly draw attention as a possible way to reduce disaster risk associated with extreme hydro-meteorological events while providing human well-being and biodiversity benefits at the same time. The PHUSICOS platform is dedicated to gather and analyse relevant NBSs used to reduce disaster risk associated with extreme hydro-meteorological events in mountainous and hilly lands. To design the platform, an in-depth review of 11 existing platforms has been performed. The platform currently references 152 literature NBS cases and is continuously enriched with demonstrator sites through the contribution of NBS community. The platform also proposes a qualitative assessment of the NBSs collected according to 15 criteria related with five ambits: disaster risk reduction, technical and economical feasibility, environment, society and local economy. This paper presents the structure of the platform and a first analysis of its content.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

Global climate change requires urgent solutions. Ambitious tree-planting initiatives, many already underway, aim to sequester enormous quantities of carbon, partly compensating for the anthropogenic CO₂ emissions that are a major cause of rising global temperatures. However, poorly planned and executed tree-planting could actually increase CO₂ emissions and have long-term, deleterious impacts on biodiversity, landscapes and livelihoods. Here, we highlight the main environmental risks of large-scale tree planting and propose ten golden rules, based on some of the most recent ecological research, to implement forest ecosystem restoration that maximizes rates of both carbon sequestration and biodiversity recovery, while simultaneously improving livelihoods. These are: i) Protect existing forest first; ii) Work together (involving all stakeholders); iii) Maximize biodiversity recovery to meet multiple goals; iv) Select appropriate areas; v) Use natural regeneration wherever possible; vi) Select species to maximise biodiversity; vii) Use resilient plant material (with appropriate genetic variability and provenance); viii) Plan ahead for infrastructure, capacity and seed supply; ix) Learn by doing (using an adaptive management approach); and x) Make it pay (ensuring the economic sustainability of the project). We focus on the design of long-term strategies to tackle the climate and biodiversity crises and support livelihood needs. We emphasize the role of local communities and their dependence on benefits from successful reforestation programmes that restore ecosystem functioning and deliver a diverse range of forest products and services. While there is no simple and universal recipe for forest restoration, it is now crucial to build on the public and private interest in this topic to ensure interventions provide effective, long-term carbon sinks and maximise benefits for biodiversity and people.

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data "from the ground up" and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers' hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

The concept of a Social License to Operate (SLO) has become increasingly important in the sustainability literature in recent years. Having its origins in the business discourse of the mining industry with respect to limiting opposition to mining projects, the notion of a social contract above and beyond legal requirements has since become applied across a number of different industries. Despite the concepts adoption confusion exists over the practices and outcomes of SLO, and particularly the nature of engagement. Given this situation it is surprising that not more attention to the role of marketing, and social marketing in particular, in operationalizing the concept. The paper discusses the potential of social marketing to contribute to SLO. Economic, political and social relations are complex in SLO and exchange is intricate in such relational environments. A community-based social marketing orientation is proposed as a means to improve exchange relations and enhance engagement. Seven models of SLO related social marketing models are discussed with community-based social marketing and adaptive co-marketing models being regarded as the most positive for the achievement of an SLO. Potential barriers to adoption of these approaches are noted.

We conducted a comparative study on the development of two synthetic methods. The solvent-free synthesis is a green chemistry method developed in a bid to ameliorate environmental adverse effects of the conventional solvent-based synthesis. The synthesis of novel dichloro and polymethoxy p-nitrophenylhydrazones through solvent free technique gave moderate to high yields which were however lower than those of the solvent-based method. The established solvent-free approach has several benefits, including universality and simplicity of the approach, catalyst-free conditions, non-use of an organic solvent, quick reaction time, fast and efficient workup, and un-solvated pure products in moderate to high yields.

Mzuzu city is Malawi’s fastest growing city but this has brought with it degradation of its urban green infrastructure (UGI) which has contributed to the city’s increased vulnerability to climate change-related hazards such as flooding. Mzuzu’s major UGI, the Lunyangwa River basin (LR) and the Mzuzu Botanic Gardens (MBG), are particularly affected. Nature-based solutions (NBS), interventions that improve the flow of ecosystem services (ES) to enhance and strengthen urban resilience and sustainability at multiple scales, contribute to the restoration, protection and management of ecosystems to address ecological degradation. This study maps land use land cover changes (LULC) to explore the role of NBS interventions in addressing the city’s climate change adaptation and mitigation challenges. The study used remote sensing data obtained from satellite imagery to map spatio-temporal changes in LULC in Mzuzu city in general and the LR basin and MBG ecosystems in particular, over a period of 34 years from 1986 to 2020. The study found that the LR basin and MBG have undergone both formal and informal urban encroachment in some sections and that in these areas urban communities are more affected by flood-related disasters than in those where there has been little or no encroachment. The study concludes that mapping and quantifying urbanisation-driven LULC provides a spatial framework for identifying and targeting NBS interventions which involve urban green infrastructure restoration and protection to address the city’s vulnerability to climate change impacts such as flooding and mudslides.

Biotechnology continues to drive innovation in the production of pharmaceuticals, biofuels, and other valuable compounds, leveraging the power of microbial systems for enhanced yield and sustainability. Genome-scale metabolic (GSM) modeling has become an essential approach in this field that enables a guide for target genetic modifications and optimization of metabolic pathways for various industrial applications. While single-species GSM models have traditionally been employed to optimize strains like Escherichia coli and Lactococcus lactis, the integration of these models into community-based approaches is gaining momentum. Constraint-based metabolic community (CBMc) models, which simulate the interactions between multiple microbial species, offer a more comprehensive understanding of metabolic networks and their potential for improvement of biotechnological processes on pilot- and industrial scales. However, there are few platforms that effectively support both single-species GSM and CBMc models, limiting the accessibility and application of these advanced modeling techniques. In this study, we demonstrate the use of the BioUML platform, one of the few tools that provides simultaneous investigation of both single-species and community models. We harnessed the platform to reconstruct and analyze microbial interactions within a synthetic consortium of widely used biotechnology microorganisms: methanotroph, Methylococcus capsulatus Bath and engineered Escherichia coli strains under oxygen- and nitrogen- limited conditions. Our research highlights the potential of E. coli to reduce the accumulation of inhibitory by-products like acetate, thereby enhancing the metabolic efficiency and growth of M. capsulatus. The study underscores the utility of CBMc models in optimization of microbial consortia for biotechnological applications, offering new strategies for the sustainable production of biofuels, single-cell proteins, and other high-value compounds.

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as Smart Dust. Several efforts focused primarily on computing and networking capabilities but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data was processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

Results-Based Financing (RBF) in pediatrics, in regions with scarce resources like Northern Uganda, provides an innovative approach to enhancing healthcare delivery. RBF aligns financial incentives with desired health outcomes, boosting service quality, efficiency, and effectiveness by tying compensation to achieving specific, measurable health targets. Lacor and Kalongo hospitals, before the introduction of RBF, faced numerous challenges, including limited resources, staffing shortages, and inconsistent quality of care. RBF adoption requires critical health outcomes to target, such as lowering child mortality rates, enhancing vaccination coverage, and increasing the frequency of timely prenatal care visits. The empirical application of RBF in pediatric services at Lacor and Kalongo Hospital from 2018 to 2024 shows improved healthcare quality and outcomes. Healthcare systems can identify ways to implement RBF models better, ensuring that financial incentives effectively contribute to attaining superior health outcomes.

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

In the construction sector, the integration of biobased materials has emerged as a pivotal aspect of EU policies, aimed at promoting sustainable practices throughout the value chain to mitigate resource consumption and carbon emissions. This paper explores the development and validation activities of a bio-based façade system within the Basajaun H2020 project, focusing on enhancing the utilization of biobased components within building envelopes to replace conventional solutions with eco-friendly alternatives. This paper reports the methodologies employed to detect requirements and outline the testing protocols undertaken to validate the façade system design devised within the project focusing on the original façade components as the bio composite profile. Vision and opaque façade modules are prototyped and tested following curtain wall standards standard for performance and acoustic assessments, to showcase the efficacy of the developed solution. The conducted tests demonstrate the feasibility of integrating bio-based components into building envelopes, aligning with project expectations, and prevailing standards for curtain wall façade solutions. Notably, the designed façade system meets technical conditions and project objectives. Nevertheless, the research underscores the need for further refinements to facilitate solution industrialization and explore broader market applicability. Future efforts will focus on addressing these improvements and assessing the scalability of the solution.

Electromobility promises to efficiently mitigate consequences of increasing traffic volume and its accompanied greenhouse gas emissions. On an individual level, electrified bikes allow emission free electrified mobility at moderate costs and consequently their stock has increased significantly in recent years. This simultaneously increases the demand for spare parts, which are often manufacturer or application-specific, and due to many variants, challenging to provide for the market. This article evaluates powder-based and extrusion-based metal additive manufacturing of a typical electrified bike component to demonstrate an alternative spare parts supply. The investigation demonstrates how these parts can be additively manufactured function equivalent and with sufficient mechanical properties, also taking economical aspects into account. Furthermore, the needed resources and related environmental consequences for metal-based additive manufacturing spare-part production are compared for both process routes. The results show that both routes are capable of producing spare-parts at comparatively same mechanical performance and resource costs, while needed resources such as energy, gases and manufacturing time are significantly lower for powder-based, respectively machine costs for extrusion-based additive manufacturing. Therefore, additive manufacturing offers a promising opportunity to produce parts in small quantities resource efficient and rapidly.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

The progress in markerless technologies is bringing clinicians tools to rapidly evaluate tests such as Timed Up and Go (TUG) without compromising the time of the assessment, yet it raises questions about the potential trade-off in accuracy compared to traditional marker-based systems, suggesting that the precision of these evaluations is not yet conclusively established. The OpenCap system is an easy tool to process 3D kinematics with two iPhone cameras placed in front of the subject. Previous research has primarily focused on walking and movement towards the cameras with the subject facing the cameras (WF) and thus has been unable to disentangle the differences in the movement directions. We have compared the OpenCap system with a standard marker-based system for both walking directions. In OpenCap, we found significantly worse results capturing walking facing away from the cameras (RW). Compared to the marker-based system, our findings indicate troubles measuring kinematic parameters but provide good accuracy in measuring spatial-temporal parameters. These insights pave the way for refining markerless assessment technologies, potentially enhancing their utility in clinical settings.

Electromagnetic actuation can support many fields of technology, such as robotics or biomedical applications. In this context, fully understanding the system behavior and proposing a low-cost package for feedback control is challenging. Modeling the electromagnetic force is particularly tricky because it is a nonlinear function of the actuated object’s position and coil’s current. Measuring in real time the position of the actuated object with the precision required for accurate motion control is also nontrivial. In this study, we propose a novel, cost-effective electromagnetic set-up to achieve position control via visual feedback. We actuate vertically and under different experimental conditions a 10 mm diameter steel ball hanging on a low-stiffness spring, demonstrating good tracking performance (the position error remains within ±0.5 mm with a negligible phase delay in the best scenarios). The experimental results confirm the feasibility of the proposed set-up, which is characterized by minimum complexity and realized with off-the-shelf and cost-effective components. For these reasons, such a contribution helps to understand and apply electromagnetic actuation even further.

Small-scale facilities play a significant role in the landscape of nuclear physics research in Europe. They address a wide range of fundamental questions and are essential for teaching and training personnel in accelerator technology and science, providing them with diverse skill sets, complementary to large projects. The current status and perspectives of nuclear physics research at small-scale facilities in Europe will be given.

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

Object-level Simultaneous Localization and Mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an Inertial Measurement Unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object’s surface and not the object’s shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.

The global consumption of vegan foods is experiencing an expressive upward trend, underscoring the critical need for quality control measures based on nutritional and functional considerations. This study aimed to evaluate the functional quality of caviar and salmon analogs food inks, based on pulses combined with nano ingredients. The primary objective of this work was to determine the total antioxidant compounds contained in these samples using a voltammetric technique with a glassy carbon electrode. Approximately 100 mg of samples underwent ethanolic extraction (70%) with 1 hour of stirring. The voltammograms were acquired under specific conditions: 10 mL of phosphate buffer electrolyte with a pH of 3.0; an initial potential (Ei) of 0.0 V; a final potential (Ef) of 1.0 V; a pulse amplitude of 50 mV; and a scan rate of 50 mV s-1. The reference electrode used was Ag/AgCl (KCl 3 mol L-1), and the auxiliary electrode was a platinum wire. The voltammograms revealed prominent anodic current peaks at 0.76-0.78 V, which are attributed to isoflavones. Isoflavones, known secondary metabolites with substantial antioxidant potential commonly found in pulses, were identified. The total isoflavone concentrations obtained ranged from 31.5 to 64.3 mg Eq genistein 100 g-1. The results not only validated the efficacy of the electrochemical sensor for quantifying total antioxidant compounds in the samples but also demonstrated that the concentration of total isoflavones in caviar and salmon analogs fell within the expected limits.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

Background: This practice guideline was developed by the Chronic Kidney Disease (CKD) Task Force, which was composed of clinical and methodological experts. The Saudi Arabian Ministry of Health and its Health Holding Company commissioned this guideline project to support the realization of the Vision 2030's healthcare transformation pillar. Methods: The guideline synthesis was guided by the GRADE-ADOLOPMENT methodology. Results: The final adoloped guideline addressed 12 clinical questions on blood pressure management in patients with CKD through a set of recommendations and performance measures. The recommendations included antihypertensive agents in children, non-renin angiotensin system inhibition (Non-RASi) vs RASi in adults, intensive vs standard blood pressure targets, early vs late assessment for kidney replacement therapy (KRT), late vs early preparation strategy for KRT, CKD symptoms during assessment for KRT or conservative management, initiation of KRT in patients with deteriorating CKD, choice of KRT modality or conservative management in certain CKD patient groups, changing or discontinuing KRT modalities, review frequency for KRT or conservative management, and Information, education and support. Conclusions: These conditional recommendations were based on low to very low certainty of evidence that highlights the need for high-quality randomized trials comparing different antihypertensive agents in patients with CKD.

The less virulent human (h) coronaviruses (CoVs) 229E, NL63, OC43, and HKU1 cause mild, self-limiting respiratory tract infections, while the more virulent SARS-CoV-1, MERS-CoV, and SARS-CoV-2 have caused severe outbreaks. The CoV envelope (E) protein, an important contributor to the pathogenesis of severe hCoVs infections, may provide insight into this disparate severity of the disease. We, therefore, generated full-length E protein models for SARS-CoV-1, -2, MERS-CoV, HCoV-229E, and HCoV-NL63 and docked C-terminal peptides of each model to the PDZ domain of the human PALS1 protein. The PDZ-binding motif (PBM) of the SARS-CoV-1, -2, and MERS-CoV models adopted a more flexible, extended coil while the HCoV-229E and HCoV-NL63 models adopted a less flexible alpha helix. All the E peptides docked to PALS1 occupied the same binding site and the more virulent hCoV E peptides generally interacted more stably with PALS1 than the less virulent ones. We propose that the increased flexibility of the PBM in more virulent hCoVs may permit more stable binding to various host proteins, thereby possibly contributing to more severe disease. This is the first paper to model full-length 3D structures for both more virulent and less virulent hCoVs E proteins, providing novel insights for possible drug and/or vaccine development.

Summary: Studying biological systems generally relies on computational modeling and simulation, e.g., for model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML), or the Open Modeling EXchange format (OMEX). Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in Java™. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations (ODEs); Stochastic Differential Equations (SDEs); constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. Availability and implementation: SBSCL is freely available at https://draeger-lab.github.io/SBSCL/.

Work has begun in earnest to formulate a post-2020 Global Biodiversity Framework which will outline the vision and targets for the next decade of biodiversity conservation and beyond. However, the performance of the 2011-2020 Strategic Plan for Biodiversity suggests that even a meaningful target can fail to deliver if not accompanied by fit-for-purpose indicators. Here we provide a review of how ‘protected area’ effectiveness was addressed in the 2011-2020 plan and based on this, provide recommendations for fit-for-purpose indicators that will measure how such efforts contribute to the conservation of biodiversity. Indicators need to be built on quantitative data from site-level biodiversity monitoring of species and ecosystems combined with measurements of the state of nature in near-time, informed by remote-sensed products and other technologies. Additionally, indicators need to capture whether the essential elements of good management are in place including the identification of ecological values, threats, and objectives, equitable governance, and sufficient management resources and capacity. These fit-for-purpose indicators will require multilateral collaboration to galvanize support for, and resources to develop, the necessary infrastructure to collate and store information from countries.

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that would take into account the particular biological and biophysical properties of MSF complexes. Here we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes, and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0– disease status and 1– reasonable control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.

Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.