This study assessed the performance of low-dose CT lung scans across various acquisition parameters and examined the impact of iterative reconstruction (IR) algorithms on image quality for lung cancer screening. The performance of multi-slice CT scanners was assessed by using a Catphan 600 phantom, specifically within the CTP404 test module that contains materials like Teflon, Delrin, Acrylic, Polystyrene, LDPE, PMP, Air, and water. Three protocols were selected and categorized into standard, low dose, and ultra-low dose. The standard protocol featured a tube voltage of 100 kVp for single-detector CT, a pitch of 0.75, detector collimation of 0.60 mm, and a gantry rotation time of 0.33 seconds, with 5 mm slice thickness. Images were reconstructed using standard kernels like B80f and 170f, combined with either FBP or various IR algorithms. Findings indicated that the noise power spectrum (NPS) peak value (HU2mm2) increased with higher IR levels, with FBP showing the highest peak. Additionally, NPS spatial frequency diminished as IR levels rose. Optimizing contrast and spatial resolution for both background and regions of interest involved adjusting the target transfer function (TTF) and contrast-to-noise ratio (CNR), crucial for protocol optimization. Proper adjustment of IR levels is vital, as higher levels can alter image texture and affect NPS spatial frequency. The results of this study showed that the use of IR algorithms in low-dose CT lung scans significantly improved image quality, particularly in terms of noise reduction and spatial resolution. However, the choice of acquisition parameters also played a crucial

Our study explores the genetic mechanisms underlying the spotted leaf phenotype in rice, focusing on the rpt5a mutant characterized by persistent reddish-brown leaf spots from seedling stage to maturity, leading to extensive leaf necrosis. Through map-based cloning, we localized the responsible locus to a 330Kb region on chromosome 2 and identified LOC_Os02g56000 (named OsRPT5A) as the causative gene. A point mutation in OsRPT5A, substituting valine for glutamic acid, was identified as the critical factor for the phenotype. Functional complementation and generation of knockout lines in the IR64 background confirmed the central role of OsRPT5A in controlling this trait. Our study also revealed that OsRPT5A is constitutively expressed across various tissues, with its subcellular localization unaffected by mutations. Notably, we observed an abnormal accumulation of reactive oxygen species (ROS) in rpt5a mutants, suggesting a disruption in the ROS system. Complementation studies indicated OsRPT5A's involvement in ROS homeostasis and catalase activity regulation. Moreover, the rpt5a mutant exhibited enhanced resistance to Xanthomonas oryzae pv. oryzae (Xoo), highlighting OsRPT5A's role in rice's pathogen resistance mechanisms. These findings elucidate the genetic basis of the spotted leaf phenotype and emphasize the intricate interplay between plant defense responses and developmental pathways.

This research aims to explore factors that influence sustainable music education from a psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines stochastic nonlinear system of control theory and identifies relevant factors affecting sustainable music learning by establishing a data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music education from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring sustainable music education and students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music education from an emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on sustainable music learning. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of sustainable student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on sustainable music learning are obtained.

With the continuous increase in ship size combined with the generally slower increase in the size of waterways, the need for the prediction of ship maneuvering in shallow waterways continues to attract attention from the international scientific community. Ship behavior in shallow water is relevant in seabed effects that result in changing the hydrodynamic forces acting on the ship. In this study, the maneuvering characteristics of container ship with four degrees of freedom in shallow water are analyzed. The Reynolds-averaged Navier Stokes approach in Ansys Fluent code is used to produce the maneuvering coefficients through the simulations of forward running, static drift, static heel, circular motion, the combined motions, and the pure roll motion of the Kriso container ship. The maneuvering characteristics of the ship are estimated for evaluating the ship behaviors in shallow water condition. The obtained results show that the roll has a significant decrease and the ship’s turning diameter has a significant increase when the ship operates in the shallow waterway. The prediction of maneuvering characteristics of the ship are in good agreement with those of free running model tests, indicating that the numerical simulation based on the Computational Fluid Dynamics method has compromising capability to predict the maneuvering derivatives and the 4-DOF ship maneuvering motion in shallow water as well.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Analysis of liquid biopsy samples can reveal a wealth of molecular as well as cellular entities circulating in various body fluids. These entities include circulating tumor cells, circulating cell-free and tumor DNA, RNA, and proteins. Detection and characterization of this genetic and proteomic material can provide crucial information on tumor characteristics, including its heterogeneity, biology, tumor progression, staging, associated gene mutations and the course of therapy. The increased use of liquid biopsy in clinical oncology offers in non-invasive manner easier tumor sampling and continuous monitoring of the disease evolution through repetitive sampling from cancer patients. The continuous monitoring offered by this method is highly relevant to the assessment of a given therapeutic regime and early detection of markers associated with possible drug resistance, which may develop in the course of the therapy. The use of proteomic approaches, including mass spectrometry-based proteomics for the analysis of biofluids extends for over three decades, yet only in the last few years such methods have found their way to clinical oncology. The non-invasive nature of liquid biopsy and the simple procedures for sample collection made this approach far more attractive than the traditional and well-established tissue biopsy. Such preference becomes more practical in the analysis of solid tumors, particularly in pediatric population. In this review we discuss some works on the emerging role of liquid biopsy analyses in clinical oncology, and how such use allows a better understanding of diagnosis, prognosis, disease evolution and response to therapy of various forms of cancer. We also discuss the role of MS-based methods in the characterization of post-translational modifications of proteins circulating in liquid biopsies.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

To address the large hysteresis and susceptibility to instability in high-speed deposition fusion moulding (FDM) 3D printing’s temperature control, this paper proposes an improved ant-lion optimization algorithm (OCALO) to optimize the PID parameters.The improvement aims to solve the problem that the traditional ant-lion optimization algorithm (ALO) reduces the population diversity and tends to fall into the local optimal solution when it is close to the global optimal solution.The generation of the initial solution is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping. This integration into Elite Opposition-Based Learning aids in optimizing population diversity. Concurrently, the incorporation of cosine factors of a specified parameter into the elitist formula of the traditional ALO algorithm is aimed at reducing the tendency of ants to randomly gravitate towards ant-lions with lower fitness values. This adjustment helps in balancing the exploration-exploitation trade-off in the algorithmic process. Compared with two existing classical algorithms and three improved ALO algorithms, the algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The improved algorithm was combined with PID control to design an OCALO-PID temperature controller and simulated on a high-speed 3D printing temperature model identified by modelling. The results show that the method improves the transient and steady-state performance of reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a physical experimental platform to verify the feasibility of the algorithm.

With the COVID-19 pandemic behind us, the U.S. Food and Drug Administration (FDA) has approved 55 new drugs in 2023, a figure consistent with the number authorized in the last five years (53 per year on average). Thus, 2023 is the second-best FDA harvest after 2018 (59 approvals) in all the series. Monoclonal antibodies (mAbs) continue to be the class of drugs with most approvals, with an exceptional 12, a number that makes it the most outstanding year for this class. As in 2022, five proteins/enzymes have been approved in 2023. However, no antibody–drug conjugate (ADC) has been released onto the market. With respect to TIDES (peptides and oligonucleotides), 2023 has proved a spectacular year, with a total of nine approvals, corresponding to five peptides and four oligonucleotides. Natural products continue to be the best source of inspiration for drug development, with 10 new products on the market. Three drugs in this year’s harvest are pegylated, which may indicate the return of pegylation as a method to increase the half-lives of drugs, after the withdrawal of peginesatide from the market in 2013. Following the trends of recent years, two bispecific drugs have been authorized in 2023. As in preceding years, fluorine and/or N-aromatic heterocycles are present in most of the drugs. Herein, the 55 new drugs approved by the FDA in 2023 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins/enzymes); TIDES (peptide and oligonucleotides); combined drugs; pegylated drugs; natural products; nitrogen aromatic heterocycles; fluorine-containing molecules; and other small molecules.

Background and Objectives: Rheumatic diseases often exhibit a complex pattern of comorbidities, including the increased occurrence of emotional disorders like anxiety and depression, which are closely related to the presence of chronic pain. Therefore, a multimodal approach is necessary for treatment, targeting both the physical and psychological functioning. Due to the increased burden within the healthcare context that emerged during the post-pandemic period, the need to adjust the content and length of psychological interventions became ubiquitous. In this way, the main objective of this study was to investigate the efficiency of a single-session, process-based psychological intervention for patients with rheumatic conditions and emotional comorbidities in a naturalistic, in-patient setting. Materials and Methods: After the screening, a total of 31 participants (Mage = 58.9, SD = 12.03) completed the single session intervention. The therapeutic protocol relied on a process-based cognitive-behavioural therapy framework aiming to improve cognitive, emotional and behavioural functioning. Assessments were conducted prior to the intervention, post-intervention and one month following the hospitalization. Results: Immediately after the intervention, significant reductions in dysfunctional cognitive and emotional processes, along with the improvement of behavioural strategies were observed. At the one-month telephonic evaluation, participants reported decreased pain, state anxiety and state depression levels. Conclusions: The single-session, process-based approach represents a promising intervention in healthcare contexts, targeting important psychopathological mechanisms in patients with rheumatic conditions. Future studies could clarify the extent to which these outcomes persist over time and their specific associations with anxiety and depressive symptoms, by using more controlled and rigorous trial designs.

The impact of long-term soil and crop management practices on soil biological and chemical properties in oilseed brassica-based production systems were deliberated for holistic soil health improvement in the present study. Different tillage, crop residue re-cycling and cropping systems were studied for 5-years in 0-15 and 15-30 cm soil profiles. No-till permanent beds with crop residue (PB+R) noticeably improved soil organic carbon (SOC), microbial biomass carbon (MBC), enzymes [dehydrogenase (DHA) and alkaline phosphatase (AlP)], nitrogen fractions (available, nitrate and total), and available phosphorus and potassium content in both the soil layers compared to conventional tillage without crop residues. Though, the plow soil layer (0-15 cm) showed higher concentration of soil carbon, enzymes, N fractions and available P than in the sub soil (15-30 cm). The dynamic soil biological and chemical properties were also varied with the crop stage, and recorded higher MBC at 30 days, SOC and enzymatic activities at 60 days, and N fractions and available P and K at harvest of mustard crop. Green gram-mustard rotation showed higher values of biological and chemical parameters. Thus, the legume-based mustard crop rotation following no-till permanent beds and residue re-cycling found holistic in improving soil health, and nutrient cycling.

Multiple Sclerosis (MS) is a neurodegenerative disease that mainly affects young adults. The high prevalence of the disease and its impact on patients' quality of life led to priority being given to the etiopathogenesis of the disease to identify possible modifiable factors and consequent effective intervention strategies. Recent hypotheses suggest that the etiopathogenesis of MS is multifactorial and includes factors related to the immune system, neuroinflammation and neuro-degeneration. In this scenario, sleep has a close - although indirect - relationship with MS, through its relationship with each of those factors. In particular, the role of cytokines in immune system impairment and neuroinflammation, mitochondrial dysfunction and oxidative stress in neurodegeneration all are associated with sleep in different forms. Furthermore, melatonin in relation with vitamin D has a potential therapeutic effect in MS sleep-related problems. Given the growing interest of research in the mechanisms underlying MS and therapies able to alleviate MS-related symptoms at all stages of the disease, a more in-depth study of the role of sleep disturbances and the factors intrinsically related to sleep and MS could be useful both to investigate the etiopathogenetic factors of MS and to develop potential non-invasive intervention strategies for MS treatment.

A capacity to reflect on experience and learn through it (reflective and reflexive practice) is deemed an essential part of ethical practice and research. Challenging traditional research approaches this arts-based, inter-disciplinary, Ph.D. project explores artistic practice as a research methodology, and how learning through experiences of ‘making’ things may enhance and amplify our understanding of the more affective aspects of human situations and experience. Grounded in the author’s artistic practice and learning through experiences of ‘making’, the author assembles and adapts frames from psychoanalysis, art (psycho)therapy, and the arts with/through which to observe and document their experience of an organisational situation. Describing the development of their method in three phases, analysis takes place through returning to revisit and rework artistic material produced, and engaging in conversation with the emergent material and others in response. Results take the form of artworks and artistic projects, including documentation of process. Emergent threads draw attention to the speculative, entangled, and affective nature of the research process, the reflexivity generated through moving and handling material, and the reflexive work of undergoing, foregrounding an ethics of responsibility, attention, and care for/of the body. As a space for imaginative encounter and performative enactment, and a site for reflexivity through which one may be pressed to notice and feel more acutely, the author argues that the research value resides in the capacity of this method to embrace complex relationalities, and engage our affective, ethical sensibilities in ways that may not emerge through more traditional approaches to reflective/reflexive practice(s). This has implications for both art therapy practice and research, amplifying the learning opportunities afforded by moving, modifying, and assembling things differently, the embodied ‘work’ of art as a method of enquiry, the cultural sensitivity of documentary fragments captured in/through various media and voices, and the value of collaborative endeavours where meaning is co-constructed in conversation.

Conditional Privacy Preserving Authentication (CPPA) schemes are an effective way of securing communications in Vehicular Adhoc Networks (VANETs), as well as ensuring user privacy and accountability. Cryptanalysis plays a crucial role in pointing out the vulnerabilities in existing schemes to enable the development of more resilient ones. In 2019, Zhang proposed a CPPA scheme for VANET security (PA-CRT), based on identity batch verification (IBV) and Chinese remainder theorem (CRT). In 2023, this scheme was crypt-analyzed by Tao. In this paper we provide our own crypt analysis of Zhang’s scheme and propose counter measures to mitigate Tao’s attacks. Furthermore, in 2021, Xiong proposed a Certificateless Aggregate Signature (CLAS) scheme which was cryptanalyzed by Shim in 2023. Xiong subsequently proposed a modified CLAS scheme which is also cryptanalyzed in this paper.

Contrast agents developed for X-ray computed tomography (CT) offer extremely valuable tools and techniques in diagnostics via contrast enhancements. Heavy metal-based nanoparticles (NPs) can provide high contrast in CT images. These result from a high density of heavy metal atoms with high X-ray attenuation coefficients that exceed that of iodine (I), which is currently used in hydrophilic organic CT contrast agents. Nontoxicity and colloidal stability are vital characteristics in designing heavy metal-based NPs as CT contrast agents. In addition, the particle size must be small for in vivo renal excretion. In vitro phantom imaging studies can provide X-ray attenuation efficiency, which is a critical parameter for CT contrast agents, and the imaging performance of CT contrast agents can be demonstrated via in vivo experiments. This review focuses on in vitro and in vivo studies of various heavy metal-based NPs (metal: Au, Pt, Pd, Ag, Ce, Gd, Dy, Ho, Yb, Ta, W, and Bi) and provides an outlook on their use as high-performance CT contrast agents.

Wildlife tourism and wildlife trade may appear juxtaposed, but are two, potentially aligning, in-come generators that could benefit conservation in developing countries. Utilising data sets collated from Madagascar’s Ministère du Tourisme and CITES respectively for the period 2007 to 2018, this study estimated levels of income from wildlife tourism and wildlife trade for Madagascar. Between 2007-2018, tourism reported yearly incomes ranging from a low of US \$1.4 million up to a high of US \$15.7 million. However, it was unclear what percentage of this figure flowed to benefit local communities. Alternatively, using reported networks for the live wildlife trade, the estimated eco-nomic value reaching collectors and/or intermediaries on Madagascar was US $72,299.80 for the period 2007 to 2018. Both revenue generators operated within different geographical areas, with tourism opportunities presenting themselves to communities adjacent NPs, while wildlife trade networks were not restricted to National Parks and operated sporadically across Madagascar. Hence, the economic benefits reached different Malagasy participants across the country. The management of both activities needs great care to ensure that environmental impacts and sus-tainability are core measures on any such activities. Whilst this study shines light on economic values and novel perspectives regarding these two trade types, it also highlighted knowledge gaps. Thus, indicating where much greater research attentions was required to allow better understanding of the specific benefits and risks from engaging with both trade types for local Malagasy people and their environments.

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope, in a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with natural waste local materials. Thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters, manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives involved a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181 – 0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275 – 0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high Transmission Loss values were obtained for the natural additives (TL = 35 – 59 dB). Petiol of Palm and Stipa Tenacissima were found as the materials able to improve both thermal and acoustic properties.

In this paper, stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system's average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to simplicity to implement and no presumptions to simplify. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is applied to determine the decision variables defined as the network's optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization, some objectives are weakened compared to their base value, while the results of the MOIF indicated a fair compromise between different objectives, and all objectives are improved. The results of MOIF based on the IMTBO cleared that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is proved compared to the conventional MTBO, particle swarm optimization, and artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag, and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively compared to the deterministic MOIF model.

Analytical chemistry applied to medical and diagnostic analysis, recently focused on the development of cost-effective biosensors able to monitor the health status or to assess the level of specific biomarkers that can be indicative for several diseases. The improvement of technologies relating to the possibility of non-invasive sampling of biological fluids, as well as sensors for the detection of analytical signals and the computational capabilities of the systems routinely employed in everyday life (e.g. smartphones, computers, etc.), make the complete integration of self-standing analytical devices more accessible. This review aims to discuss the biosensors that have been proposed in the last 5 years focusing on two principal detecting approaches, optical and electrochemical, which have been employed for quantifying different kind of target analytes reaching detection limits below the clinical sample levels required. These detection principles applied to Point-of-care (POC) devices, have been extensively reported in literature and even the limited examples found on the market are based on these strategies. This work will show the latest innovations considering the integration of optical and electrochemical detection with the most commonly reported analytical platforms for POC applications such as paper-based or wearable and implantable devices.

Current and dynamic developments in green technologies have led to several innovation practices in the manufacturing sector only to become the top approaches used for achieving and accelerating sustainable development (SD) in the current business markets. In addition, manufacturing firms is in need of green innovation to be able to monitor and control their operations and enhance their environmental performance. However, regardless of its many benefits, the level of green innovation adoption and implementation is still lower than expected among manufacturing industries. Thus, this study aimed to minimize the gap by developing and validating a study model underpinned by Resource based view and Institutional theories, along with the Technology-Organization-Environment (TOE) framework in combination to convince firms to adopt green innovation. The study gathered data from 179 respondents using a survey distributed to manufacturing firms, after which data was exposed to Structural Equation Modeling (PLS-SEM) approach, for analysis. Based on the approach deliverables, all the integrated constructs of the model, namely perceived benefits, top management support, coercive pressure, normative pressure and mimetic pressure all predicted green management accounting practices. Moreover, green management accounting practices were found to directly and significantly affect green environmental performance. The developed integrated model provides a clear implication to decision-makers, indicating the importance of adopting and using green practices and innovative technologies for enhancing environmental performance. Based on the results from the reviewed advanced green technologies studies, there is considerable connection between green management accounting practices and environmental performance in the context of developing economies.

Because of hyper complexity, difficulty to define, multiple stakeholders with conflicting perspectives, and lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations Research has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing sub-problems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical sub-divisions. The proposed methodology, implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable sub-problems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes sub-problems. Finally, the sub-problems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The article concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond.

Oral and parenteral delivery of first-line anticonvulsant Valproic cid (VA) are associated with serious adverse effects, high hepatic metabolism, high clearance, and low bioavailability in brain. GastroPlus program was used to predict in vivo performance of immediate (IR) and sustained release (SR) products in human. HSPiP software predicted excipients with maximum possible miscibility of the drug. Based on GastroPlus and HSPiP program, various excipients were screened for experimental solubility study and prepared cationic nanoemulsions and respective gels for nasal to brain delivery. These were characterised for size, size distribution, polydispersity index, zeta potential, morphology, pH, % transmittance, drug content, and viscosity. In vitro drug release, ex vivo permeation profile (goat nasal mucosa), and penetration studies were conducted. Result showed that in-vivo oral drug dissolution and absorption were predicted as 98.6 mg and 18.8 mg, respectively from both tablets (IR and SR) at 8 h using GastroPlus. The drug access to the portal vein was relatively predicted high in IR (115 mg) as compared to SR (82.6 mg). Plasma drug concentration-time profile predicted was in good agreement with published reports. The program predicted duodenum and jejunum as the prime site of drug absorption and no effect of nanonization on Tmax for sustained release formulation. Hansen parameters suggested suitable selection of excipients. The program recommended for nasal to brain delivery of the drug using cationic mucoadhesive product. The optimized stable CVE6 was associated with optimal size (113 nm), low PDI (0.26), high zeta potential (+34.7 mV), high transmittance (97.8%), and high strength (0.7 %w/w). In vitro release and ex vivo permeation of CVE6 were found to be substantially high as compared to anionic AVE6 and respective gels. Penetration study executed high fluorescence intensity with CVE6 and CVE6-gel as compared to suspension and ANE6 which may be attributed to electrostatic interaction between mucosal membrane and nanoglobules. Thus, cationic nanoemulsion and respective mucoadhesive gel are promising strategy for delivery of VA to brain through intransal administration for the treatment of seizure and convulsion.

This review article is aimed at exploring the possibility of developing an integrated approach in the management of the different needs of endometrial cancer (EC) patients seeking to become pregnant. Life preservation of the woman, health preservation of the baby, a precocious and – as much as possible – minimally invasive characterization of the health and fertility parameters of the patient, together with the concerns regarding the obstetric, neonatal, and adult health risks of the children conceived via assisted reproduction techniques (ART) are all essential aspects of the problem to be taken into consideration, yet the possibility to harmonize such needs through a concerted integrated approach is still highly challenging. This article aims to illustrate the main features of EC and how it affects the normal physiology of pre-menopausal women. Then, we focus on the prospect for a miR-based, molecular evaluation of patient health status, including both EC early diagnosis and staging and, similarly, the receptivity of the woman, discussing the possible evaluation of both aspects using a single specific panel of circulating miR in the patient, thus allowing a relatively fast, non-invasive way to perform such testing with a significantly reduced margin of error. Finally, the ethical and legal/regulatory complexities arising from such innovative techniques require not only a risk-benefit analysis; respect for patient autonomy and equitable health care access allocation are fundamental issues as well.

Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.

A claim management office (CMO) is a new intra-organisation fit based on an organisational project management (OPM) approach and deals specifically with improving claim performance. However, CMO is either misunderstood or often overlooked in construction companies, mainly due to ignorance of the OPM context but further exacerbated by a lack of real case study research on how to adopt CMO in these organisations. This study addresses this prevailing knowledge gap by investigating the nature of the construction industry from the CMO viewpoint and developing an extensive framework through which to improve organisational claims performance. Organisational ambidexterity theory integrated with X-inefficiency theory is adopted for claims performance outcomes, due to organisation intra-firm irrational decisions when managing such claims. Data was obtained and analysed from a international large construction company (which used CMOs) and its internal focus group discussion (as a multi-method approach). The reference framework provides new perspectives on how construction-related companies can adopt a CMO structure, which enables them to improve claims performance by planning in three interrelated activities viz. function-, process- and performance-based-. Furthermore, findings contribute to researchers and practitioners by providing a true understanding of the CMO-related mechanism and plausible roadmap for future work.

Knowledge of the temperature evolution is crucial to understand and control laser beam welding of low-melting materials. Existing temperature determination approaches are restricted to i) one-dimensional temperature information (e.g. ratio-pyrometers), ii) a priori knowledge of the emissivity (e.g. thermography) and iii) high temperature regions (e.g. two-wavelength imaging). In this paper, a ratio-based two-color-thermography approach is developed that allows for two-dimensional temperature determination in low-melting temperature ranges (< 1200 K). For static measurement situations it is demonstrated, that temperature can be determined despite variation in signal intensity and emissivity with high accuracy. The two-color-thermography set-up is further transferred into a commercial laser beam welding machine and experiments are conducted for varying process parameters. The direct application of the developed two-color-thermography system in dynamic process situations is limited as image artifacts presumably caused by internal reflections inside the optical beam path are present.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

As a result of the growing demand for foods with reduced animal protein content, many new alternative diets are now emerging. Nevertheless, recent studies have shown that the Western population is unprepared for drastic changes and is disinclined to accept foods based on alternative proteins. However, hybrid products might become a good transitional offer. This study used a desirability-based mixture design to model hybrid spreadable cheese analogues (SCAs). The design combined the dairy protein (MPC), Tenebrio molitor (IF) and faba bean (FBP) flours. Nine SCAs with different MPC/FBP/IF ratios were formulated, representing 0, 50 and 100% MPC replacement (7.1% of the formula). Incorporating the IF negatively impacted the desirable texture properties. The FBP flour improved the texture (achieving increased firmness and stickiness and decreased spreadability), but only when combined with MPC. Changing the MPC/FBP/IF ratio affected the colour of SCAs. Sensory analysis showed that hybrid SCAs (≤ 50% MPC) had a more characteristic cheesy flavour than the commercial plant-based reference, and sample C2 had a texture profile similar to the dairy reference. Samples containing IF showed a better flavour profile than the products without IF. The SCAs had higher protein and lower saturated fat, starch and sugar con-tent than commercial analogues.This study demonstrates that the inclusion of alternative proteins can be effective as a strategy to reduce dairy protein content in hybrid product formulations.

Membrane-based beverage dealcoholization is a successful process for producing low- and non-alcoholic beer and represents a fast-growing industry. Polyamide NF and RO membranes are commonly applied for this process. Polyelectrolyte multilayer (PEM) NF membranes are emerging as industrially relevant species and their unique properties (usually hollow fiber geometry, high and tunable selectivity, low fouling) underlines the importance of testing them in the food industry as well. To test PEM NF membranes for beer dealcoholization, at small pilot scale we dealcoholized filtered and unfiltered lager beer with the tightest available commercial polyelectrolyte multilayer NF membrane (NX Filtration dNF40), which has a MWCO=400Da which is quite high for these purposes. Dealcoholization is possible with a reasonable flux (10 LMH) at low pressures (5-8.6 bar) with a real extract loss of 16-18% and an alcohol passage of ~100%. Inorganic salt passage is high (which is typical for PEM NF membranes), which greatly affected beer flavor. During the dealcoholization process, the membrane underwent changes which substantially increased its salt rejection values (MgSO4 passage decreased 4-fold) while permeance loss was minimal (less than 10%). According to our sensory examination, the process yielded an acceptable flavored beer which could be greatly enhanced by the addition of the lost salts and glycerol.

The vulcanization of rubber compounds is an exothermal process. A carbon black-filled and natural rubber-based (NR) formulation was mixed with different levels of sulfur (0.5, 1.0, 2.0, 4.0 and 6.0 phr) and studied with Differential Scanning Calorimetry (DSC) for the determination of the vulcanization enthalpy. It was found that the vulcanization enthalpy is dependent from the amount of sulfur present in the compound and the vulcanization heat released was -18.4 kJ/mol S if referred to the entire rubber compound formulation or -46.0 kJ/mol S if the heat released is referred only to the NR present in the compound. The activation energy for the vulcanization of the rubber compounds was also determined by a DSC study at 49 kJ/mol and found quite independent from the sulfur content of the compounds under study. A simplified thermochemical model is proposed to explain the main reactions occurring during the vulcanization. The model correctly predicts that the vulcanization is an exothermal process although it gives an overestimation of the vulcanization enthalpy which is larger for the EV vulcanization package and smaller for the conventional vulcanization system. If the devulcanization is conducted mechanochemically in order to break selectively the sulfur-based crosslinks, then the natural rubber compounds recovered from used tires can be re-vulcanized again and the exothermicity of such process can be measured satisfactorily with DSC analysis. This paper not only proposes a simplified mechanism of vulcanization and devulcanization but also proposes an analytical method to check the devulcanization status of the recycled rubber compound in order to distinguish truly devulcanized rubber from reclaimed rubber.

In this empirical article, we argue that, while emergency remote teaching (ERT) may have achieved its goal of saving the academic years during the COVID-19 pandemic, it also constructed unintended pedagogical consequences that were possibly overlooked at the time of advocating it. The research question that the article attempted to answer is, what unintended pedagogical consequences did students and lecturers suffer because of the move to ERT at rural-based universities (RBUs) in South Africa? Drawing on students' and lecturers’ lived experiences of ERT, this article foregrounds unintended pedagogical consequences that arose at one RBU in South Africa during the transition from face-to-face teaching to ERT. Underpinned by the tenets of critical realism philosophy, as well as student integration theory, in-depth interviews with three lecturers and six students were conducted. The findings of the study indicate that home conditions, individual characteristics, pre-COVID-19 blended learning experiences, university training and support, and teaching, learning, and assessment practices and policies altogether contributed to the construction of unintended pedagogical consequences of ERT presented in this article. These consequences include (1) exclusion of low-income students in active teaching and learning, (2) equipping middle-class students with better chances of success than working-class students. (3) distressing female students and lecturers more than their male counterparts, and (4) unproductive assessment practices. This study may be beneficial to academics and policymakers from similar contexts in their plight to continue with remote teaching and assessment (RTA) beyond the pandemic.

Underpinned by the new world Kirkpatrick model and in the context of a community-based, sport psychology program (My Strengths Training for Life™) for young people experiencing homelessness, this process evaluation investigated: (1) young peoples’ reactions (program and facilitator evaluation, enjoyment, attendance, and engagement) to and learning (mental skills and transfer intention), (2) the relationship between reaction and learning variables, and (3) the mediators underpinning this relationship. 301 young people living in a West Midlands housing service completed questionnaires on demographics, reaction and learning variables. Higher levels of program engagement were positively associated with more favorable reactions to the program. Enjoyment positively predicted learning outcomes, which was mediated by transfer intention. Recommendations are made for: (1) a balance between rigor and flexibility for evaluation methods with disadvantaged youth, (2) including engagement as well as attendance for indicators of meaningful program participation, (3) measuring program experiences (e.g., enjoyment) to understand program effectiveness, and (4) providing opportunities for skill transfer during and after program participation. Findings have implications for researchers, program commissioners, and policy makers working designing and evaluating programs in community-based settings.

The ability to directly look into genome sequences has opened great opportunities in plant breeding. Yet, the assembly of full-length chromosomes remains one of the most difficult problems in modern genomics. Genetic maps are commonly used in de novo genome assembly and are constructed on the basis of a statistical analysis of the number of recombination rather than physical distance in base pairs. This may affect the accuracy of the ordering and orientation of scaffolds within the chromosome, especially in the region of recombination suppression. Here we report the use of Tyr-FISH for validation of genetic and pseudochromosome maps. For probe design we developed a pipeline that is based on selection of a unique sequence with minimal potential fluorescent background arising from non-specific in situ hybridization. In total of 24 unique genes were located on physical chromosomes 2 and 6. The order of markers has been corrected by integration genetic and cytogenetic maps. Tyr-FISH mapping showed that the order of 23.1% (chromosome 2) and 27.3% (chromosome 6) of the tested genes differed between physical chromosomes and pseudochromosomes. Also the position of the mlh1 gene, which was not on the genetic map, was defined on physical chromosome 2. The results suggest the possibility of Tyr-FISH mapping of any gene that may not be neither on the genetic map nor in the assembly. Hence, Tyr-FISH provides valuable information for the improvement of the genome assembly.

Background: pathophysiological changes like low cardiac output (LCO) impact pharmacokinetics, but its extent may be different throughout pediatrics compared to adults. Physiologically based pharmacokinetic (PBPK) modelling enables further exploration. Methods: A validated propofol model was used to simulate the impact of LCO on propofol clearance across age groups using the PBPK platform, Simcyp® (version 19). The hepatic and renal extraction ratio of propofol was then determined in all age groups. Subsequently, dose explorations were conducted under LCO conditions, targeting a 3 µg/mL (80-125%) propofol concentration range. Results: Both hepatic and renal extraction ratios increased from neonates, infants, children to adolescents and adults. The relative change in clearance following CO reductions increased with age, with the least impact of LCO in neonates. The predicted concentration remained within the 3 µg/mL (80-125%) range under normal CO and LCO (up to 30%) conditions in all age groups. When CO was reduced by 40-50%, a dose reduction of 15% is warranted in neonates, infants and children, 25% in adolescents and adults. Conclusions: PBPK driven, the impact of reduced CO on propofol clearance is predicted to be age-dependent, proportionally greater in adults. Consequently, age group specific dose reductions for propofol are required in LCO conditions.

Most methods for measuring environmental lead (Pb) content are time consuming, expensive, hazardous, and restricted to specific analytical systems. To provide a facile, safe tool to detect Pb, we created pMet-lead, a portable fluorescence resonance energy transfer (FRET)-based Pb biosensor. pMet-lead comprises a 3D-printed frame housing a 405-nm laser diode — an excitation source for fluorescence emission images (YFP and CFP) — accompanied by optical filters, a customized sample holder with a Met-lead 1.44 M1 (the most recent version)-embedded biochip, and an optical lens aligned for smartphone compatibility. Measuring the emission ratios (Y/C) of the FRET component enables Pb detection with a dynamic range of nearly 2 (1.96), pMet-lead/Pb dissociation constant (K_d) 45.62 nM, and limit of detection 40 nM (0.832 μg/dL, 8.32 ppb). To mitigate earlier problems with lack of selectivity for Pb vs. zinc, we preincubated samples with tricine, a low-affinity zinc chelator. We validated pMet-lead measurements of characterized laboratory samples and unknown samples from six regions in Taiwan by inductively coupled plasma mass spectrometry (ICP-MS). Notably, two unknowns had Y/C ratios significantly higher than that of the control (3.48 ± 0.08 and 3.74 ± 0.12 vs. 2.79 ± 0.02), along with Pb concentrations (10.6 ppb and 15.24 ppb) above the WHO-permitted level of 10 ppb in tap water, while the rest four unknowns showing no detectable Pb upon ICP-MS. These results demonstrate that pMet-lead provides a rapid, sensitive means for on-site Pb detection in water from the environment and in living/drinking supply systems to prevent potential Pb poisoning.

With increased vulnerabilities and vast technology landscapes, it is extremely critical to build systems which are highly resistant to cyber-attacks, to break into systems to exploit. It is almost impossible to build 100% secure authentication & authorization mechanisms merely through standard password / PIN (With all combinations of special characters, numbers & upper/lower case alphabets and by using any of the Graphical password mechanisms). The immense computing capacity and several hacking methods used, make almost every authentication method susceptible to cyber-attacks in one or the other way. Only proven / known system which is not vulnerable in spite of highly sophisticated computing power is, human brain. In this paper, we present a new method of authentication using a combination of computer’s computing ability in combination with human intelligence. In fact this human intelligence is personalized making the overall security method more secure. Text based passwords are easy to be cracked ^[6]. There is an increased need for an alternate and more complex authentication and authorization methods. Some of the Methods ^{[7] [8]} in the category of Graphical passwords could be susceptible, when Shoulder surfing/cameras/spy devices are used.

It is widely accepted that plastic waste is one of our most urgent environmental concerns the world is currently facing. Plastic has contributed greatly to innovation in all fields, but the raw material for its production (fossil fuels), as well as the linear economy in which it is currently produced and used, makes the material problematic from a sustainability and human health perspectives. The emergence of bio-based plastics provides an opportunity to reduce dependency on fossil fuels and transition to a more circular plastics economy. For polyethylene terephthalate (PET), one of the most prevalent plastics in packaging and textiles, there exist two bio-based alternatives that are similar or superior in material property and recyclability. These are polyethylene furanoate (PEF) and polytrimethylene terephthalate (PTT). The overarching aim of this study was to examine the transition from fossil-based to renewable plastics, through the lens of PET upcycling into PEF and PTT. The process for the production of PEF and PTT from three feed streams (post-consumer PET waste, lignocellulosic biomass-derived cellulose and biodiesel-derived crude glycerol) was developed in the software SuperPro Designer and the economic viability assessed via a discounted cumulative cash flow (DCCF) analysis. This study represents a conceptual case study of the valorisation of existing plastic waste into new, recyclable bio-based plastics, for a more sustainable plastics production. A techno-economic analysis of the designed process revealed that the minimum selling price (MSP) of second generation-derived PEF and PTT is 3.13 USD/kg, and that utilities and the feedstock used for the production of 2,5-furandicarboxylic acid (FDCA) needed in PEF synthesis contributed the most to the process operating costs. The effect of recycling PEF and PTT through the process at three recycling rates (42%, 50% and 55%) was investigated and revealed that increased recycling could reduce the MSP of the 2G bio-plastics (by 48.5%) to 1.61 USD/kg. This demonstrates that increasing recycling rates would have a beneficial effect on the economic viability of the plastic biorefinery.

The oil and gas sector has recognised structural integrity assessment of ageing platform for prospective life extension as a rising concern, particularly in encountering the randomness of the harsh ocean environments. This condition leads to uncertainty in wave-in-deck load estimates and a high load level being imposed on offshore structures. This emphasises the necessity of enhanced reliability, as failure might result in inaccessibility because of the uncertainties related to long-distance services, such as accuracy of predictions of loads and responses. Even though the established guidelines present a fundamental assessment, additionally, comprehensive rules are required. This paper performed a reliability analysis incorporating practical approaches that can more accurately represent time-dependent structural deterioration. The following two procedures have been adopted by a majority of significant oil and gas operators to monitor the safety and integrity of these structures: a) Ultimate Strength Assessment (USA) method and b) Reliability Design Assessment (ReDA) method. A comparison of these two reliability approaches was performed on selected ageing jacket structures in the region of the Malaysian sea. The comparative findings, namely, reserve strength ratio (RSR) at various years of the return period (RP) and ratio value for risk of failure regarding the probability of failure (POF), provided a check and balance in strengthening confidence in the results. The findings showed that the structural components might safely survive either using the USA and ReDA method in such conditions, as the reliability indexes were determined to be satisfactory compared to allowable values from ISO 19902 design specifications. Therefore, these evaluations were determined to control the risk level of the structure during the remaining of its lifetime and undertake cost-effective inspections or mitigation strategies when necessary.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

This study investigated the roles of low-molecular-weight fucoidan (LMWF) in enhancing the anti-cancer effects of fluoropyrimidine-based chemotherapy. HCT116 and Caco-2 cells were treated with LMWF and 5-FU. Cell viability, cell cycle, apoptosis, and migration were analyzed in both cell types. Potential mechanisms underlying how LMWF enhances the anti-cancer effects of fluoropyrimidine-based chemotherapy were also explored. The cell viability of HCT116 and Caco-2 cells was significantly reduced after treatment with a LMWF-5-FU combination. In HCT116 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through the 1) induction of cell cycle arrest in the S phase and 2) late apoptosis mediated by the Jun-N-terminal kinase (JNK) signaling pathway. In Caco-2 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through both c-mesenchymal–epithelial transition (MET)/ Kirsten Rat Sarcoma virus (KRAS)/ extracellular signal-regulated kinase (ERK) and c-MET/ phosphatidyl-inositol 3-kinases (PI3K)/ protein kinase B (AKT) signaling pathways. Moreover, LMWF enhanced the suppressive effects of 5-FU on tumor cell migration through the c-MET/ matrix metalloproteinase (MMP)-2 signaling pathway in both HCT116 and Caco-2 cells. Our results demonstrated that LMWF is a potential complementary therapy for enhancing the efficacies of fluoropyrimidine-based chemotherapy in colorectal cancers (CRCs) with the wild-type or mutated KRAS gene through different mechanisms. However, in vivo studies and in clinical trials are required to validate the results of the present study.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

In situ imaging of molecular markers on a physical chromosome is an indispensable tool for refin-ing of genetic maps and validation genome assembly at the chromosomal level. Despite tremen-dous progress in genome sequencing the plant genome assembly at chromosome level still remain a challenge. Recently developed optical and Hi-C mapping aim to assist in genome assembly. For high-confidence in the genome assembly at chromosome level more independent approaches will be required. The present study aimed to refined an ultrasensitive Tyr-FISH technique and to de-velop a reliable and easy method for in situ mapping of a short unique DNA sequences on plant chromosomes. We have carefully analyzed the critical steps of the Tyr-FISH technique to find out the reasons for the failures of using the method. It has been shown that successful visualization of marker/gene depends significantly on method of chromosome slide preparation, probe design and labelling, high stringency washing. Appropriate adjustment of these steps allowed us to detect a short DNA sequence of 1.7Kb with a frequency of 51.6%. Based on our results, we developed a reliable and simple protocol for dual-color Tyr-FISH visualization of short unique DNA sequences on plant chromosomes. New protocol allows more accurate determination of the physical distance between markers and can be applied for faster integration of genetic and cytogenetic maps.

The emotional dimension in education has become increasingly important in recent decades. Enhancing the emotional dimension of prospective teachers in science subjects is higher education stuff responsibility. The implementation of active methodologies could modify the traditional student-teacher roles that are encouraged by the educational policies implemented in the Bologna Process. The principal aim of this work is to describe a Project Based Learning methodology and to introduce it as potential resource for the emotional and cognitive improvement of 19 prospective primary teachers enrolled in a scientific subject. This is a qualitative study with a transversal sustainability approach in the context of a research line focused on Higher Education for Sustainable Development. A questionnaire was designed and filled by the students at two different times, before and after implementation of the activity. The initial feedback from students was surprisingly enthusiastic by the fact that they were working with rockets, despite of this is not a common emotion in the science field. The results show the emotional improvement of prospective teachers after the implementation. It is concluded that a correct science education is necessary during the training of teachers taking into account their emotional dimension and the social repercussion due to the future transmission.

This paper summarizes over a decade of collaborative eco-archaeological research along the central coast of California involving researchers from the University of California, Berkeley, tribal citizens from the Amah Mutsun Tribal Band, and California Department of Parks and Recreation archaeologists. Our research employs remote sensing methods to document and assess cultural resources threatened by coastal erosion and geophysical methods to identify archaeological deposits, minimize impacts on sensitive cultural resources, and provide tribal and state collaborators with a suite of data to consider before proceeding with any form of invasive archaeological excavation. Our case study of recent eco-archaeological research developed to define the historical biogeography of threatened and endangered anadromous salmonids demonstrates how remote sensing technologies help identify dense archaeological deposits, remove barriers, and create bridges through equitable and inclusive research practices between archaeologists and the Amah Mutsun Tribal Band. These experiences have resulted in the incorporation of remote sensing techniques as a central approach of the Amah Mutsun Tribal Band when conducting archaeology in their traditional territories.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

Nowadays, mindfulness-based interventions (MBI) have experimented a remarkable development of studies among childhood and adolescent interventions. For this reason, dispositional mindfulness (DM) measures for children and adolescents have been developed to determine the effectiveness of MBI at this age stage. However, little is known about how key elements of DM (f. e., cognitive de/fusion or experiential avoidance that both conform psychological inflexibility) are involved in the mechanisms of the children and adolescents’ mental health outcomes. This research examined the mediating effect of cognitive fusion between DM and anxiety and other negative emotional states in a sample of 318 Spanish primary-school students (aged between 8 and 16 years, M=11.24, SD=2.19, 50.8% males). Participants completed the AFQ-Y, which is a measure of psychological inflexibility that encompasses cognitive defusion and experiential avoidance; CAMM (DM for children and adolescents), PANAS-N (positive and negative affect measure for children, the Spanish version of PANASC), and STAIC (an anxiety measure for children). The study accomplished ethical standards. As MBI relevant literature has suggested, cognitive defusion was a significant mediator between DM and symptoms of both negative emotions and anxiety in children and adolescents. However, experiential avoidance did not show any significant mediating relationship. Probably, it is needed improvement of the assessment of experiential avoidance. MBI programs for children and adolescents may include more activities for reducing the effects of the cognitive defusion on their emotional distress.

In this paper, a new class of q-Hermite based Frobenius type Eulerian polynomials is introduced by means of generating function and series representation. Several fundamental formulas and recurrence relations for these polynomials are derived via different generating methods. Furthermore, diverse correlations including the q-Apostol-Bernoulli polynomials, the q-Apostol-Euler poynoomials, the q-Apostol-Genocchi polynomials and the q-Stirling numbers of the second kind are also established by means of the their generating functions.

We introduce a new kind of extended Hermite-based Frobenius type Eulerian polynomials and then derive diverse explicit and implicit summation equations including some symmetric formulas by utilizing series manupulation method. Multifarious summation formulas and identities given earlier for some well known polynomials such as Eulerian polynomials and Frobenius type Eulerian polynomials are generalized.

In this article, a new class of $q$-Hermite based unified Apostol type polynomials is introduced by means of generating function and series representation. Several important formulas and recurrence relations for these polynomials are derived via different generating methods. We also introduce $q$-analog of Stirling numbers of second kind of order $\nu$ by which we construct a relation including aforementioned polynomials.

The recovery of precious metals is a project with both economic and environmental significance. In this paper, it presents how to use bacterial mineralization to selectively recover gold from multi-ionic aqueous systems. The Bacillus licheniformis FZUL-63, separated from a landscape lake in FuZhou University, was shown to selectively mineralize and precipitate gold from coexisting ions in aqueous solution. The removal of Au(III) was almost happened in first hour, and FTIR data show that the amino, carboxyl and phosphate groups on the surface of the bacteria are related to the adsorption of gold ions. XPS results implied that Au(III) ions are reduced to monovalent, and then the Au(I) was adsorbed on the bacterial surface at the beginning stage(first hour). XRD results showed the gold biomineralization began about 10 hours after the interaction between Au(III) ions and bacteria. The Au(III) mineralization has been rarely influenced by other co-existing metal ions. TEM analysis shows the gold nanoparticles are polyhedral structure with a particle size of ~20 nm. The Bacillus licheniformis FZUL-63 could selectively mineralize and recover 478 mg/g(dry biomass) gold from aqua regia-based metal wastewater through four cycles. It could be of great potential in the practical application.

Infiltration based stormwater best management practices bring considerable economic, social and ecological benefits. Controlling stormwater quantity and quality are primarily important to prevent urban flooding and minimizing loads of pollutants to the receiving waters. However, there have been growing concerns about how the traditional design approach contributes to the failure of infiltration based BMP’s that have caused flooding, ponding, prolonged movement of surface water, and frequent clogging, etc. Many of these problems were due to the fact that the current design approaches of stormwater BMP’s only focus on surface hydrology and give little or no attention to the underline subsoil permeability rate and other constraints during the design and sizing process. As a result, we are exhibiting many newly constructed infiltration based BMP’s are failing to function well. This paper presents and demonstrates a new paradigm shift in designing infiltration-based stormwater BMP’s by combining subsurface hydrology and undelaying native soil constraints to establish acceptable criteria for sizing infiltration based BMPs.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

We examine how external triggers, including the digital imperative and the need for more sustainable resource and stakeholder employment, spark the development of transformative sustainable business models. Drawing on the resource-based view and the shared value approach we conceptualize a multifaceted framework that helps to identify key determinants and coherent layers of transformative sustainable businesses models. Our theoretical arguments integrate recent research findings on external dynamics, such as digital technological advances and rising global competitive dynamics, with internal capabilities on both the organizational and the individual level, allowing for a more complete understanding of transformative potentials on the firm level. We propose that key determinants of sustainable transformative business models adhere to both, innovative value-creating reconstructionist and sustainable shared-value logic, and include elements such as co-creation with customers, usage-based pricing, agile and adaptive behavior, closed-loop resource employment, asset-sharing, and collaborative business ecosystems. At the same time, organizational, economic, and environmental layers encompassing sustainable business models need to be both horizontally and vertically coherent to unfold their full potential.

Interfaces between layers in 3D-printed elements produced by extrusion-based material deposition were investigated on both macro- and micro-scales. On the macro-scale, compression and bend tests were performed on two 3D-printable cement-based compositions (3PCs), namely C1 and C2. The influences of binder composition and time interval between layers on layer-interface strength were critically analyzed. In the context of additive manufacturing, the optimized composition C2, containing pozzolanic additives, exhibited mechanical performance superior to that of the mixture with Portland cement as the sole binder. In particular, Mixture C2 showed a less pronounced decrease in interface tensile strength. Even for time intervals between depositions of two layers as long as 1 day the loss in corresponding flexural strength was below 25%, as compared with C2 specimens tested in the perpendicular direction. In contrast, the decrease in flexural strength measured for C1 specimens amounted to over 90% for the same set of parameters. Higher porosity at the interfaces of the printed concrete layers was identified as the cause for the lower interface strengths of C1. Microscopic observations supported the findings of the macroscopic investigations. While a pronounced recovery (“self-healing”) of the porous, discontinuous interlayers was observed with increasing age for Mixture C2, in case of C1 the filling products grown in the porous interlayer were found to be non-strengthening.

In this paper, we introduce a general family of Lagrange-based Apostol-type Hermite polynomials thereby unifying the Lagrange-based Apostol Hermite-Bernoulli and the Lagrange-based Apostol Hermite-Genocchi polynomials. We also define Lagrange-based Apostol Hermite-Euler polynomials via the generating function. In terms of these generalizations, we find new and useful relations between the unified family and the Apostol Hermite-Euler polynomials. We also derive their explicit representations and list some basic properties of each of them. Some implicit summation formulae and general symmetry identities are derived by using different analytical means and applying generating functions.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

Since their discovery in 1993, numerous microRNAs (miRNAs) have been identified in humans and other eukaryotic organisms, and their role as key regulators of gene expression is still being elucidated. It is now known that miRNAs not only play a central role in the processes that ensure normal development and physiology, but they are often dysregulated in various diseases. In this review, we present an overview of the role of miRNAs in normal renal development and physiology, in maladaptive renal repair after injury, and in the pathogenesis of renal parenchymal diseases. In addition, we describe methods used for their detection and their potential as therapeutic targets. Continued research on renal miRNAs will undoubtedly improve our understanding of diseases affecting the kidneys and may also lead to new therapeutic agents.

Cycling reliability is crucial for Si-based materials due to severe volume change during cycles that results in the fast capacity fading. Though the binder only occupies a very low amount of the total mass of anodes, it is proved to perform a key parameter to improve the cycle performance of Si-based anodes. Because they are eco-friendly and cost saving, water-based binders styrene-butadiene rubber (SBR) and carboxymethyl cellulose (CMC) are regarded as the better binder to substitute Poly (vinylidene difluoride) (PVDF) as the binder for Si-based anode. In this study, the anodes are fabricated by simply mixing the active materials (naso Si, graphite and conductive additive) together and using the mixture of SBR and CMC as a binder. The results showed that the retention capacities of the anodes are more than 440 mAh/g after 400 cycles. It indicates that it is an easy and simple way to make high performance anodes.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

This study explores the application of multi-real option valuation integrated with Security Token Offerings (STO) as an innovative approach to real estate project financing, using the Aspen Resort development as a case study. Traditional Discounted Cash Flow (DCF) methods often fall short in capturing the dynamic nature of long-term real estate projects, particularly in adapting to market uncertainties and changes. This research addresses these limitations by employing a multi-real option model, which allows for flexibility in decision-making by evaluating various investment phases through the binomial lattice framework. The analysis demonstrates that the STO-based project financing (STO-PF) model, when applied to the Aspen Resort, provides superior financial outcomes and strategic advantages compared to conventional DCF methods. Key findings reveal that the STO-PF model enhances liquidity, democratizes investment access, and offers improved risk management through digital platforms. The results showcase how integrating STOs with real option models quantifies the project’s expanded net present value (ENPV), thereby optimizing investment decisions under volatile market conditions. This study not only contributes theoretically to the literature on STO financing but also provides practical implications for developers seeking flexible and innovative financing solutions in real estate. Future research should explore the applicability of STOs across diverse market conditions and regulatory environments, including the integration of emerging technologies such as AI and blockchain for enhanced financial modeling.

Background/Objectives: The study outlines the systematic approach taken to develop and apply a robust NLP algorithm to determine the necessity of performing routine imaging after central venous catheter (CVC) placement and removal, based on the incidence of pneumothorax observed. Methods: Electronic health records from four university hospitals in Salzburg, Austria, on X-rays performed between 2012 and 2021 following CVC procedures, were analyzed using an NLP al-gorithm. The algorithm detected a pneumothorax in radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, missing data, or unrelated follow-up X-rays. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. Results: The study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was identified in 106 cases after insertion (1.3%) and in 3 cases after removal (0.02%). The Chi-square test showed no statistically significant difference in pneumothorax incidence between insertion and removal (p = 0.4797), in-dicating no substantial difference in pneumothorax risk between these procedures. Conclusions: Given the lack of significant difference in pneumothorax incidence between CVC insertion and removal, and the established consensus that routine imaging is unnecessary after CVC removal, these findings indicate that routine imaging following CVC insertion may also be unnecessary in asymptomatic patients.

Ephrin type A receptor 2 (EphA2) binds to membrane-bound ligands, ephrin A1, A2, and A5, eliciting bidirectional signaling. This signaling regulates many physiological processes, such as tissue development, homeostasis, and regeneration. The dysregulation of the EphA2-ephrins axis contributes to various diseases, including cancers. The high expression of EphA2 is observed in various cancers, which promotes cancer malignancy, whereas its levels are relatively low in most normal adult tissues. Therefore, EphA2 is a promising target for cancer therapy. In this study, we developed anti-human EphA2 monoclonal antibodies using the Cell-Based Immunization and Screening method. Among them, a clone Ea₂Mab-7 (IgG₁, κ) exhibited a high affinity and sensitivity in flow cytometry. The dissociation constant values of Ea₂Mab-7 for CHO/EphA2 and MDA-MB-231 cells were determined as 7.7 × 10⁻⁹ M and 2.1 × 10⁻⁹ M, respectively. Furthermore, Ea₂Mab-7 can detect endogenous EphA2 in western blot and immunohistochemistry. Therefore, the Ea₂Mab-7 is highly versatile for basic research and is expected to contribute to clinical applications, such as antibody therapy and tumor diagnosis.

The aim of the study was to analyze the functional properties of newly obtained films based on sodium alginate and lecithin with the addition of antioxidant-rich coffee extracts, and to verify their potential as safe edible food packaging materials. In our study we developed alginate-lecithin films enriched with green or roasted coffee bean extracts. The roasting process of coffee beans had a significant impact on the total phenolic content (TPC) in the studied extracts. The highest value of TPC (134.86 mg GAE/L) as well as antioxidant activity (AA) (0.88 mM T/L) was observed for the extract of light roasted coffee beans. Films with addition of medium roasted coffee extracts and baseline films had the highest tensile strength (21.21±0.73 N). The addition of coffee extract improved the barrier properties of films against UV light by a decrease in transmittance values (200-400 nm), regardless of the type of extract added. Studies on Caco-2, HepG2 and BJ cells shown that digestated films were non-cytotoxic materials (100-0,1 ug/mL) and had no negative effect on cell viability, an increase was noted for all cell lines, the highest after 48-hours in dose 1 ug/mL for film with medium-roasted coffee (194.43±38.30) for Caco-2. The tested films at 20% digestate concentrations demonstrated the ability to reduce nitric oxide (NO) production in the RAW264.7 cell line by 25 to 60% compared to the control. Each of the tested films with coffee extracts had growth inhibitory properties towards selected species of bacteria.

There is a strong global demand for sulfate- free personal cleansing products. The objective of sulfate-free personal cleansing technology should not be aimed at the absence of “sulfate” wording in the ingredient listing, but on the true benefits to both people and the planet, including but not limited to safety, mildness, and sensory for the people perspective and renewability, low carbon footprint, low water footprint, biodegradability &amp; sustainability for the planet perspective. In addition, some surfactants or their precursors contain 1,4-dioxane as a by-product in their manufacturing, which is a major safety concern. In this paper, several commonly used sulfate-free surfactants are compared based on a variety of desirable criteria from both the people and the planet perspective. There is also an important trend promoting green manufacturing processes using natural, renewable, safe, biodegradable &amp; sustainable feedstocks to mitigate the challenges presented by climate change. It is concluded that the best options for the sulfate-free personal cleansing products of the future are glutamates for skin cleansing and alaninates for hair cleansing.

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backstepping sliding mode control (BSMC) approach to achieve high-accuracy tracking control. In particular, the model uncertainties, including nonlinearities, parameter perturbations and external disturbances are analyzed and treated together as a lumped disturbance. Then, a fourth-order compensation function observer (CFO) is constructed, which fully utilizes the system state information to accurately estimate the lumped disturbance. On this basis, the estimate of the lumped disturbance is incorporated into the design of an backstepping sliding mode controller, allowing the control system to compensate for the disturbance effect. The stability of the closed-loop control system under the CFO and BSMC is rigorously proven through the use of Lyapunov theory, which guarantees that all the tracking error signals converge exponentially to the origin. Comparative simulations are carried out to show the effectiveness and efficiency of the proposed approach, i.e., compared with PID and ESO-based BSMC methods, the tracking accuracy is improved by 99.93% and 99.76%, respectively.

Chemical plants are characterized by the management and operation of various hazardous materials; thus, accident risk is inherently high. Nevertheless, the incidence of chemical accidents in Korea has effectively stabilized, with no marked increase attributable to the implementation of systematic safety management procedures based on process safety management system principles. In June 2023, the Ministry of Employment and Labor in Korea unveiled the Serious Injury and Fatality (SIF) report, meticulously cataloging 4,432 instances of grave accidents spanning six years (2016–2021). The compilation comprises 1,834 and 2,574 cases within the manufacturing and affiliated sectors and the construction sector, respectively. This study provides an overview of each accident, and elucidates the causes, and recommends preventative strategies. The objective was to galvanize companies across various industrial landscapes and fortify their intrinsic safety management protocols by using accident data delineated per sector. Sixteen specific accident cases from the SIF report, all of which occurred in facilities regulated by process safety management (PSM), were investigated. Technical analysis was conducted, focusing on explosion accidents within the organic catalyst packaging process, notable for their unique patterns of occurrence. This analysis extended to an examination of the reproducibility of such accidents. Employing two system analysis methodologies, system theoretic accident model and process and barrier-based systemic cause analysis technique, this study provides insights into the causative factors of the accidents and formulates comprehensive strategies for the prevention of future accidents. The findings will contribute significantly to reducing the risk of accidents within the catalyst packaging domain and assist in the development of robust recurrence prevention measures.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

Community-Based Disaster Risk Reduction (CBDRR) plays a vital role in strengthening the resilience of local communities against both natural and human-made disasters. This paper seeks to examine the role of CBDRR in enhancing the resilience of local communities to both natural and man-made disasters. It underscores the critical importance of active community involvement across all stages of disaster management, from preparedness and mitigation to response and recovery. The paper also explores the key factors that contribute to the successful implementation of CBDRR, including institutional backing, capacity development, inclusive strategies, and the use of modern technologies. Additionally, it discusses the challenges and opportunities in building strong partnerships between local communities and external stakeholders, which are crucial for ensuring the sustainability of these efforts. Finally, the paper offers recommendations aimed at improving the effectiveness of CBDRR initiatives, drawing on insights gained from past disaster experiences. The findings of this study point to the importance of continuous monitoring and evaluation of CBDRR programs to maintain their relevance and ensure their effectiveness as risks evolve. Incorporating lessons learned from past disasters into future planning also plays a crucial role in helping communities enhance their resilience over time. Ultimately, by encouraging collaboration between communities, governments, and various organizations, CBDRR lays the foundation for a comprehensive disaster management framework that is equipped to meet future challenges and supports long-term sustainable development.

In recent years, the demand for natural and synthetic zeolites has surged due to their distinctive properties and myriad industrial applications. This research aims to synthesise crystalline zeolites by co-recycling two industrial wastes: salt slag (SS) and rice husk ash (RHA). Salt slag, a problem-atic by-product of secondary aluminium smelting, is classified as hazardous waste due to its reac-tive and leachable nature, though it is rich in aluminium. Conversely, RHA, an abundant and cost-effective by-product of the agro-food sector, boasts a high silicon content. These wastes were utilised as aluminium and silicon sources for synthesising various zeolites. The study examined the effects of temperature, ageing time, and sodium concentration on the formation of different zeolite phases and their crystallinity. Results indicated that increased Na+ concentration favoured SOD zeolite formation, whereas LTA-type zeolite formation was promoted at higher temperatures and extended ageing times. The formation range of the different zeolites was defined and supported by a crystallographic, microstructural, and morphological analysis. Additionally, the thermal be-haviour of the zeolites was investigated. This work underscores the potential to transform indus-trial waste, including hazardous materials like salt slag, into sustainable, high-value materials, fostering efficient waste co-recycling and promoting clean, sustainable industrial production through cross-sectoral industrial symbiosis.

The United Nations' sustainable development goals have underscored the importance of enhancing supply chain sustainability while appropriately distributing orders. This study proposes a novel framework involving Z-number, game theory, indifference threshold-based attribute ratio analysis (ITARA), and combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations; enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are applied to assess the objective weight of criteria and the ranking of assessed order allocations, respectively. Afterwards, a case study of China’s company is illustrated the utility of the proposed framework and inform their decision-making process regarding the orders to be assigned to the assessed suppliers.

Digital transformation is revolutionizing inventory management practices within supply chains, offering unprecedented opportunities and challenges for businesses worldwide. This study explores the impact of digital technologies on inventory management, focusing on the adoption of IoT sensors, RFID tags, AI-driven analytics, and cloud-based systems. Through a qualitative research approach encompassing interviews with industry professionals and secondary data analysis, the study examines key themes including enhanced inventory visibility, improved accuracy, advanced demand forecasting, and streamlined supply chain collaboration. Findings reveal that digital technologies significantly enhance inventory visibility by providing real-time tracking and data integration capabilities. This facilitates accurate inventory monitoring and decision-making, reducing errors and optimizing inventory levels to meet fluctuating demand effectively. AI-driven analytics and machine learning models emerge as pivotal tools for predictive demand forecasting, enabling businesses to anticipate market trends and adjust inventory strategies accordingly. Additionally, cloud-based systems and electronic data interchange (EDI) foster improved communication and coordination among supply chain partners, enhancing overall operational efficiency. Despite these benefits, challenges such as system integration complexities, high implementation costs, data quality management, cybersecurity risks, and regulatory compliance issues are prevalent. Successful adoption of digital inventory management solutions requires strategic planning, investment in technology infrastructure, and organizational readiness to navigate these challenges effectively. This study contributes to the understanding of how digital transformation reshapes inventory management practices, offering insights for researchers and practitioners alike to leverage digital technologies for enhanced supply chain performance and competitive advantage

We present a novel benchmarking methodology for Large Language Models (LLMs) to evaluate their susceptibility to hallucinations, thereby determining their reliability for real-world applications involving greater responsibilities. This method, called Deception-Based Benchmarking, involves testing the model with a task that requires composing a short paragraph. Initially, the model performs under standard conditions. Then, it is required to begin with a misleading sentence. Based on these outputs, the model is assessed on three criteria: accuracy, susceptibility, and consistency. This approach can be integrated with existing benchmarks or applied to new ones, thus facilitating a comprehensive evaluation of models across multiple dimensions. It also encompasses various forms of hallucination. We applied this methodology to several small opensource models using a modified version of MMLU, DB-MMLU1 . Our findings indicate that most current models are not specifically designed to self-correct when the random sampling process leads them to produce inaccuracies. However, certain models, such as Solar-10.7B-Instruct, exhibit a reduced vulnerability to hallucination, as reflected by their susceptibility and consistency scores. These metrics are distinct from traditional benchmark scores. Our results align with TruthfulQA, a widely used benchmark for hallucination. Looking forward, DB-benchmarking can be readily applied to other benchmarks to monitor the advancement of LLMs.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

Infectivity assays are the key analytical technology for development of manufacturing processes for virus-based therapeutics. Here, we introduce a novel assay format that utilizes label-free bright field images to determine the kinetics of infection-dependent changes in cell morphology. In particular, cell rounding is directly proportional to the amount of infectious virus applied, enabling rapid determination of viral titers in relation to a standard curve. Our kinetic infectious virus titer (KIT) assay is stability-indicating and, due to its sensitive readout method, provides results within 24 hours post-infection. Compared to traditional infectivity assays, which depend on a single readout of an infection endpoint, cumulated analysis of kinetic data by a fit model results in precise results (CV &lt; 20%) based on only three wells per sample. This approach allows for a high throughput with ~400 samples processed by a single operator per week. We demonstrate the applicability of the KIT assay for VSV-GP and NDV, but it can potentially be extended to a wide range of viruses that induce morphological changes upon infection. The versatility of this assay, combined with its independence from specific instruments or software, makes it a promising solution to overcome the analytical bottleneck in infectivity assays within the pharmaceutical industry and as a routine method in academic research.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Polymer-based (nano)composite foams, especially those having a microcellular structure, containing suitable conductive (nano)fillers, have been shown to act as good shielding materials, hence coming as promising to act as shields in electronic devices, limiting/avoiding electromagnetic interference (EMI) pollution. Nevertheless, due to their high (micro)structural complexity, related to their multiphase nature, there is still a lot of unawareness behind the shielding mechanisms acting in these materials, coming as a necessity to study their microstructure-cellular/porous structure-properties relations, especially in terms of their electrical conductivity and EMI shielding efficiency (EMI SE). To target and control the electrical conductivity through a controlled distribution/dispersion of conductive nanofillers, single or combined (nanohybrids), as there is a direct relation between electrical conductivity and EMI SE, as well as the main EMI shielding mechanisms working on the developed microcellular structure of the foams, which are expected to be absorption-dominated or absorption/multiple reflection-based, are two of the main objectives when combining polymer foaming with the addition of conductive nanofillers. The present review work considers the recent developments and trends on polymer-based foams containing conductive nanofillers, especially carbon-based such as carbon nanotubes or graphene-based materials, as well as similar porous structures created using trending technologies such as 3D printing, for EMI shielding applications. It is divided in different sections, starting with the strategy of microcellular foaming to develop polymer-based foams with enhanced EMI shielding characteristics, especially focusing on technologies and methods using supercritical CO2 (sCO2); continuing with the use of polymer foams as templates to synthesize carbon foams with improved EMI shielding performance for high use temperature applications; the recent strategy of combining different functional (nano)fillers to create hybrid nanofillers/nanohybrids to be used at lower amounts as conductive fillers in polymer foams; the control and selective distribution of the nanofillers in terms of favoring the formation of a more effective conductive network and hence promote the enhancement of the EMI SE; the very recent use of computational approaches to tailor the EMI shielding properties of composite foams; and the very trending possibility of creating cellular components with varied porous structures from these materials using 3D printing; to finish, some future perspectives are discussed.

Debates on climate change challenges around the world are moving in favour of nature-based solutions (NBS) as potential mitigation and adaptation options. Mangroves and associated blue carbon ecosystems are nature-based solutions against sea level rise and other coastal hazards. As such, protecting the existing mangrove forests and restoration of the degraded areas is integral in this aspect. However, conventional mangrove restoration approaches in high-energy areas result in poor performance due to the removal of seedlings by waves. Here, we assessed the efficacy of using modified Riley Encasement Methods in the restoration of eroding intertidal areas of Gazi Bay, Kenya. Vegetation and soil baseline data were collected in 49 square plots of 100m2; established along belt transects perpendicular to the waterline. The following mangrove vegetation data was collected; species composition, tree height (m), and stem diameter (cm); from which the importance value index (IV), basal area and standing density (stems/ha) were derived. Sediment cores were made in the center of each square plot for carbon and grain size analysis. Mangrove (Rhizophora mucronata) planting adopted a randomized complete block design (RCBD) in which the planting area was divided into three blocks (A, B, C). Within each block, treatments (bamboo and different-sized PVC pipes) were randomly assigned locations. Results show that seedlings grown within PVC encasements had high survival and growth rates, surpassing bamboo and control groups. This implies that PVC pipes offer better protection from external factors. These findings are critical in highlighting potential enhancements for the effectiveness of PVC pipes in mangrove restoration. They also reinforce on the need to consider the incorporation of PVC encasements as an alternative mangrove restoration technique in high-energy locations.

Reducing meat consumption is better for the environment. Unfortunately, commercial plant-based meat substitutes are often more expensive than meat and thus have not seen widespread adoption. This paper analyzes commercially-available spirulina, soy, pea, and brown rice protein isolates characteristics to provide data for non-meat protein cost reductions. Thermal and rheological properties, viscosity, density, and particle size distribution are analyzed for further study on alternative protein-based food processing. Differential scanning calorimetry analysis produced dry amorphous-shaped curves and paste curves with a more distinct endothermic peak. Extracted linear temperature ranges for processing in food production for spirulina was 70-90ºC; soy protein was 87-116ºC; pea protein was 67-77ºC; and brown rice protein was 87-97ºC. Viscosity analysis determined each protein material was shear-thinning and that viscosity increased with decreased water concentration, with rice being an exception to the latter trend. The obtained viscosity range for spirulina was 15,100-78,000cP; soy protein was 3,200-80,000cP; pea protein was 1,400-32,700cP; and brown rice protein was 600-3,500cP. The results indicate that extrusion is a viable method for further processing of the protein isolates as this technique has a large temperature operating range and variable screw speed. Data provided here can be used to make single or multi-component protein substitutes.

A small but growing body of literature suggests that outdoor experiences during childhood affect environment-related behaviors in adulthood. However, research on the magnitude of the effect (effect size) of outdoor experience on learners’ behaviors remains scarce. In this study, we explore the extent to which outdoor experiences associate with environmentally responsible behaviors. Our sample consisted of 143 ninth- and tenth-grade students living on a Greek island. The data were collected using a properly adjusted environmental literacy instrument. Two different methodological pathways, i.e., a quasi-experimental approach and correlation analysis, were used to analyse the data. A tentative variable representing the frequency and intensity of students’ experiential contact with nature was found to be the strongest available predictor of their self-reported pro-environmental behaviors. The findings of this study support the significance of outdoor, experiential learning during childhood in shaping individuals’ environmental behaviors.

Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

The possibility of deep radiochemical purification of Li2CO3 has been examined in the frame of the purification program of AMoRE collaboration. In this experiment, commercial Li2CO3 was converted into LiNO3. Co-precipitation with inorganic salt-based carriers followed by membrane filtration and sorption using MDM inorganic sorbent methods were tested for the removal of alkaline-earth and transition metals, potassium, magnesium, aluminum, uranium, thorium, and radium. The calcium molybdate-based carrier was the most efficient for removing Th, U, and K. Subsequently, the radium, calcium, and barium contaminations were removed with MDM sorbent. After the impurities’ removal, the final Li2CO3 product was synthesized with NH4HCO3 sludge. The separation factors were derived by means of ICP-MS and HPGe analyses of the initial material, intermediate and final products. The study showed the optimum conditions of co-precipitation and sorption to reach a high yield and radiopurity of lithium carbonate used for low radioactive background experiments.

Climate and land-use changes have been contributing to increase the occurrence of extreme wild-fires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the effects of land-use change on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini index) between 1984 and 2019, which was combined with 2018 carbon stock data (above- and below-ground), and a landscape typology re-sulting from cluster analysis over land-use composition, diversity and configuration, to explore relationships between landscape types and the two major ecosystem services at stake: wildfire reduction and carbon stock. Cross tabulations, logistic and linear regressions were performed on these data and results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt forest plantations and high-hazardous fire regime but hold the highest carbon stock. Shrubland-mixed landscapes were associated with lower carbon stocks but less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agro-forestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes however, this good wildfire performance is achieved at the cost of the poorest carbon stock, de-noting land degradation, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Soybean are vulnerable to drought and temperature increases potentially induced by climate change. The purpose of this study was to determine if the use of cover crops (CC) can influence the gas exchange potential of glyphosate tolerant soybean when the vapor pressure deficit (Vpd) increases. This two years study was conducted in an open experimental field comprising Direct Seeding plots without CC (DS) or with CC (DSCC). Stomatal conductance (Gs) was measured five times on the same identified leaves following GBH application. These leaves were then collected in order to observe the stomata (size, density and stomatal index) with a scanning electron microscope and to perform measurements on foliar traits (venation density, width of midrib). The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CC promotes phenotypic change in soybean leaves (more elaborate venation and a higher stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in DSCC plots compared to DS ones along with similar Vpd values. This study shows that the benefits of using CC can be observed through the morphological development strategies of the crop plants and their higher tolerance to drought.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent based simulator will enable the policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify the vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM ) which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population, allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of Covid-19 are used to validate the proposed model. However, it is highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

Autoantibodies against type 1 interferons (IFN-I) are highly specific marker for type 1 autoimmune polyglandular syndrome (APS-1). Moreover, determination of antibodies to IFN-ω and IFN-α2 allows a short-term diagnosis in patients with isolated and atypical forms of APS-1. In this study, a comparison of three different methods, namely, multiplex microarray-based, cell-based and enzyme-linked immunosorbent assays for detection of antibodies against omega-interferon and alpha2-interferon was carried out. A total of 206 serum samples from adult patients with APS-1, APS-2, isolated autoimmune endocrine pathologies, non-autoimmune endocrine disorders and healthy individuals were analyzed. In the APS-1 patient cohort (n=18), there was good agreement between the results of anti- IFN-I antibody tests performed by three methods, with 100% specificity and sensitivity for microarray-based assay. Although only the cell-based assay can determine the neutralizing activity of autoantibodies, the microarray-based assay can serve as a highly specific and sensitive screening test to identify anti- IFN-I antibody positive patients.

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the expansion of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for static and dynamic scenarios, regardless of the number of stations. Moreover, the performance difference is amplified as the number of stations increases, with DQN and DDPG showing a 27% increase in throughput with 50 stations compared to BEB. Furthermore, DQN and DDPG presented similar performances.

Acute low back pain (LBP) stands as a leading cause of activity limitation and work absenteeism, and its associated healthcare expenditures are expected to become substantial when acute LBP develops into a chronic and even refractory condition. Therefore, early intervention is crucial to prevent progression to chronic pain whose management is particularly challenging and for which the most effective pharmacological therapy is still controversial. Current guideline treatment recommendations vary and are mostly driven by expertise with opinion differing across different interventions. Thus, it is difficult to formulate evidence-based guidance when relatively few randomized clinical trials did explore the diagnosis and management of LBP while employing different selection criteria, statistical analyses, and outcome measurements. This narrative review aims to provide a critical appraisal of current acute LBP management by discussing the unmet needs and areas of improvement from bench-to-bedside and proposes multimodal analgesia as the way forward to attain an effective and prolonged pain relief and functional recovery in patients with acute LBP.