Modeling Tools Research Articles

3D Hybrid modeling for the ultrasonic phased array inspection of porosity in heavy plates: Simulation and experimental validation

Quantitative NDT modeling and simulation tools are important in design and fabrication of ultrasonic phased array probes with optimum characteristics and reduce the number of experimental efforts and development time. In order to achieve high accuracy (Amplitude deviation < 1 dB) with a low computational time in the simulation, the modeling tools require not only the high-frequency approximation of the ultrasonic wave propagation but also the high-precision electro-acoustic coupling model. This paper describes a computationally efficient 3D time-dependent hybrid model for the simulation of ultrasonic phased array inspection of porosity in heavy plates. This method combines the results of both 3D finite element analysis (PZFLex-FEA) and the semi-analytical ultrasonic simulation “Ray-Tracing”. As a first step, we demonstrate the FEA modeling of an ultrasonic phased array probe, which includes the full 1-3 piezocomposite design with the arrangement of array elements, conducting electrodes, multi-phase backing material, acoustical matching layer and electrical circuit of a 50 Ohm coaxial cable. In the current FEA simulation, we considered the acoustical and electrical cross-talk between the adjacent elements of an ultrasonic array sensor. In the second step, the FEA-simulated ultrasonic pulse-echo signal and its frequency spectrum of an array element in the farfield region are further used as the reference input signal in the CIVA-UT software to simulate the imaging of porosity in heavy plates, quantitatively. The design of the 3D wedge-geometry of the application specific array sensor is modeled and optimized using the CIVA raytracing and multi-phase backing models. The real excitation pulse of the industrial ultrasonic testing equipment “ROMIS (ROSEN Modular Inspection System)” is also included in the simulation to achieve high accuracy in the current hybrid modeling. The dependency of the detectability of the porosity on the frequency of the array sensor and the active element group so-called “Virtual Probe” are quantitatively analyzed. Finally, the 3D hybrid model simulated Time Corrected Gain (TCG) curves of the porosity like defects in the reference steel plates are compared with the real experiments and a good quantitative agreement is achieved.

Historical biogeography of the Saharan horned viper enlightens past dynamics of hyperarid desert habitats

Hyperarid habitats comprise the most dominant and harsh portion of the Sahara Desert, yet how local biotas have responded to paleoclimatic shifts and landscape heterogeneity remains unclear. Here, we assess the historical biogeography of Cerastes cerastes, a conspicuous but poorly known vertebrate inhabiting the hyperarid Sahara, to understand the links between intermittent and stable climatic suitability and patterns of genetic diversity and differentiation in these extreme environments. We combine phylogeographic and ecological modelling tools using a comprehensive species dataset, which includes DNA samples (n = 62) and occurrence records (n = 557) covering most of its range. We show that paleoclimatic oscillations were major evolutionary and biogeographic drivers in C. cerastes, which exhibits different levels of intra-specific diversity and allopatric lineage structuration across the desert. High lineage richness in the north-western Sahara contrasts with an overall lack of genetic diversity throughout the central and eastern Sahara. Climatic refugia along the desert’s periphery and around mountain patches harbour single endemic lineages, stressing the links between stable climatic conditions and population isolation and divergence. Increasing climatic suitability during historical arid phases prompted widespread connectivity across the desert. This study lays the groundwork for better understanding the historical dynamics of the hyperarid Sahara.

Innovative decision support tools for perishable food supply chain management

PurposeWe introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.Design/methodology/approachThis study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.FindingsTaking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.Research limitations/implicationsLimited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.Practical implicationsThe tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.Social implicationsThe proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.Originality/valueThe integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.

Porter Framework vs A More Extended Framework

Michael Porter presents a framework for defining an industry's structure to assist strategic teams in identifying the right strategy to gain a competitive advantage over rivals. This framework is based on two major axioms that determine the industry's structure. The first axiom focuses on the relationship among firms in the value chain, involving a zero-sum or distributive bargaining relationship according to Porter. The second axiom sets out that all firms in an industry are confronted with similar conditions. We defend an alternative axiom in determining the industrial structure. The assumption here is that participants in a value chain are involved in a non-zero-sum or integrative bargaining relationship. The alternative axiom that focuses more on mutual cooperation between participants in the value chain is more appropriate for the long-term prosperity of the company. It provides a better set of factors influencing a greater degree of market penetration and profits in the long run. In this paper, we add two more forces to Porter’s Framework that according to today’s business environment and context will help to create a more complete framework for analyzing the industry. We plan to use and test the framework as a basis for research in the future in order to determine the competitive advantage of businesses operating in an industry but with different means, in other words from brick-and-mortar businesses to totally virtual firms that have flourished over the past years. The modeling tools help in understanding the business potential, whereas, the value chain analysis provides help to understand where the most productive advantage is based.

Tackling Data Scarcity Challenge through Active Learning in Materials Processing with Electrospray

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one‐factor‐at‐a‐time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high‐dimensional features in ML; Surrogate‐based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query‐by‐Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in‐house materials development task ‐ electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers.

Parameterization of H2SO4 and organic contributions to volatile PM in aircraft plumes at ground idle.

Volatile Particulate Matter (vPM) emissions are challenging to measure and quantify, since they are not present in the condensed form at the engine exit plane and they evolve to first form in the aircraft plume and then continue to grow and change as they mix and dilute in the ambient atmosphere. To better understand the issues associated with the initial formation and growth of vPM, a modeling study has been undertaken to examine several key parameters that affect the formation and properties of the vPM that is created in the initial cooling and dilution of the aircraft exhaust. A modeling tool (Aerosol Dynamic Simulation Code, ADSC) that was developed and enhanced over a series of past research projects supported by NASA, DoD's SERDP/ESTCP, and FAA (Wong et al. 2010, 2014, 2015) was used to perform a parametric analysis of vPM. The parameters of fuel sulfur content (FSC), emitted condensable hydrocarbon (HC) concentrations, and the species profile of the HCs were used to construct a computational matrix that framed a wide range of expected parameter values. This computational matrix was executed for two representative commercial aircraft engines at ground idle and results were obtained for distances of 250 m and 1000 m downstream. From prior results, the most significant vPM emissions occur at the lowest power settings, so an engine power condition of 7% rated thrust was used. A primary goal of the parametric study is to develop an updated vPM modeling methodology and also to help interpret data collected in experimental campaigns. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. The aim is to provide additional understanding on how the vPM properties vary with fuel and engine parameters to increase the utility of vPM predictions.Implications: Volatile ParticulateMatter (vPM) is an important contribution to the total PM emitted by aviationengines. While vPM is not currently a part of engine emissions certificationregulations, vPM is used in aviation environmental impact assessments and forair quality modeling in and around airports. Current methods in use, such asFOA (Wayson et al. 2009), were developed before many recent advances inexperimental data acquisition and in understanding of vPM processes. Theparameterization proposed here allows the vPM emission composition and particlenumbers to be estimated in greater detail than current methods. These estimatescan be used to develop inventories and provide a better estimate of total PMemission for most aviation engines. Its use in international regulatory toolscan inform possible future regulatory actions regarding vPM.

Development of bio-based lightweight and thermally insulated bricks: Efficient energy performance, thermal comfort, and CO2 emission of residential buildings in hot arid climates

This study explores the viability of using industrial wastewater sludge (IWWS), generated by the mycelium treatment of dyeing wastewater, for the fabrication of lightweight and thermally insulating bricks. The pulverized IWWS was used at various replacement ratios of 0, 5, 10, 15, and 20 wt% of clay. The mixture was subjected to firing temperatures of 700, 800, and 900 °C. The effect of applying IWWS to the brick characteristics is assessed through an examination of the physico-mechanical and thermal properties. The utilization of IWWS resulted in a significant enhancement in apparent porosity (34.4 %), cold water absorption (25.6 %), boiling water absorption (28.5 %), linear drying shrinkage (5.1 %), and linear firing shrinkage (7.9 %) using 20 % IWWS at 900 °C. Furthermore, the density, compressive strength, and thermal conductivity were reduced as the substitution IWWS ratio increased, reaching up to 1662.9 kg/m3, 11.56 MPa, and 0.198 W/m.K., respectively, using 20 wt% IWWS at 900 °C. The effect of fabricated fired bricks (FBs) on the thermal insulation, CO2 emissions, and energy consumption of residential buildings was tested. This is accomplished by using energy modeling tools, such as Honeybee, Ladybug, and Climate Studio (Grasshopper plugins), in addition to Galapagos for optimizing wall design. The suggested wall design is being used in a communal residential building located in two distinct climatic regions, namely Cairo in Egypt and Jazan in Saudi Arabia. According to the projected mean vote (PMV) and anticipated percentage of discontent (PPD) measures, the utilization of fabricated FBs as wall materials exhibits significant improvements in thermal comfort. Furthermore, they lead to a 6 % and 7 % drop in CO2 emissions in Cairo and Jazan cities, respectively, as well as a significant 38.8 % and 39 % reduction in energy consumption in Cairo and Jazan cities, respectively. Therefore, the use of fabricated FBs demonstrates itself as a feasible and environmentally friendly choice for building construction.

Phytochemical inhibitors from Leucas aspera against the target proteins induced by Trichophyton mentagrophytes using computational techniques.

Dermatophytosis is a cutaneous infection that is able to degrade the keratinized tissues of the animal/human body, like skin, nails, and hair, causing chronic or subacute infection with the contact of some specific fungal strains. Trichophyton mentagrophytes are the most potential fungal pathogen causing dermatophytoses. The present study focuses on computationally based in silico antifungal activity of selected phytocompounds of Leucas aspera (Willd.) Link. against dermatophytic fungus, T. mentagrophytes. Validation and screening of derived phytocompounds is performed using Lipinski rule of five and toxicity test through Protox-II. Five target genes involved in dermatophytosis, induced by T. mentagrophytes are retrieved from the UniProt Database, and the corresponding proteins such as glucan 1,3-beta-glucosidase ARB_02797, Probable class II chitinase ARB_00204, squalene monooxygenase, actin, and ubiquitin are selected for in silico study. Three-dimensional structures of the target protein were computationally determined and validated through modeling tools and techniques due to the lack of validated protein structures in the database. Then, these proteins are used for in silico molecular docking through the AutoDock Vina tool to find out the promising phytocompounds. This study could be utilized in designing more effective drugs against T. mentagrophytes. Based on this work, a plant-based natural alternative can be added to the treatment of dermatophytosis rather than synthetic supplements.

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

Cross-Watershed Leakage of Agricultural Nutrient Runoff

Abstract Agricultural nutrient runoff has been a major contributor to hypoxia in many downstream coastal ecosystems. Although programs have been designed to reduce nutrient loading in individual coastal waters, cross watershed interdependencies of nutrient runoff have not been quantified due to a lack of suitable modeling tools. Cross-watershed pollution leakage can occur when nutrient runoff moves from more to less regulated regions. We illustrate the use of an Integrated Assessment Model (IAM) that combines economic and process-based biophysical tools to quantify Nitrogen loading leakage across three major US watersheds. We also assess losses in consumer and producer surplus from decreased commodity supply and higher prices when nutrient delivery to select coastal ecosystems is restricted. Reducing agricultural N loading in the Gulf of Mexico by 45% a) increases loading in the Chesapeake Bay and Western Lake Erie by 4.2% and 5.5%, respectively, and b) results in annual surplus losses of $7.1 and $6.95 billion with and without restrictions on leakage to the Chesapeake Bay and Lake Erie, respectively.

Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice

AbstractIntroductionMachine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects of implementing therapist feedback via a deep‐learning model (DLM) tool that predicts client treatment response (i.e., reliable improvement on the Patient Health Questionnaire‐9 [PHQ‐9] or Generalized Anxiety Disorder‐7 [GAD‐7]) to internet‐delivered cognitive behavioural therapy (iCBT) in routine clinical care and (2) therapist acceptability of this prediction tool.MethodsFifty‐one therapists were randomly assigned to access the DLM tool (vs. treatment as usual [TAU]) and oversaw the care of 2394 clients who completed repeated PHQ‐9 and GAD‐7 assessments.ResultsMultilevel growth curve models revealed no overall differences between the DLM tool vs. TAU conditions in client clinical outcomes. However, clients of therapists with the DLM tool used more tools, completed more activities and visited more platform pages. In subgroup analyses, clients predicted to be ‘not‐on‐track’ were statistically significantly more likely to have reliable improvement on the PHQ‐9 in the DLM vs. TAU group. Therapists with access to the DLM tool reported that it was acceptable for use, they had positive attitudes towards it, and reported it prompted greater examination and discussion of clients, particularly those predicted not to improve.ConclusionAltogether, the DLM tool was acceptable for therapists, and clients engaged more with the platform, with clinical benefits specific to reliable improvement on the PHQ‐9 for not‐on‐track clients. Future applications and considerations for implementing machine learning predictions as feedback tools within iCBT are discussed.

Molecular Orientation Behavior of Lyotropic Liquid Crystal-Carbon Dot Hybrids in Microfluidic Confinement.

Lyotropic liquid crystals represent an important class of anisotropic colloid systems. Their integration with optically active nanoparticles can provide us with responsive luminescent media that offer new fundamental and applied solutions for biomedicine. This paper analyzes the molecular-level behavior of such composites represented by tetraethylene glycol monododecyl ether and nanoscale carbon dots in microfluidic channels. Microfluidic confinement allows for simultaneously applying multiple factors, such as flow dynamics, wall effects, and temperature, for the precise control of the molecular arrangement in such composites and their resulting optical properties. The microfluidic behavior of composites was characterized by a set of analytical and modeling tools such as polarized and fluorescent microscopy, dynamic light scattering, and fluorescent spectroscopy, as well as image processing in Matlab. The composites were shown to form tunable anisotropic intermolecular structures in microchannels with several levels of molecular ordering. A predominant lamellar structure of the composites was found to undergo additional ordering with respect to the microchannel axis and walls. Such an alignment was controlled by applying shear and temperature factors to the microfluidic environment. The revealed molecular behavior of the composite may contribute to the synthesis of hybrid organized media capable of polarized luminescence for on-chip diagnostics and biomimetics.

Comprehensive analysis of the bZIP gene family in ‘Duli’ pear (Pyrus betulifolia Bunge): Insights for research on abiotic stress, especially drought, in pear

Plants are unavoidably subjected to various abiotic stresses, such as drought, salt, and cold, during their growth and development. Drought, accompanied by water shortages in North China, has become an environmental factor seriously affecting agriculture production. Basic leucine zipper (bZIP) is one of the most diverse transcription factor families in plants. It participates in the regulation of many physiological processes and plays an important role in plants’ response to abiotic stress. In this study, for the first time, 88 bZIP family members (PbbZIPs) in ‘Duli’ pear (Pyrus betulifolia Bunge) were identified using both hidden Markov model (HMM) and basic local alignment search tool (BLAST) methods. A phylogenetic tree of all the PbbZIPs members was constructed. The physicochemical properties, subcellular localisation, chromosomal location, gene structure, conserved motifs and domains, secondary and tertiary structures, and cis-acting promoter elements of the 88 family members were comprehensively analysed. The results indicated that PbbZIPs may play relevant roles in stress response in ‘Duli’. Therefore, we determined the expressions of all PbbZIPs in ‘Duli’ seedlings under drought, salt, and cold conditions, and in seedlings treated with stress-related hormones such as abscisic acid (ABA), salicylic acid (SA), methyl jasmonate (MeJA), and melatonin (MT). PbbZIPs responsive to various abiotic stresses and hormones were screened. In addition, we determined the expression patterns of 12 PbbZIP genes that strongly responded to both drought and ABA in ‘Duli’ tissue culture plants treated with different concentrations of PEG4000 at different time points. Finally, we selected three key genes (PbbZIP65, PbbZIP77 and PbbZIP81) that may play a major role in drought resistance in ‘Duli’, and preliminarily identified their function through the VIGS method, finding that PbbZIP77 could positively regulate the drought resistance of ‘Duli’ seedlings, while PbbZIP65/81 could negatively regulate the drought resistance of ‘Duli’ seedlings. This study not only broadens our knowledge about the bZIP gene family in ‘Duli’ pear, but also provides notable guidance for further functional research on PbbZIPs in abiotic stress, especially drought resistance, in the pear plant.

PHREEQ C Modelling Tool Application to Determine the Effect of Anions on Speciation of Selected Metals in Water Systems within Kajiado North Constituency in Kenya

Heavy metals and anions in water pose a significant risk to living organisms, including humans, and must therefore be carefully monitored. There are numerous approaches and methods of analysis to choose from. Using the PHREEQ C chemical modeling technique, this study looked at the effect of certain anions on the speciation of selected metals in the water environment of Kajiado North. Twenty-five sampling points were used in the study, which covered five Kajiado North election wards. Samples were taken twice during the dry and rainy seasons, in November 2019 and March 2020. Using standard methods, physicochemical characteristics were analyzed, concentrations of selected heavy metals using the (ICP- OES) 7000 series, and selected anions using a multi parameter photometer, their concentration values compared to the specified maximum contaminant level and speciation analysis done using the Computer-based technique PHREEQ C. The range observed for pH, temperature, electrical conductivity, sulphate, chloride, fluoride, nitrate and carbonates was 7.43- 9.30, 23.0 -28.0oC, 335-1265μS/cm, 4.54-361.41mgL-1, 5.19-184.6 mgL-1, 0.01-2.90 mgL-1, 0.1-8.6 mgL-1, and 3.18-55.19 mgL-1 respectively. The range observed for heavy metals was 0.06-1.08 mgL-1 , 0.00 -0.05082 mgL-1, 0.01-0.02597 mgL-1, 0.01-0.01610 mgL-1, 0.00 -0.0327 mgL-1, 0.00-42.7842 mgL-1, and below detection limit, for Fe, Mn, Cu, Zn, Pb, Mg, and Cd respectively. The findings revealed that Cd, Zn, Cu, Cl-, NO2-, and eElectrical cconductivity concentrations were below the WHO levels. At certain sampling points, however, SO42-, F-, Pb, Fe, and Mn were above WHO limits. In some water systems, traces of SO42-, F-, Cu, Fe, Cd, and Mn were found to pose significant health risks, according to PHREEQ C speciation analysis. Anion concentrations, pH, temperature, and metal concentrations are all important variables in modeling. Metals formed anion complexes and the total abundance of metal species was classified at the highest levels as Fe(OH)3, PbCO3, Zn(CO3)2, Cu(CO3)2, Mn+2, MgF-, and Mg+2. At a water system temperature of 25oC without anions, it was found to have the highest chemical species. CuCO3, CuNO3, and CuOH+ complexes are also formed at low pH. Based on the data for different metal species at different pH values, the bioavailability of metal species definitely depends on pH.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.

Can the Establishment of National Parks Promote the Coordinated Development of Land, the Environment, and Residents’ Livelihoods?

National parks play a crucial role in the worldwide system of natural conservation, significantly influencing land utilization, the sustainability of local livelihoods, and the environmental integrity of nearby regions. In this study, the coupled coordination model and ArcGIS tools are applied to examine the temporal and spatial progression of the coordination of the system of land, the environment, and residents’ livelihoods (the LEL system). The construction of the LEL system and the use of natural geographic and socio-economic data are the main innovations of this research. The findings indicate that over the last 15 years, Wuyishan National Park has witnessed growth in its ecological and agricultural areas alongside a decline in its water-based ecological zones. The creation of the national park has obviously promoted the coordination of the LEL systems in nearby areas and has a significant spatial spillover effect. Consequently, for the upcoming development of Wuyishan National Park, it is crucial to modify the framework of the agricultural sector, enhance local public infrastructure, and bolster the safeguarding of water ecological areas.

Regulatory framework for the assessment of the impacts of plant protection products on biodiversity: review of strengths and limits.

The placing of plant protection products (PPPs) on the market in the European Union is governed by numerous regulations. These regulations are among the most stringent in the world, however they have been the subject of criticisms especially because of the decline in biodiversity. The objectives of this work were to review (1) the functioning and actors involved in the PPP framework processes, (2) the construction of the environmental risk assessment focused on biodiversity, and (3) the suggested ways to respond to the identified limits. Both literature from social sciences and ecotoxicology were examined. Despite the protective nature of the European regulation on PPPs, the very imperfect consideration of biodiversity in the evaluation process was underlined. The main limits are the multiplicity of applicable rules, the routinization of the evaluation procedures, the lack of consideration of social data, and the lack of independence of the evaluation. Strengths of the regulation are the decision to integrate a systemic approach in the evaluation of PPPs, the development of modeling tools, and the phytopharmacovigilance systems. The avenues for improvement concern the realism of the risk assessment (species used, cocktail effects…), a greater transparency and independence in the conduct of evaluations, and the opening of the evaluation and decision-making processes to actors such as beekeepers or NGOs. Truly interdisciplinary reflections crossing the functioning of the living world, its alteration by PPPs, and how these elements question the users of PPPs would allow to specify social actions, public policies, and their regulation to better protect biodiversity.

The Birth of a New BIM Standard: From PED 2018 to 2023, New Parameters and Workflows "Going Live" for Everyone

Building Information Modelling (BIM) has become the de facto standard for the digital representation of buildings. However, from the pedestrian dynamics perspective, BIM Industry Foundation Classes (IFC) schema specification do not fully support data properties required for two-way data sharing with pedestrian modelling tools. An international team of academic and industry researchers, supported by buildingSMART International (bSI), is developing an Occupant Movement Analysis (OMA) standard. The project is focused on expanding the IFC schema specification to support workflows for pedestrian simulation tools and is close to completion. So far, multiple process maps and a list of data properties synchronised with several representative pedestrian modelling tools have been produced. This list of data properties was then converted into bSI's recommended flexible and machine interpretable Information Delivery Specification (IDS) format for specifying data exchange requirements and to add clarity. Currently, this is undergoing testing and review by the project team. Once completed, it will be submitted to bSI’s committees for review. Also, to support this work, a prototype open-source Add-in has been developed to demonstrate a two-way integrated data sharing between BIM authoring tools and pedestrian simulation tools. This standard will enhance data sharing between BIM authoring and pedestrian modelling tools by facilitating the capturing of the required data and addressing friction in multiple design iterations and reassessment.

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

Abstract The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models' performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.

Retrofitting Battery Electric Machinery with Unchanged Hydraulic System and Enhanced Control Strategies

The push for environmental sustainability has accelerated the acceptance of electric vehicles, as well as the exploration of electrified Non-Road Mobile Machinery. This study emphasizes the challenges of electrifying off-highway machinery, which include the many machinery layouts and the presence of Small- and Medium-sized Enterprises in the market. Recognizing the barriers faced by these companies, this paper shows how modeling and simulation can be effective tools for system integration and control optimization, even when lacking extensive expertise in the topic. However, it emphasizes the need for user-friendly modeling tools and methods adaptable to the operational needs of Small- and Medium-sized Enterprises. This study presents a case study of a retrofitted battery-electric hydraulic material handler. The machinery is simulated using Simscape, and the accuracy of the model is confirmed through experimental validation. By simulating a rational duty cycle, this study proposes two solutions for performance enhancement while maintaining the integrity of the hydraulic system. These solutions offer a balanced compromise between energy consumption and productivity and a novel control algorithm to minimize energy consumption. Most importantly, the two proposed solutions can be easily switched by the operator, which can decide to favor productivity over energy saving based on driving needs.