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Automated Guided Vehicles (AGVs) have played an important role in modern factories or warehouses, replacing traditional conveyor-based sorting systems due to their flexibility and scalability. However, existing works on AGV sorting systems primarily focus on improving performance from mechanical perspectives or network optimization for routing design. There is a lack of discussion on how to provide a holistic assignment strategy that considers not only the assignment at the sorting area but also the impact of traffic flow from upstream stations in the system. This study introduces a novel two-stage optimization model for AGV sorting systems in central fill pharmacies, implemented via discrete-event simulation and a simulation-based heuristic algorithm. The methodology is based on a detailed analysis of the sorting system layout, assessing performance through key performance indicators (KPIs) such as throughput, utilization, and cycle time, complemented by a sensitivity analysis regarding the number of AGVs. Operational implications include improved assignment strategies that enhance overall system efficiency, reduced cycle time, and optimized resource utilization. Results demonstrate broad applicability across different automated systems, suggesting significant implications for operational efficiency. [ABSTRACT FROM AUTHOR]

Power metallurgy hot isostatic pressing (PM-HIP), as a versatile manufacturing process, can produce net-shape or near-net-shape components with complicated geometries from materials that are not easily cast, deformed, or welded. In PM-HIP, capsule (or die) filling is a critical step to get dimensionally and microstructurally sound outputs. Particularly, capsule filling controls the initial relative density (homogeneity) of the PM-HIP compact. In this study, the pre-consolidation capsule filling process is simulated by the discrete element method (DEM), to capture the impact of vibration parameters, including the vibration frequency, amplitude, duration, and direction, on the initial relative density (RD). The output of the DEM model was imported into a user subroutine-based finite element of PM-HIP containing a combined constitutive model of compressive and consolidative mechanical behavior of powder. The simulation model was used to quantitatively study the relationships between the vibration parameters and the initial RD of the product. The ultimate results of this work have shown an optimal vibration frequency for maximizing the initial density. The study also quantitatively tested the relationship between the other vibration parameters and the initial relative density and provided the analysis. [ABSTRACT FROM AUTHOR]

Automated Storage and Retrieval System (AS/RS) is driven by multiple motors for loading and unloading the items (z-axis) onto the fork or stacker, then moving the items horizontally (x-axis) and vertically (y-axis) at a time. Thus, it is practical to determine the speed configuration for each movement. To be responsive, it is reasonable to set the speed as fast as possible. However, high speed leads to high energy consumption which is undesirable in the context of green warehousing. Given that the speed changes dynamically, it is important to have an advanced optimization model for balancing both objectives and providing accurate estimation. This study proposed metamodel-based simulation–optimization (MSO) allowing to jointly optimize four speed-related variables namely horizontal speed (x), vertical speed (y), fork or depth speed (z), and acceleration/deceleration under the dynamicity of AS/RS. A case study was given in a warehouse comprising five cranes and ten racks. Using Desirability Function Analysis, the optimal speed configuration is obtained efficiently for minimizing travel time and energy consumption of AS/RS. The result also shows that row-based storage provides better responsiveness and energy efficiency than random-based storage. Further, rack design also indicates a significant impact on the AS/RS speed configuration. [ABSTRACT FROM AUTHOR]

Infectious diseases have caused enormous disasters in human society, and asymptomatic carriers are an important challenge in our epidemic prevention and control process. Nucleic acid testing has played an important role in rapid testing for asymptomatic individuals. How to carry out nucleic acid testing in a scientific manner is a practical problem encountered in normal production and life. Based on the real COVID-19 epidemic data from Shanghai, we established a susceptible-exposed-infected-asymptomatic-recovered-death (SEIARD) dynamic model. The least squares method was used to fit the data and estimate the unknown parameters β and E(0) in the model, and MATLAB software was employed to simulate the development of the epidemic. The data fitting results indicated that the SEIARD model can better describe the early development patterns of the epidemic (R2 = 0.98; MAPE = 2.54%). We calculated the basic reproduction number of the Shanghai epidemic as R0 = 2.86. As the frequency of nucleic acid testing increased, the basic reproduction number R0 continued to decrease. When there is one latent carrier and one asymptomatic carrier in the nucleic acid testing team, the number of queues is directly proportional to the number of infected individuals, the nucleic acid testing team increases by 50 people, and the number of new asymptomatic cases increases by approximately 4 people. If both susceptible individuals (S) and asymptomatic patients (A) are not wearing masks, the infection rate reaches approximately 7%; after wearing masks, the final infection rate is less than 1% at 1.5 m between two people. The queue spacing is inversely proportional to the number of infected individuals. With a distance of d = 1 m, a nucleic acid testing team of 100 people added 8% of the infected individuals; when d = 1.5 m, fewer than 2% of the newly infected individuals. The results confirmed that controlling the queue size for nucleic acid testing, strictly wearing masks, and maintaining a queue spacing of more than 1.5 m are safe and effective nucleic acid testing strategies. Our findings are also applicable to the prevention of other newly emerging infectious diseases. [ABSTRACT FROM AUTHOR]

In this study, an ordinary-deterministic equation system modeling the spread dynamics of malware under mutation is analyzed with fractional derivatives and random variables. The original model is transformed into a system of fractional-random differential equations (FRDEs) using Caputo fractional derivatives. Normally distributed random variables are defined for the parameters of the original system that are related to the mutations and infections of the nodes in the network. The resulting system of FRDEs is simulated using the predictor-corrector method based fde12 algorithm and the forward fractional Euler method (ffEm) for various values of the model components such as the standard deviations, orders of derivation, and repetition numbers. Additionally, the sensitivity analysis of the original model is investigated in relation to the random nature of the components and the basic reproduction number ( R 0 ) to underline the correspondence of random dynamics and sensitivity indices. Both the normalized forward sensitivity indices (NFSI) and the standard deviation of R 0 with random components give matching results for analyzing the changes in the spread rate. Theoretical results are backed by the simulation outputs on the numerical characteristics of the fractional-random model for the expected number of infections and mutations, expected timing of the removal of mutations from the network, and measurement of the variability in the results such as the coefficients of variation. Comparison of the results from the original model and the fractional-random model shows that the fractional-random analysis provides a more generalized perspective while facilitating a versatile investigation with ease and can be used on different models as well. [ABSTRACT FROM AUTHOR]

Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of −6.4, −5.1, −6.6, −5.1, and − 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function. [ABSTRACT FROM AUTHOR]

Background: The technical complexity and limited casuistry of neonatal surgical pathology limit the possibilities of developing the necessary technical competencies by specialists in training. Esophageal atresia constitutes the paradigm of this problem. The use of synthetic 3D models for training is a promising line of research, although the literature is limited. Methods: We conceptualized, designed, and produced an anatomically realistic model for the open correction of type III oesophageal atresia. We validated it with two groups of participants (experts and non-experts) through face, construct, and content-validity questionnaires. Results: The model was validated by nine experts and nine non-experts. The mean procedure time for the experts and non-experts groups was 34.0 and 38.4 min, respectively. Two non-experts did not complete the procedure at the designed time (45 min). Regarding the face validity questionnaire, the mean rating of the model was 3.2 out of 4. Regarding the construct validity, we found statistically significant differences between groups for the equidistance between sutures, 100% correct in the expert group vs. 42.9% correct in the non-expert group (p = 0.02), and for the item "Confirms that tracheoesophageal fistula closure is watertight before continuing the procedure", correctly assessed by 66.7% of the experts vs. by 11.1% of non-experts (p = 0.05). Concerning content validity, the mean score was 3.3 out of 4 for the experts and 3.4 out of 4 for the non-experts. Conclusions: The present model is a cost-effective, simple-to-produce, and validated option for training open correction of type III esophageal atresia. However, future studies with larger sample sizes and blinded validators are needed before drawing definitive conclusions. [ABSTRACT FROM AUTHOR]

Background: Robot-assisted procedures are increasingly common, and several systems are available for thoraco-abdominal surgery. Specific structured training is necessary, while access to these systems is still limited. This study aimed to assess surgeons' skill progress during consecutive training days of a curriculum with exposure to different robotic systems. Methods: This prospective observational study enrolled 47 surgeons with anonymized analysis of SimNow™ simulator performance scores and dedicated questionnaires after written consent. The primary outcome was the overall score, based on economy of motion, time to complete the exercise, and penalty for errors. Course participants in 2022–2023 had chosen 2 full hands-on days on Da Vinci® consoles with either virtual reality (VR) simulation training using the SimNow (n = 21, 44.7%) or digestive surgery procedures with a live animal model (n = 26, 55.3%). In all participants, training on Da Vinci® systems included console functions and principles of docking, camera, and instrument use for console and procedural training. They additionally had access to introductory dry-lab and VR simulator exercises on the Versius, HugoTMRAS, and Dexter systems and to VR exercises on the ROBOTiS simulator. Results: The participants (16F/31M, median age 40 years, range 29–58) from various surgical specialties (general/visceral/vascular) had no (n = 35, 74.5%) or little (n = 12, 25.5%) robotic experience including bedside assistance only and 20 (42.6%) had robotic simulator experience. The demographic variables fully completed by 44/47 participants (93.6%) and choice of module had no significant impact on the primary outcome. The considerable performance improvement from days 1 to 2 was exemplified by a significantly increased economy of motion and decreased amount of excessive force. Conclusion: Robotic surgical training is increasingly complex with several systems on the market. Within a dedicated robotic surgery curriculum and based on integrated performance metrics, a significant improvement of skill levels was observed in a relatively short period of time. [ABSTRACT FROM AUTHOR]

Background: Perineal proctectomy is a complex procedure that requires advanced skills. Currently, there are no simulators for training in this procedure. As part of our objective of developing a virtual reality simulator, our goal was to develop and validate task-specific metrics for the assessment of performance for this procedure. We conducted a three-phase study to establish task-specific metrics, obtain expert consensus on the appropriateness of the developed metrics, and establish the discriminant validity of the developed metrics. Methods: In phase I, we utilized hierarchical task analysis to formulate the metrics. In phase II, a survey involving expert colorectal surgeons determined the significance of the developed metrics. Phase III was aimed at establishing the discriminant validity for novices (PGY1-3) and experts (PGY4-5 and faculty). They performed a perineal proctectomy on a rectal prolapse model. Video recordings were independently assessed by two raters using global ratings and task-specific metrics for the procedure. Total scores for both metrics were computed and analyzed using the Kruskal–Wallis test. A Mann–Whitney U test with Benjamini–Hochberg correction was used to evaluate between-group differences. Spearman's rank correlation coefficient was computed to assess the correlation between global and task-specific scores. Results: In phase II, a total of 23 colorectal surgeons were recruited and consensus was obtained on all the task-specific metrics. In phase III, participants (n = 22) included novices (n = 15) and experts (n = 7). There was a strong positive correlation between the global and task-specific scores (rs = 0.86; P < 0.001). Significant between-group differences were detected for both global (χ2 = 15.38; P < 0.001; df = 2) and task-specific (χ2 = 11.38; P = 0.003; df = 2) scores. Conclusions: Using a biotissue rectal prolapse model, this study documented high IRR and significant discriminant validity evidence in support of video-based assessment using task-specific metrics. [ABSTRACT FROM AUTHOR]

Background: Simulation is increasingly being explored as an assessment modality. This study sought to develop and collate validity evidence for a novel simulation-based assessment of operative competence. We describe the approach to assessment design, development, pilot testing, and validity investigation. Methods: Eight procedural stations were generated using both virtual reality and bio-hybrid models. Content was identified from a previously conducted Delphi consensus study of trainers. Trainee performance was scored using an equally weighted Objective Structured Assessment of Technical Skills (OSATS) tool and a modified Procedure-Based Assessment (PBA) tool. Validity evidence was analyzed in accordance with Messick's validity framework. Both 'junior' (ST2–ST4) and 'senior' trainees (ST 5–ST8) were included to allow for comparative analysis. Results: Thirteen trainees were assessed by ten assessors across eight stations. Inter-station reliability was high (α = 0.81), and inter-rater reliability was acceptable (inter-class correlation coefficient 0.77). A significant difference in mean station score was observed between junior and senior trainees (44.82 vs 58.18, p =.004), while overall mean scores were moderately correlated with increasing training year (rs =.74, p =.004, Kendall's tau-b.57, p = 0.009). A pass-fail score generated using borderline regression methodology resulted in all 'senior' trainees passing and 4/6 of junior trainees failing the assessment. Conclusion: This study reports validity evidence for a novel simulation-based assessment, designed to assess the operative competence of higher specialist trainees in general surgery. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic appendectomy is a common procedure and introduced early in general surgical training. How internal (i.e. surgeon's experience) or external (i.e. disease severity) may affect procedure performance is not well-studied. The aim of this study was to evaluate factors that may have an influence on the performance scores for surgical trainees. Methods: A prospective, observational cohort study of laparoscopic appendectomies performed by surgical trainees (experience < 4 years) operating under supervision. Trainers evaluated trainees' overall performance on a 6-point scale for proficiency. Perioperative data were recorded, including appendicitis severity, operating time and the overall difficulty of the procedure as assessed by the trainer. A "Challenging" procedure was defined as a combination of either/or "perforation" and "difficult". Trainees who had performed > 30 appendectomies were defined as "experienced". The trainees were asked if they had used simulation or web-based tools the week prior to surgery. Results: 142 procedure evaluation forms were included of which 19 (13%) were "perforated", 14 (10%) "difficult" and 24 (17%) "Challenging". Perforated appendicitis was strongly associated with procedure difficulty (OR 21.2, 95% CI 6.0–75.6). Experienced trainees performed "proficient" more often than non-experienced (OR 34.5, 95% CI 6.8–176.5). "Difficult" procedures were inversely associated with proficiency (OR 0.1, 95% CI 0.0–0.9). In "Challenging" procedures, identifying the appendix had lowest proficiency (OR 0.4, 95% CI 0.1–0.9). The procedures assessed as "difficult" had significantly longer operating time with a median (IQR) of 90 (75–100) min compared to 59 (25–120) min for the non-difficult (p < 0.001). Conclusion: Both internal and external factors contribute to the performance score. Perforated appendicitis, technical difficult procedures and trainee experience all play a role, but a "difficult" procedure had most overall impact on proficiency evaluation. [ABSTRACT FROM AUTHOR]

Experiments are routinely conducted to evaluate the toxicity of water effluents or chemicals. In a Ceriodaphnia dubia reproduction test, organisms are exposed to varying concentration levels of the toxicant or other adverse treatment and the number of young produced after a given experiment period are recorded. To analyze the test outcomes, mean reproduction is modeled as a function of the concentration, and the estimated concentrations associated with specified levels of adverse effect often are used in risk management. While aquatic toxicity analyses often focus on outcomes from the current test, laboratories commonly have a history of conducting such tests using the same species, following a similar experimental protocol. So it seems reasonable to assume that the same underlying biological process generates the historical and current tests. In the present study, we propose using a calibrated power prior approach to incorporate historical control outcomes as prior input, and compare the behavior of the method when different congruence measures are used to determine the amount of historical input that will be incorporated. Simulation results show that three of the congruence measures can improve precision and reduce bias of the potency estimates.Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

The quality assurance of the earth-atmosphere system, which runs primarily to temperature variations and other disturbances of the earth's climate, compels the assessment of greenhouse gases (GHG) emissions due to various Organizations for Economic Co-operation and Development (OECD) countries' human activities that disturb the radioactive energy balance. The purpose of this paper is to thus determine the usefulness and acceptability of statistical process control for assessing the level of GHG emissions from OECD countries. We apply the exponentially weighted moving average (EWMA) control charts with one-time, two-time, and three-time smoothing processes for monitoring the process variance under a repetitive sampling scheme, named S 2 -EWMA RS , S 2 -DEWMA RS and S 2 -TEWMA RS charts, and also utilize a cumulative sum (CUSUM) control chart under a repetitive sampling scheme, named as S2-CUSUMRS chart, to assess these countries' emission levels of GHG. The control charts are a graphical representation of the trend product of sequential procedures. All the OECD countries can thus independently measure their emission level of GHG as a self-assessment tool. Consequently, we recommend use of the advanced control charts as a tool for OECD assessment at an individual country level. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Purpose: This umbrella review aimed to identify, critically appraise, and synthesize current evidence from systematic reviews and meta-analyses on the applications of virtual reality-based supportive care interventions in cancer. Methods: Three bibliographic databases were searched from inception to February 1, 2024. Two independent reviewers screened the titles and abstracts of 421 records and retrieved 26 full-text systematic reviews. Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2) was used for quality assessment. Qualitative syntheses were performed to investigate the effects of virtual reality-based supportive care interventions on quality of life and physical, psychological, cognitive, and functional outcomes. Meta-analysis was performed based on data from the distinct primary studies that were extracted from the included reviews. Results: This umbrella review included 20 systematic reviews that were published between 2018 and 2023; nine of them conducted meta-analyses. A total of 86 distinct primary studies were identified. According to the AMSTAR-2 assessment, two reviews were evaluated as moderate quality, nine as low, and nine as critically low. Meta-analyses of primary studies revealed significant effects of virtual reality on anxiety (p < 0.001), depression (p < 0.001), and pain (p < 0.001), but not fatigue (p = 0.263). Qualitative syntheses revealed positive effects of virtual reality on physical function, cognitive function, and quality of life. Limited evidence was reported regarding outcomes of balance, gait, mobility, and activities of daily living. Conclusion: Virtual reality has proven to be a safe and feasible approach to deliver supportive care in cancer. Virtual reality can be implemented in various stages and settings of the cancer care continuum to support patients undergoing painful procedures, during or after chemotherapy, and after surgical interventions. Virtual reality can serve as an effective supportive care intervention to manage anxiety, pain, and depression for patients with cancer. [ABSTRACT FROM AUTHOR]

The 3D inspection process based on the use of optical sensors is more widely used for industrial applications, due to their great ability to scan the entire surface in a short time. Laser-plane sensors mounted on coordinate measuring machines (CMMs) are commonly used for high-precision inspection. However, measurement quality is affected by the sensor path, defined primarily by the sensor orientation and position. Executing the scan path on the machine is time-consuming, and potential collision problems can occur. In this context, the aim of this paper is to develop a 3D scan simulation tool to evaluate a given scan path in terms of visibility and quality. Scanning quality is first investigated and evaluated experimentally depending on the configurations of the laser-plane sensor relative to the part geometry. The developed simulator is therefore based on the qualification results. The principle of the simulator is formulated by functions expressing visibility and quality conditions relative to scanning distance and angle. The path simulator is set up by visualizing the two conditions on facets of the part surface, and the quality is then estimated through the corresponding function. A case study is presented to illustrate the proposed simulation tool for scanning a test part defined by its CAD model with a given sensor path. The results are compared to real scans obtained from the sensor. This study demonstrates the effectiveness of the proposed simulation tool for predicting and estimating measurement quality. The outcome of the presented research in this paper has to be used as a predictive tool to evaluate scan paths in order to scan industrial parts with the required quality. Its integration into CMMs enables accurate anticipation of scanning results, thereby enhancing operational efficiency. [ABSTRACT FROM AUTHOR]

Electro-discharge machining (EDM) has been extensively employed for machining hard alloys, and its simulations have been widely conducted using finite element analysis (FEA). However, the majority of mesh-based models depended on forecasting the crater profile only based on the temperature gradient, without offering detailed data regarding the machined material properties. It is crucial to understand the behaviour of the machined material in order to accurately assess the flushing efficiency, analyse the wear on the electrode, and examine the interaction between the debris generated during machining and the remaining workpiece. This is done to ensure that no recast material is left behind after the EDM process. For the first time, a meshless smoothed particle hydrodynamics multi-phase model was implemented to gain practical insights and comprehensively understand a very intricate phenomenon that occurs within a very short time. Additionally, this approach is utilised to investigate the characteristics of the materials being machined. We utilised our SPH model to simulate both the capacitance- and transistor-based EDM of Ti–6Al–4V and AISI304 steel. Our simulation considered the temperature-dependent thermal properties and latent heats of the materials. The accuracy of our model was confirmed by comparing its results with experimental, analytical, and finite element analysis (FEA) results. The machined material was observed during its removal from the surface, and the dimensions of the resulting crater, as well as its aspect ratio and the rate at which the material was removed, were predicted with an error ranging from 2 to 22%. This error is far lower than that of the typical finite element (FE) prediction. This model lays the groundwork for a more complex model that will more accurately represent EDM and other similar manufacturing processes. [ABSTRACT FROM AUTHOR]

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Concerns regarding air pollution and the risk of infectious diseases caused by bioaerosols have led to growing demand for effective filtration solutions. The effective capture of bioaerosols is essential; however, addressing the potential multiplication of captured aerosols on the air filter over time is also crucial. Therefore, the development of air filters with enhanced antimicrobial protection is imperative for preventing the proliferation of bioaerosols and ensuring safer and healthier air filtration systems. In this study, antimicrobial peptides conjugated nanostructures were used to enhance the capture and killing of bioaerosols with synergistic chemo-mechano-antimicrobial actions. We designed a puncturable nanostructure on an air filter and functionalized it with the antimicrobial LL-37 peptide. Bioaerosol filtration and antimicrobial performance tests were conducted to evaluate the synergistic effects of the antimicrobial puncturable nanostructures on bioaerosol removal. The peptide-conjugated puncturable nanostructured air filter outperformed bare air filters in bioaerosol capture and exhibited significantly better contact-killing properties against bioaerosols. These attributes indicate the ability of the filter to significant capture and kill airborne pathogens. The synergetic chemo-mechano-antimicrobial nanostructured filter is promising for combating airborne threats and serves as a safe filtration solution that is free from biotoxicity concerns and is suitable for widespread implementation in the market. [ABSTRACT FROM AUTHOR]

The primary focus of this research is to use additive manufacturing to provide cellular structure, as opposed to solid structure, for tooling applications. The purpose of this study is to determine how effective the sparse mode build style is in terms of both build time and material usage when building cellular structures. In accordance with the ASTM D695 standard, sparsely built cellular structure specimens (crisscross and honeycomb) were subjected to a compression test at room temperature using a hydroforming die. An industrial loading scenario was studied using a finite element analysis model to analyse the stress–strain behaviour of sparse build hydroforming dies with and without cellular features. Using an FDM UPrint SE machine, a sparse build hydroforming die without a cellular structure was created in ABS plus material and successfully used in the hydroforming process to create thin metallic bellows with a thickness of 0.2 mm. According to the findings, the hydroforming die made with a sparse mode build style required an average of 17.835% less time to construct and 12.274% less material than a solid mode build style. This analysis of sparse build FDM tooling performance may be expanded to include tooling use in non-thermal based forming processes. [ABSTRACT FROM AUTHOR]

The Indian Railway network is the world's fourth largest, transporting millions of people every day. One of the most difficult challenges for travelers is crossing the overhead bridges or subways to reach the right platform. To make this experience more comfortable we have developed the automatic system termed Railway Platform Crossing Automatic Bridge (RPCAB) to connect two opposite platforms. Here, the fabricated metal frame bridge is moved using a pair of double acting hydraulically/ pneumatically actuated telescopic cylinders. After the train pulls out of the station, the bridge connects to the other side of the platform, allowing passengers to walk on the bridge to cross the tracks. The position sensors, alarms, audio/visual indicators, and actuators are all in sync with the train traffic signaling system and the master controller, a Programmable Logic Controller (PLC). To prevent any mishaps from happening, a comprehensive safety interlock system has been implemented, including position sensors, safety barricades, emergency alarms, and an audio-visual information system. The proposed mechanical bridge facilitates the passage for the passengers who are physically impaired, with heavy luggage, pregnant women, and the elderly persons to cross the platform. Additionally, it controls the congestion of passengers when the train has left the station. The proposed system is simulated using PLC simulator for testing, validation, and analysis of the system's behavior in a simulated environment. The simulation results presented in this paper show how efficient and reliable the proposed design is. Prior to constructing a working prototype in real time, it is essential to put the system through a virtual environment. The results support the viability of applying the proposed design in real-world settings, which will improve both safety and efficiency at railway platform crossings. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis is a deadly protozoan parasitic disease and a significant health problem in underdeveloped and developing countries. The global spread of the parasite, coupled with the emergence of drug resistance and severe side effects associated with existing treatments, has necessitated the identification of new and potential drugs. Objective: This study aimed to identify promising compounds for the treatment of leishmaniasis by targeting two essential enzymes of Leishmania donovani: trypanothione reductase (Try-R) and trypanothione synthetase (Try-S). Methods: High-throughput virtual and in vitro screening of in-house and commercial databases was conducted. A pharmacophore model with seven features was developed and validated using the Guner-Henery method. The pharmacophore-based virtual screening yielded 690 hits, which were further filtered through Lipinski's rule, ADMET analysis, and molecular docking against Try-R and Try-S. Molecular dynamics studies were performed on selected compounds, and in vitro experiments were conducted to evaluate their activity against the promastigote and amastigote forms of L. donovani. Results: The virtual screening and subsequent analysis identified 33 promising compounds. Molecular dynamics studies of two compounds (comp-1 and comp-2) demonstrated stable binding interactions with the target enzymes and high affinity. In vitro experiments revealed that 13 compounds exhibited moderate activity against both the promastigote (IC50, 41 µM–76 µM) and the amastigote (IC50, 44 µM–72 µM) forms of L. donovani. Compounds 1 and 2 showed the highest percent inhibition and the lowest IC50 values. Conclusion: The identified compounds demonstrated significant inhibitory activity against Leishmania donovani and stable interactions with target enzymes. These findings suggest that the compounds could serve as promising leads for developing new treatments for leishmaniasis. [ABSTRACT FROM AUTHOR]

Heterostructured materials (HS) are a new type of novel materials that overcome the strength–conductivity trade-off due to significant differences in mechanical and physical properties between heterogeneous regions, resulting in a synergistic strengthening mechanism. This review article starts with the classification of heterostructured materials, which are divided into gradient nanograined materials, layered heterogeneous materials, and bimodal structural materials. The strengthening mechanisms of heterostructured materials are analyzed, including gradient plasticity mechanism, heterogeneous deformation-induced (HDI) strengthening and hardening mechanism, and the "shear band" strengthening mechanism. The HDI mechanism is attributed to the accumulation of geometrically necessary dislocations in the soft region at the grain boundary. The fatigue properties and formability of HS have been emphatically reviewed. Additionally, the combination of simulation and machine learning with materials genomics has promoted the rapid development of heterostructured materials, providing guidance for the prediction and design of high-strength and high-toughness heterostructured materials. [ABSTRACT FROM AUTHOR]

Modeling IoT applications in distributed computing systems as workflows enables automating their procedure. There are different types of workflow-based applications in the literature. Executing IoT applications using device-to-device (D2D) communications in distributed computing systems especially edge paradigms requiring direct communication between devices in a network with a graph topology. This paper introduces a toolkit for simulating resource management of scientific workflows with different structures in distributed environments with graph topology called WIDESim. The proposed simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators, also evaluated its performance in real-world scenarios of distributed computing. The results indicate that WIDESim’s performance is close to existing standard simulators besides its improvements. Additionally, the findings demonstrate the satisfactory performance of the extended features incorporated within WIDESim. [ABSTRACT FROM AUTHOR]

Purpose: This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice. Methods: Simulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees. A skill performance assessment protocol is presented based on the trained virtual agents. Results: The proposed benchmark suite presents an API for training reinforcement learning agents in the context of arthroscopic skill training. The evaluation scheme based on both heuristic and learned reward functions robustly recovers the ground truth ranking on a diverse test set of human trajectories. Conclusion: The presented benchmark enables the exploration of a novel reinforcement learning-based approach to skill performance assessment and in-procedure assistance for simulated surgical training scenarios. The evaluation protocol based on the learned reward model demonstrates potential for evaluating the performance of surgical trainees in simulation. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. Wildfire spread is a highly heterogeneous process with non-uniform spreading speed and fireline intensity in both space and time. When planning the path of a UAS for monitoring a dynamically growing wildfire, it is important to consider the uneven spread of the fire because different parts of the fire boundary would need different levels of monitoring attention. This paper presents a real-time path planning algorithm for a UAS to autonomously monitor the perimeter of a spreading wildfire with uneven importance. The proposed path planning algorithm allows the UAS to focus on the most active regions of a wildfire while still covering the entire fire perimeter. The design and implementation of this algorithm are described, and an in-depth analysis of the performance of the algorithm is provided. Experiment results based on simulated wildfires demonstrate the effectiveness and robustness of the proposed algorithm for monitoring dynamic wildfires. [ABSTRACT FROM AUTHOR]

We develop a numerical algorithm for simulation of wave propagation in anisotropic thermoelastic media, established with a generalized Fourier law of heat conduction. The wavefield is computed by using a grid method based on the Fourier differential operator and a first-order explicit Crank-Nicolson algorithm to compute the spatial derivatives and discretize the time variable (time stepping), respectively. The model predicts four propagation modes, namely, a fast compressional or (elastic) P wave, a slow thermal P diffusion/wave (the T wave), having similar characteristics to the fast and slow P waves of poroelasticity, respectively, and two shear waves, one of them coupled to the P wave and therefore affected by the heat flow. The thermal mode is diffusive for low values of the thermal conductivity and wave-like (it behaves as a wave) for high values of this property. As in the isotropic case, three velocities define the wavefront of the fast P wave, i.e, the isothermal velocity in the uncoupled case, the adiabatic velocity at low frequencies, and a higher velocity at high frequencies. The heat (thermal) wave shows an anisotropic behavior if the thermal conductivity is anisotropic, but an elastic source does not induce anisotropy in this wave if the thermal properties are isotropic. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid Watts-MPI multidimensional poverty measure that combines the multidimensional Watts poverty index (MWPI), which can accommodate continuous poverty dimensions, with the multidimensional poverty index (MPI), which can accommodate binary poverty dimensions. Unlike the stand-alone MPI that entails total loss of dimension-specific information on both poverty intensity with respect to shortfall and inequality, the proposed hybrid Watts-MPI measure entails only partial loss of such information since poverty intensity and inequality estimates can still be obtained for the continuous poverty dimensions included in the hybrid measure. The hybrid Watts-MPI also specializes to the stand-alone MWPI and MPI when all the poverty dimensions are continuous and binary, respectively. Furthermore, formation of the hybrid Watts-MPI does not entail loss of normative properties by either the constituent MWPI or MPI. The seemingly unrelated regression approach to the estimation of the hybrid Watts-MPI is described and an empirical example demonstrating its efficacy is provided. [ABSTRACT FROM AUTHOR]

The use of 3D Deep Learning (DL) models for LiDAR data segmentation has attracted much interest in recent years. However, the generation of labeled point cloud data, which is a prerequisite for training DL models, is a highly resource-intensive exercise. Simulated LiDAR data, which are already labeled, provide a cost-effective alternative, but their efficacy and usefulness must be evaluated. This paper examines the role of simulated LiDAR point clouds in training DL models. A high-fidelity 3D terrain model representing the real environment is developed, and the in-house physics-based simulator "Limulator" is used to generate labeled point clouds through various realizations. The paper outlines a few major hypotheses to assess the usefulness of simulated data in training DL models. The hypotheses are designed to assess the role of simulated data alone or in combination with real data or by strategic boosting of minor classes in simulated data. Several experiments are carried out to test these hypotheses. An experiment involves training a DL model, PointCNN in this case, using various combinations of simulated and real LiDAR data and measuring its performance to segment the test data. Results show that training using simulated data alone can produce an overall accuracy (OA) of 89% and the weighted-averaged F1 score of 88.81%. It is further observed that training using a combination of simulated and real data can achieve accuracies comparable to when only a large quantity of real data is employed. Strategic boosting of minor classes in simulated data improves the accuracies of minor classes by up to 23% compared to only real data. Training a DL model using simulated data, due to the ease in its generation and positive impact on segmentation accuracy, can be highly beneficial in the use of DL for LiDAR data. The use of simulated data for training has the potential to minimize the resource-intensive exercise of developing labeled real data. [ABSTRACT FROM AUTHOR]

This paper introduces a bounded probability distribution which is derived from the Muth distribution. The main statistical properties are studied and analytical expressions are provided for the moments, incomplete moments, inverse of the cumulative distribution function, extropy, Lorentz and Bonferroni curves, among others. Moreover, it possesses both monotone and non-monotone hazard rate functions so the new distribution is rich enough to model real data. Different estimation methods are applied to estimate the parameters of the model and a Monte Carlo simulation study assesses their performances. The usefulness in practical applications is illustrated using two real data sets and the results show that the proposed distribution provides better fits than other competing distributions commonly used to model data with bounded support. [ABSTRACT FROM AUTHOR]

A Kesterite CZTS semiconductor contains earth-abundant elements and has been recognized as a promising absorber layer for highly efficient and low-cost thin-film solar cells. We present a numerical approach for analyzing the performance of CZTS-based photovoltaic cell with a non-toxic ZTO buffer layer through the use of a solar cell capacitance simulator. The performance of the p-CZTS/n-ZTO device has been examined in terms of ZTO buffer layer parameters such as thickness, donor concentration, mid-gap defect density, electron affinity and bandgap. The maximum cell efficiency obtained for the conduction band offset was 0.2 eV, which shows a small spike at the interface between the absorber and the buffer layers. In the optimized cell, the efficiency and fill factor were found to be 17.48% and 82.53%, respectively, which is the highest value reported so far. There are different kinds of current–voltage distortions observed, such as crossover and red-kink behavior, and the reason behind these distortions have been discussed. The results of this study provide valuable insights into the development of Cd-free highly efficient solar cells based on CZTS. [ABSTRACT FROM AUTHOR]

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth. [ABSTRACT FROM AUTHOR]

Simulation-based robotic surgery training may help surgeons gain operative skills and experience in the simulation environment. This bibliometric analysis examined the development of simulation-based training for robotic surgical education. Articles pertaining to robotic surgical simulation training that were included in the Web of Science Core Collection up to April 25, 2024, were included. The temporal patterns in published paper numbers were evaluated using Microsoft Excel software, and the data regarding co-authorship and keyword co-occurrence were analyzed and visualized using the VOSviewer and SCImago Graphica tools. A total of 594 papers on simulation-based training for robotic surgical education were evaluated in this study. The United States and United Kingdom were the leading contributors in this field. The most published authors were Professor Ahmed Kamran (23 publications) and Prokar Dasgupta (22 publications). The highest number of papers was published in the journal titled "Surgical Endoscopy and Other Interventional Techniques." The most common keywords were "virtual reality," "curriculum," "robotic surgery simulator," "assessment," and "learning curve." Our study offers a detailed overview of international research on simulation-based training for robotic surgical education, including the publishing countries, institutions, authors, journals, and research hotspots. It also methodically summarizes the state of knowledge in the area, and provides definite directions and concepts for further in-depth analysis. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy. [ABSTRACT FROM AUTHOR]

Milling difficult-to-machine metals like titanium alloys requires a precise surface finish at the nanoscale levels. This examines the surface properties of the Ti-6Al-4V alloy when milled in a tungsten carbide tool coated with TiAlN. With the latest technological improvements, more awareness is being developed to utilize the field of Machine learning. This research is focused on different Machine learning algorithms such as Neural networks (NN), Linear Regression analysis (LR), and Hyperplane based Support Vector Regression (SVR) are being employed to predict the accuracy of the cutting force and Surface finish (Ra) model during milling of Titanium alloy machining. The cutting force is simulated using the AdvantEdge software and compared the results with experimental values using a PVD-coated cemented tungsten carbide tool coated with TiAlN. Taguchi analysis was applied to the milling process in dry conditions using various cutting speeds (120-180 m/min), feed rates (0.05-0.1 mm/tooth), and depths of cut (0.5-1 mm) while applying TOPSIS multi-criterion decision making to get the optimum process parameters for cutting forces. According to the TOPSIS multi-criterion technique, cutting speed has the biggest contribution (64.17%) when compared to the feed rate (18.12 %). Milling cutting force is analyzed using the Deep learning model and compared the results with experiments and error percentile are calculated to obtain the most optimum model for the prediction. The productivity of the manufacturing product is improved by using simulation software to predict the cutting force and surface roughness and implementing intelligent machine learning tools. The polynomial regression model was used to predict the cutting force on milling Ti alloy and found that linear regression prediction was 220.21 whereas polynomial regression prediction was 222.98 at depth of cut 0.5 mm which is more accurate and the curve is fitted properly with most of the data points. [ABSTRACT FROM AUTHOR]

Advanced ceramics are widely used in various industries like medical, automotive, and aerospace. Production of ceramic components has been constrained due to challenging machining procedures and the difficulty of forming complicated parts. Powder injection molding is one of the suitable methods to produce complex ceramics and overcome the difficulties of producing these parts. This paper investigates the effect of micro and nano powder addition on the rheological properties, segregation, and imbalance filling. For this purpose, different amounts of nano and micro SiC powder (5 wt%, 10 wt%, 15 wt%, and 20 wt% of micro SiC and 1-3 wt% of nano alumina) were added to alumina powder and the prepared feedstocks were injected at various flow rates. Rheological properties of feedstocks and segregation phenomenon in the green parts were investigated by rotational rheometer and thermogravimetric analyzer, respectively. As well as, mold filing, segregation and distribution of temperature during filling were simulated using Moldflow Synergy (Autodesk) 2019 software and compared to the experimental results. It was found that feedstocks containing 15 wt% micro SiC and 2 wt% nano SiC showed the best rheological behavior. The segregation phenomenon was observed in samples injected at flow rate of 15 cm3/s. No imbalance filling was observed in none of the samples, but by increasing the flow rate the segregation was intensified. [ABSTRACT FROM AUTHOR]

Cost, logistics, and availability of robotic simulation opportunities suppose a real challenge for robotic surgery training. We aimed to test a new methodology for introduction to robotic surgery pre-congress courses. Two different "introduction to robotic surgery" pre-congress courses were developed. A new methodology using a sleeve/bypass, a ventral TAPP and an inguinal TAPP silicone models was implemented. After the session, the trainees answered a questionnaire to evaluate the course and the methodology using 1–5 Likert scales. A total of 21 participants participated in the courses and (72.2%) had no experience in robotic surgery. All trainees rated the course as good or excellent. There was a strong agreement between participants regarding the adequacy of the silicone models for this type of simulation/course. Trainees agree that the course gave them more confidence to perform a real robotic procedure, increased their interest in robotic surgery and made them feel ready to start their robotic surgery pathway. Congresses are a frequent way of contact between surgeons and robotic systems, mostly in the form of technical demonstrations or pre-congress courses. Our methodology showed that it is possible to allow for this contact in a low-cost way. This kind of courses is well received by congress delegates and have a positive educational impact. Despite of being "Discovery" courses, they have a positive impact on the congress, on the acquisition of robotic surgery skills and increase the interest in robotic surgery. [ABSTRACT FROM AUTHOR]

In recent years, alcohol addiction has become a major public health concern and a global threat due to its potential negative health and social impacts. Beyond the health consequences, the detrimental consumption of alcohol results in substantial social and economic burdens on both individuals and society as a whole. Therefore, a proper understanding and effective control of the spread of alcohol addictive behavior has become an appealing global issue to be solved. In this study, we develop a new mathematical model of alcohol addiction with treatment class. We analyze the dynamics of the alcohol addiction model for the first time using advanced operators known as fractal–fractional operators, which incorporate two distinct fractal and fractional orders with the well-known Caputo derivative based on power law kernels. The existence and uniqueness of the newly developed fractal–fractional alcohol addiction model are shown using the Picard–Lindelöf and fixed point theories. Initially, a comprehensive qualitative analysis of the alcohol addiction fractional model is presented. The possible equilibria of the model and the threshold parameter called the reproduction number are evaluated theoretically and numerically. The boundedness and biologically feasible region for the model are derived. To assess the stability of the proposed model, the Ulam–Hyers coupled with the Ulam–Hyers–Rassias stability criteria are employed. Moreover, utilizing effecting numerical schemes, the models are solved numerically and a detailed simulation and discussion are presented. The model global dynamics are shown graphically for various values of fractional and fractal dimensions. The present study aims to provide valuable insights for the understanding the dynamics and control of alcohol addiction within a community. [ABSTRACT FROM AUTHOR]

Electric impulse drilling (EID) technology has great industrialization potential since it has unique advantages in crushing hard rocks of deep formation. To study the mechanism of dielectric breakdown is of great significance for accelerating the practical application of EID technology. In this paper, the dielectric breakdown damage model (DBDM) is established based on the heterogeneous granite model. On the basis of DBDM, the whole process of absolute dielectric breakdown (ADB) is studied, including the formation of the plasma channel and the variation of the electric field. Then, the mechanism of regional dielectric breakdown (RDB) development to ADB is studied, and the influence of peak voltage, electrode spacing and the parameters of a high-voltage electric pulse (HVEP) is discussed. Besides, the efficiency of two dielectric breakdown modes is evaluated. It is found that dielectric breakdown will only occur in the domain where the strength of the local electric field formed by HVEP exceeds the critical breakdown field strength of rock. RDB will occur when the pulse duration is too short or the peak voltage is too low. It is worth noting that RDB can be developed into ADB under multi-HVEPs. Moreover, under the same voltage, there is almost no difference between the failure volume caused by RDB under multi-HVEPs and ADB under a single HVEP. However, the energy consumption of the RDB under multi-HVEPs decreases exponentially with increasing voltage and is always higher than that of the ADB under a single HVEP. When designing the supporting tools and bits of EID technology, the life of insulation, the characteristics of formation, and energy utilization should be fully considered. The related research in this paper aims to provide reference and guidance for the application of EID technology in drilling engineering. Highlights: The variation of the electric field, the change of minerals and the generation of plasma channel in granite during dielectric breakdown is studied. The effect of the characteristics of high-voltage electric pulse (HVEP) on the dielectric breakdown in granite is studied. The mechanism and rock-breaking efficiency of absolute dielectric breakdown (ADB) and regional dielectric breakdown (RDB) are explored. [ABSTRACT FROM AUTHOR]

In non-ideal solutions, partial molar volumes change with composition, which means a diffusion process is always accompanied by change in volume of the system. To account for this change in volume while solving diffusion equation, it is necessary to know the velocity of the local center of volume ( U V ). An expression is derived for U V , using a treatment that is applicable to a multicomponent system. Simulations of multicomponent diffusion profiles with composition dependent partial molar volumes have been absent in the literature so far. The expression derived in this work is also used to generate diffusion profiles in a hypothetical ternary diffusion couple. Significant difference is observed between the concentration profiles obtained with and without the assumption of constant molar volume. Exact calculation of U V also enables the estimation of the expansion or contraction accompanied by diffusion, which in turn would help in assessing diffusion induced stresses and dimensional changes. [ABSTRACT FROM AUTHOR]

Two moons of Saturn, Janus and Epimetheus, are in co-orbital motion, exchanging orbits approximately every four Earth years as the inner moon approaches the outer moon and they gravitationally interact. The orbital radii of these moons differ by only 50 km (less than the moons' mean physical radii), and it is this slight difference in their orbits that enables their periodic exchanges. Numerical n-body simulations can accurately model these exchanges using only Newtonian physics acting upon three objects: Saturn, Janus, and Epimetheus. Here we present analytical approaches and solutions, and corresponding computer simulations, designed to explore the effects of the initial orbital radius difference on otherwise similar co-orbital systems. Comparison with our simulation results illustrates that our analytic expressions provide very accurate predictions for the moon separations at closest approach and simulated post-exchange orbital radii. Our analytic estimates of the exchange period also match the simulated value for Janus and Epimetheus to within a few percent, although systems with smaller differences in their orbital radii are less well-modeled by our simple approach, suggesting that either full simulations or more sophisticated analytic approaches would be required to estimate exchange periods in those cases. [ABSTRACT FROM AUTHOR]

Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness? From the perspective of Karl Friston's free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness. [ABSTRACT FROM AUTHOR]

To gain a complete understanding of the optical switch that uses phase-change material, Ge2Sb2Te5 (GST), we conducted a simulation considering various physical phenomena related to the switch, such as electrical conduction, Joule heating, heat diffusion, phase change of GST, and light propagation. It was found that the phase state (amorphous and crystalline) of the GST film can be controlled by applying an appropriate pulse voltage to the indium tin oxide (ITO) heater layer, thereby changing the transmittance of the optical switch. Calculations showed that during cooling in the amorphization process, part of the GST film was recrystallized, reducing the transmittance of the optical switch in the ON state. Moreover, the large temperature difference in the GST film during heating was caused by a substantial amount of Joule heat generated in the region distant from the GST film. This difference in distance from the heat-generating region resulted in a considerable temperature difference in the GST film. Thus, herein, we have proposed modified model structures to reduce this temperature difference in the GST film, which can recrystallize during the amorphization process. Calculation using the modified model structures revealed that increasing the thickness of the ITO heater layer, over which the GST film was not placed, can effectively reduce the temperature difference and eventually suppress recrystallization. [ABSTRACT FROM AUTHOR]

The review paper delves into the intricacies of water quality index (WQI) and its relationship with land use and land cover in the Ganga River basin of Uttar Pradesh. The study highlights the significance of the Ganga River, a major perennial river in India, and presents findings from previous paper work shows that 57 sites of Ganga basin show fair, poor, or heavily polluted water quality. Analysis of live storage data within the basin revealed a notable increase in LULC compared to previous years, resulting in decrease water quality indicators such as low rate of dissolved oxygen (DO) levels and high concentrations of pollutants like faecal coliform, total coliform, and nitrate (NO3-). The observed increased in nitrate concentration, particularly attributed to enhance industrial activities and agricultural runoff during the harvesting season, suggests a negative trend towards water quality restoration. The prevalence of untreated commercial and industrial wastewater remains a significant challenge to sustained water quality improvement. In conclusion, the paper advocates for addressing land use land cover of the Ganga basin and ensuring adequate flow releases to rejuvenate the river effectively. It underscores the need for comprehensive measures to sustainably manage land use and cover water resources and mitigate anthropogenic impacts on the middle Ganga River. [ABSTRACT FROM AUTHOR]

Elevator systems serve as the primary mode of transportation in tall buildings which consumes approximately 5–15% of a building's total energy demand. This research explores the potential for energy savings in elevator systems while maintaining passenger comfort through the implementation of green approaches. The research concentrates on a contemporary high-rise office building situated in the central business district of a developing economy. It employs a case study methodology involving traffic simulation to determine optimal elevator specifications for industry-standard service levels. By conducting the simulation, the researchers identified the optimal number of elevators, elevator capacity, speed, and the most suitable elevator management system. Following that, a range of green measures were implemented, including the incorporation of a regenerative system, to effectively reduce the electrical energy consumption of the elevator system. Subsequently, a passenger traffic simulation model was integrated with an energy calculation model to jointly simulate and calculate the elevator system's overall energy consumption and regeneration. The elevator energy requirements were optimized, while maintaining user-friendliness and requirements related to guidelines given in the standards. The results showed that 36% of the energy consumption was reduced by incorporating an energy regenerative option into the elevator system in addition to selecting efficient mechanical components and implementing an efficient elevator traffic management system. This research contributes to the limited body of literature on energy optimization in elevators and emphasizes the importance of balancing energy efficiency with service quality. These findings provide guidance for establishing benchmarks in reducing energy consumption, in relation to elevator systems. [ABSTRACT FROM AUTHOR]

Bulk density is among the fundamental properties for defining quality attributes of dehydrated foods. Mathematical models offer elegant tools for predicting bulk density and therefore optimizing dehydration processes. In this contribution, three machine learning techniques, namely extreme learning machine (ELM), support vector machine (SVM), and evolutionary polynomial regression (EPR), were assessed for predicting bulk density. Comprehensive experimental data including eight variables (product, initial moisture content, initial bulk density, pretreatment, technology, temperature, pressure, and microwave power) from 191 datasets were used to feed the models. All possible combinations of input variables were examined with ELM to obtain the optimum models involving three to eight input variables. The latter were ranked from one to eight according to their impact on the performance of the models in predicting bulk density. The results showed that the product and drying technology are the essential variables for predicting bulk density. The minimum impact on predictions was observed for pretreatment and power. The initial moisture content was shown to be relevant for predicting the bulk density of freeze-dried products. In addition, the best models obtained by ELM were examined and compared with SVM and EPR models. The SVM showed the highest accuracy, but the EPR had the best performance when both the accuracy and simplicity of the models were considered simultaneously. The developed equations can be used as tools for predicting bulk density and exploited in optimizing dehydration processes without needing costly, time-consuming, and energy-demanding experiments. Opportunities and limitations of artificial intelligence have also been discussed. [ABSTRACT FROM AUTHOR]

A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling. [ABSTRACT FROM AUTHOR]

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Considerable spring precipitation occurs over South China (SC), a region that is adjacent to large-scale Asian topography and oceans. Its reasonable simulation is crucial for improving regional climate predictability. This study investigates spring precipitation biases over SC and their possible causes in atmosphere-only and coupled Flexible Global Ocean–Atmosphere–Land System finite-volume version 3 (FGOALS-f3) models with different horizontal resolutions. The performance of spring precipitation simulation over SC varies across different FGOALS-f3 model versions, with the best reproducibility in the high-resolution coupled model (25 km). In the low-resolution atmosphere-only model (100–125 km), the precipitation dry bias over SC is closely linked to overestimated surface sensible forcing over the eastern Tibetan Plateau (TP), which weakens the subtropical anticyclone over the western Pacific (SAWP) through regional circulation responses. By contrast, the high-resolution atmosphere-only model further amplifies surface thermal forcing in the Asian continents, causing intensified land-sea thermal contrast between the Southeast Asian continents and western Pacific, enhanced southerly winds and SAWP, and increased water vapor transport into SC. Meanwhile, the reduced middle-high level cold bias over 10°–30°N in the high-resolution atmosphere-only model intensifies the East Asian westerly jet and ascent over SC, leading to enhanced spring precipitation there. The high-resolution coupled model simulation not only reduces sea surface cold bias over the Bay of Bengal, thus intensifying the Indian-Burma trough and strengthening low-level water vapor transport into SC, but also enhances ascent over SC. As a result, the high-resolution coupled model better reproduces the magnitude and pattern of spring precipitation over SC than its atmosphere-only model. Compared with low-resolution models, the domain-mean spring precipitation dry bias decreases by 11.2% over SC in the high-resolution atmosphere-only model and by 35.9% in the coupled model. These results demonstrate that the high-resolution FGOALS-f3 models can improve simulations of the influencing atmospheric circulations and spring precipitation over SC. [ABSTRACT FROM AUTHOR]

Conversational Artificial Intelligence (CAI) systems (also known as AI "chatbots") are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not "digital therapists" and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in mental health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects' affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy. [ABSTRACT FROM AUTHOR]