Development Of Model Research Articles

Considering the Secondary Use of Clinical and Educational Data to Facilitate the Development of Artificial Intelligence Models.

Medical training programs and health care systems collect ever-increasing amounts of educational and clinical data. These data are collected with the primary purpose of supporting either trainee learning or patient care. Well-established principles guide the secondary use of these data for program evaluation and quality improvement initiatives. More recently, however, these clinical and educational data are also increasingly being used to train artificial intelligence (AI) models. The implications of this relatively unique secondary use of data have not been well explored. These models can support the development of sophisticated AI products that can be commercialized. While these products have the potential to support and improve the educational system, there are challenges related to validity, patient and learner consent, and biased or discriminatory outputs. The authors consider the implications of developing AI models and products using educational and clinical data from learners, discuss the uses of these products within medical education, and outline considerations that should guide the appropriate use of data for this purpose. These issues are further explored by examining how they have been navigated in an educational collaborative.

From Finite Element Simulations to Equivalent Circuit Models of Extracellular Neuronal Recording Systems Based on Planar and Mushroom Electrodes.

define a new methodology to build multi-compartment lumped-elements equivalent circuit models for neuron/electrode systems. the equivalent circuit topology is derived by careful scrutiny of accurate and validated multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of the neuron membrane ion channels, and signal acquisition by the electronic readout. robust and accurate circuit models are systematically derived, suited to represent the dynamics of the sensed extracellular signals over a wide range of geometrical/physical parameters (neuron and electrode sizes, electrolytic cleft thicknesses, readout input impedance, non-uniform ion channel distributions). FEM simulations point out phenomena that escape an accurate description by equivalent circuits; notably: steric effects in the thin electrolytic cleft and the impact of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model. our multi-compartment equivalent circuits match accurately the FEM simulations. They unveil the existence of an optimum number of compartments for accurate circuit simulation. FEM simulations suggest that while steric effects are in most instances negligible, the extracellular ion transport affects the reversal potentials and consequently the recorded signal if the electrolytic cleft becomes thinner than approximately 100 nm. the proposed methodology and circuit models improve upon the existing area and point contact models. The coupling between the extracellular concentrations and reversal potential highlighted by FEM simulations emerges as a challenge for future developments in lumped-element modeling of the neuron/sensor interface.

A signal detection-based confidence-similarity model of face matching.

Face matching consists of the ability to decide whether two face images (or more) belong to the same person or to different identities. Face matching is crucial for efficient face recognition and plays an important role in applied settings such as passport control and eyewitness memory. However, despite extensive research, the mechanisms that govern face-matching performance are still not well understood. Moreover, to date, many researchers hold on to the belief that match and mismatch conditions are governed by two separate systems, an assumption that likely thwarted the development of a unified model of face matching. The present study outlines a unified unequal variance confidence-similarity signal detection-based model of face-matching performance, one that facilitates the use of receiver operating characteristics (ROC) and confidence-accuracy plots to better understand the relations between match and mismatch conditions, and their relations to factors of confidence and similarity. A binomial feature-matching mechanism is developed to support this signal detection model. The model can account for the presence of both within-identities and between-identities sources of variation in face recognition and explains a myriad of face-matching phenomena, including the match-mismatch dissociation. The model is also capable of generating new predictions concerning the role of confidence and similarity and their intricate relations with accuracy. The new model was tested against six alternative competing models (some postulate discrete rather than continuous representations) in three experiments. Data analyses consisted of hierarchically nested model fitting, ROC curve analyses, and confidence-accuracy plots analyses. All of these provided substantial support in the signal detection-based confidence-similarity model. The model suggests that the accuracy of face-matching performance can be predicted by the degree of similarity/dissimilarity of the depicted faces and the level of confidence in the decision. Moreover, according to the model, confidence and similarity ratings are strongly correlated. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A structural equation modeling approach for modeling variability as a latent variable.

Drawing upon recent developments in structural equation modeling, the current study presents an analytical framework for addressing research questions in which, rather than focusing on means, it is intraindividual (or intragroup) variability that is of direct research interest. Beyond merely serving as an alternative to existing multilevel modeling approaches, this framework allows for extensions to accommodate a variety of complex research scenarios by parameterizing variability as a latent variable that can in turn be embedded within a broader covariance and mean structure involving other observed and/or latent variables. The estimation procedures and parameter interpretation for the latent random variability models are discussed. The versatility of the proposed methods is demonstrated through four empirical examples. The Mplus, BUGS, and Stan model syntax for the illustrative examples are supplied to facilitate the application of the methods. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

758 An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery

INTRODUCTION: Overwhelming evidence indicates that the quality of reporting of predictive models is poor, often being confounded by small datasets, inappropriate statistical methods, a lack of validation, and reproducibility. METHODS: Articles reporting prediction models published in the top five neurosurgery journals by SJR2-rank (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified via a PubMed search strategy that combined terms related to machine learning and prediction models. The identified original research articles were analyzed against the TRIPOD criteria. RESULTS: 110 articles were included and analyzed against the TRIPOD checklist. Among the articles screened, the median compliance was 57.4% (50.0-66.7%). Models utilizing machine learning-based models exhibited lower compliance on average compared to conventional learning-based models (57.1%, 50.0-66.7% vs. 68.1%, 50.2-68.1%, p = 0.472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures including confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%). CONCLUSIONS: Published machine learning models predicting outcomes in neurosurgery often fall short of meeting the established guidelines laid out by TRIPOD for their development, validation, and reporting. This lack of compliance may indicate the extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up. Data of N = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from k = 6 randomized trials (digital stress intervention vs. waitlist) were collected. Prognostic models were developed using multilevel least absolute shrinkage and selection operator and boosting algorithms, and were validated using bootstrap bias correction and internal-external cross-validation. Subsequently, expected effects among those with and without a treatment recommendation were estimated based on clinically derived treatment assignment cut points. Performances ranged from R² = 21.0%-23.4%, decreasing only slightly after model optimism correction (R² = 17.0%-19.6%). Predictions were greatly improved by including an interim assessment of depressive symptoms (optimism-corrected R2 = 32.6%-35.6%). Using a minimally important difference of d = -0.24 as assignment cut point, approximately 84.6%-93.3% of patients are helped by this type of intervention, while the remaining 6.7%-15.4% would experience clinically negligible benefits (δ^ = -0.02 to -0.19). Using reliable change as cut point, a smaller subset (39.3%-46.2%) with substantial expected benefits (δ^ = -0.68) receives a treatment recommendation. Meta-analytic prognostic models applied to individual participant data can be used to predict differential benefits of a digital stress intervention as an indirect treatment for depression. While most patients seem to benefit, the developed models could be helpful as a screening tool to identify those for whom a more intensive depression treatment is needed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Sensitivity of HAFS-B Tropical Cyclone Forecasts to Planetary Boundary Layer and Microphysics Parameterizations

Abstract Understanding how model physics impact tropical cyclone (TC) structure, motion, and evolution is critical for the development of TC forecast models. This study examines the impacts of microphysics and planetary boundary layer (PBL) physics on forecasts using the Hurricane Analysis and Forecast System (HAFS), which is newly operational in 2023. The “HAFS-B” version is specifically evaluated, and three sensitivity tests (for over 400 cases in 15 Atlantic TCs) are compared with retrospective HAFS-B runs. Sensitivity tests are generated by 1) changing the microphysics in HAFS-B from Thompson to GFDL, 2) turning off the TC-specific PBL modifications that have been implemented in operational HAFS-B, and 3) combining the PBL and microphysics modifications. The forecasts are compared through standard verification metrics, and also examination of composite structure. Verification results show that Thompson microphysics slightly degrades the days 3–4 forecast track in HAFS-B, but improves forecasts of long-term intensity. The TC-specific PBL changes lead to a reduction in a negative intensity bias and improvement in RI skill, but cause some degradation in prediction of 34-kt (1 kt ≈ 0.51 m s−1) wind radii. Composites illustrate slightly deeper vortices in runs with the Thompson microphysics, and stronger PBL inflow with the TC-specific PBL modifications. These combined results demonstrate the critical role of model physics in regulating TC structure and intensity, and point to the need to continue to develop improvements to HAFS physics. The study also shows that the combination of both PBL and microphysics modifications (which are both included in one of the two versions of HAFS in the first operational implementation) leads to the best overall results. Significance Statement A new hurricane model, the Hurricane Analysis and Forecast System (HAFS), is being introduced for operational prediction during the 2023 hurricane season. One of the most important parts of any forecast model are the “physics parameterizations,” or approximations of physical processes that govern things like turbulence, cloud formation, etc. In this study, we tested these approximations in one configuration of HAFS, HAFS-B. Specifically, we looked at two different versions of the microphysics (modeling the growth of water and ice in clouds) and boundary layer physics (the approximations for turbulence in the lowest level of the atmosphere). We found that both of these sets of model physics had important effects on the forecasts from HAFS. The microphysics had notable impacts on the track forecasts, and also changed the vertical depth of the model hurricanes. The boundary layer physics, including some of our changes based on observed hurricanes and turbulence-resolving models, helped the model better predict rapid intensification (periods where the wind speed increases quickly). Work is ongoing to improve the model physics for better forecasts of rapid intensification and overall storm structure, including storm size. The study also shows the combination of both PBL and microphysics modifications overall leads to the best results and thus was used as one of the two first operational implementations of HAFS.

Development of a security model for protecting the privacy of patients in a cloud-based information system

This work presents a security model in which medical professionals and other users can have access to patient’s health data stored in the cloud without knowledge of the identity of the particular patient which the database belongs to. The access control techniques used in this project grants classifier access to patient data, that is to say, it grants access to data based on the role of the data user. Thus, the aim and objective of the project is achieved.

Wildlife as sentinels for emerging zoonotic diseases: A review of surveillance systems in the USA

Wildlife plays a crucial role as sentinels for emerging zoonotic diseases, serving as early indicators of potential threats to human and animal health. This review examines the surveillance systems in the USA that utilize wildlife as indicators for the presence of emerging zoonotic pathogens. The review focuses on the methodologies, challenges, and implications of these surveillance systems. In the USA, wildlife surveillance for zoonotic diseases is primarily conducted through passive surveillance, where wildlife carcasses are collected and tested for pathogens. Additionally, active surveillance programs target specific wildlife species known to host zoonotic pathogens or species that may be in close contact with humans or domestic animals. These surveillance efforts are complemented by the use of sentinel species, such as birds or bats, which are monitored for signs of disease that could indicate the presence of zoonotic pathogens. Challenges in wildlife surveillance include the vast geographic range of many wildlife species, making it difficult to sample populations comprehensively. Additionally, the diversity of wildlife species and habitats in the USA presents logistical challenges for surveillance efforts. Furthermore, there are challenges related to data sharing and coordination among agencies responsible for wildlife and public health. The implications of wildlife surveillance for public health policy and practice are significant. Early detection of zoonotic pathogens in wildlife can lead to timely public health interventions, such as vaccination campaigns or changes in land use practices to reduce human-wildlife contact. Moreover, wildlife surveillance can inform the development of predictive models for disease outbreaks, enabling more effective preparedness and response measures. In conclusion, wildlife surveillance plays a critical role in the early detection and monitoring of emerging zoonotic diseases in the USA. Continued investment in surveillance systems and research is essential to enhance our understanding of zoonotic disease dynamics and improve our ability to protect human and animal health.

The impact of digital transformation on business development strategies: Trends, challenges, and opportunities analyzed

Digital transformation has become a crucial driver of business development, reshaping industries and redefining how companies operate. This review analyzes the trends, challenges, and opportunities arising from this transformative process. Companies are increasingly adopting digital technologies such as cloud computing, AI, IoT, and big data analytics to streamline operations, improve customer experiences, and drive innovation. Digital transformation has led to a focus on understanding and meeting customer needs through personalized experiences and targeted marketing strategies. COVID-19 pandemic accelerated the adoption of remote work and digital collaboration tools, leading to new ways of working and collaborating across teams and organizations. Many organizations struggle with outdated legacy systems and infrastructure, making it challenging to integrate new digital technologies. As companies collect and analyze more data, ensuring data privacy and security has become a major concern. There is a growing demand for professionals with digital skills, but many organizations face challenges in finding and retaining talent. Digital transformation enables companies to collect and analyze vast amounts of data, providing valuable insights into customer behavior and preferences. Automation and digital technologies can streamline processes, reduce costs, and improve efficiency. Digital transformation opens up new opportunities for innovation and the development of new business models, enabling companies to stay competitive in a rapidly changing market. Digital transformation is reshaping business development strategies, driving innovation, and creating new opportunities for growth. However, it also presents challenges, such as legacy systems, data security, and skills gaps, that organizations must address to fully realize the benefits of digital transformation.

Recent Developments in Financial Innovation Research and Sustainable Accounting Models Bibliometric Analysis approach

This study conducts a systematic bibliometric analysis to examine recent developments in financial innovation research and sustainable accounting models. The intertwining of financial innovation and sustainability reflects a significant shift in the global economic paradigm, with innovations such as green bonds and impact investing reshaping financial markets. The research aims to identify key themes, trends, and interdisciplinary connections within the literature, offering insights for academia, practitioners, policymakers, and other stakeholders. Analysis reveals a growing emphasis on the role of financial innovation in driving sustainable growth and the importance of sustainable accounting in fostering transparency and accountability. The study also highlights emerging research directions, including interdisciplinary approaches, drivers of innovation and development, microfinance impact on household economics, and the nexus between education and health. Ultimately, this research seeks to contribute to the advancement of knowledge and practices promoting both financial prosperity and environmental and social well-being.

Pengembangan LKPD Elektronik Berbasis Masalah Terintegrasi Potensi Lokal TPST Piyungan pada Materi Perubahan Lingkungan untuk Meningkatkan Kemampuan Pemecahan Masalah dan Literasi Lingkungan Peserta Didik Kelas X SMA

This research aims to determine the characteristics and produce problem-based electronic student worksheets that are integrated with the local potential of TPST Piyungan in appropriate, practical, and effective environmental change material to improve students' problem-solving abilities and environmental literacy. This research is research and development (R&D) using the 4D model (Define, Design, Develop, and Disseminate). A field trial of electronic worksheets was carried out through quasi-experimental research with a pretest-posttest control group design. The field trial subjects were 72 students from two classes. Data analysis was carried out using the MANOVA test to analyze differences in students' problem-solving abilities and environmental literacy. The results of the research show that based on the assessment of expert lecturers, the content of electronic worksheets is very suitable for encouraging problem-solving abilities and environmental literacy, and the MANOVA test results show that there are differences in problem-solving ability and environmental literacy when using electronic worksheets based on problems integrated with the local potential of TPST Piyungan. It can be concluded that the integrated worksheet based on local potential problems at TPST Piyungan is feasible, practical, and effective for use in implementing environmental change materials

Development of Numeracy Assessment Based on Ethnomathematics in the Merdeka Curriculum Phase B

The independent curriculum develops learning according to the interests, learning styles, and abilities of students, which are connected to teacher competencies. The phases of the independent curriculum in elementary school are Phase A, Phase B, and Phase C. In the independent curriculum, there is a Minimum Competency Assessment (MCA), which contains numeracy literacy. This research aims to produce a numeracy assessment product for the phase-B independent curriculum that contains Ethnomathematics. Ethnomathematics is the integration of curriculum, pedagogy, mathematics, and local wisdom. Local wisdom used as ethnomathematics integration makes students trained and able to explore mathematical concepts around the environment. This research method is Research And Development (R&D) with the 4D model (Define, Design, Develop, and Desseminase). The results of material validation are 4.9 with a score of 98% conclusion worth using, the results of language validation are an average of 4.6 with a score of 92% conclusion worth using, and the results of material validation 4.7 with a score of 94% conclusion worth using. The average pretest result of trial-1 was 61, and trial-2 was 62.5. The posttest results from trial-1 were 88.4, and trial-2 were 92. The N-Gain Score results obtained 0.715, which means high, with the interpretation of the effectiveness of N-Gain 71.5%, which means effective. Student response questionnaire results in 83% very satisfied category. It is concluded that the ethnomathematics-based numeracy assessment in the independent curriculum Phase B developed is feasible to use and effective in learning mathematics.

Diverse perspectives in zebrafish seizure models: An exploration of chemical inducers

Numerous studies have investigated seizures in zebrafish, employing diverse methods to induce and observe epilepsy. Chemical inducers have become indispensable tools for precisely manipulating neural activity in controlled experiments. This research is dedicated to a meticulous comparison and analysis of various seizure-inducing chemicals in zebrafish. The selected chemical inducers include pentylenetetrazole (PTZ), kainic acid (KA), picrotoxin (PTX), pilocarpine, and other substances. The primary mechanism of these inducers involves altering neurotransmitter receptors, particularly GABA and glutamate, at inhibitory or excitatory synapses. This alteration creates an imbalance between inhibitory and excitatory signals in the brain, contributing to epileptogenesis. The resulting hyperactivity in the zebrafish brain disrupts metabolism, elevates reactive oxygen species (ROS), and causes neuronal damage. Chemical induction offers valuable insights into the neurological activity of zebrafish, enabling the observation of behaviors such as whole-body contractions, loss of posture, or freezing. Various parameters can be adjusted to examine the effects of chemical inducers on zebrafish development and to replicate various types of seizures, such as status epilepticus and temporal lobe epilepsy. Despite the array of options available, many chemical inducers remain underutilized in zebrafish studies. To ensure the successful induction of models, monitoring gene expression markers such as c-fos, which represents neuronal activity, becomes crucial. Additionally, a scoring system is employed to analyze seizure severity and identify treatment efficacy. The existing gaps in utilizing chemical inducers present great opportunities for future model development. This chemically-induced seizure model in zebrafish is pivotal for unraveling seizure mechanisms, contributing significantly to advancements in neurological research.

Development of a numerical model for RC columns considering seismic dynamic effect and different failure modes

Due to the strain rate sensitivity of concrete and reinforcing steel materials, the mechanical behaviors of RC column members subjected to dynamic loading are significantly different from those measured under static loading, i.e. the dynamic effect. In this paper, a numerical model capable of reflecting the dynamic effect and different failure modes is innovatively developed for RC columns by adding the modified sub-elements to the fiber beam-column element on the OpenSees platform. The methods for considering the dynamic effect on multiple deformation components, including the flexural, shear and bond-slip deformations are put forward. Moreover, the accuracy and effectiveness of the proposed model are verified by comparing the numerical and experimental hysteretic curve and deformation components of RC columns under different failure modes and loading rates. By analyzing the simulated results of hysteretic behavior using the modified and unmodified model, the necessity of dynamic modification to the deformation sub-elements are highlighted. It is revealed that the proposed numerical model can provide reasonable predicted results to the dynamic hysteretic behavior and failure mode of RC column members, and can be further applied to improve the accuracy of seismic performance assessment for RC structures.

Tropical cyclones in global high-resolution simulations using the IPSL model

AbstractDespite many years of extensive research, the evolution of Tropical Cyclone (TC) activity in our changing climate remains uncertain. This is partly because the answer to that question relies primarily on climate simulations with horizontal resolutions of a few tens of kilometers. Such simulations have only recently become accessible for most modeling centers, including the Institut Pierre-Simon Laplace (IPSL). Using recent numerical developments in the IPSL model, we perform a series of historical atmospheric-only simulations that follow the HighResMIP protocol. We assess the impact of increasing the resolution from $${\sim }\, 200$$ ∼ 200 to 25 km on TC activity. In agreement with previous work, we find a systematic improvement of TC activity with increasing resolution with respect to the observations. However, a clear signature of TC frequencies convergence with resolution is still lacking. Cyclogenesis geographical distributions also improve at the scale of individual basins. This is particularly true of the North Atlantic, where the agreement with the observed distribution is impressive at 25 km. In agreement with the observations, TC activity correlates with the large-scale environment and ENSO in that basin. By contrast, TC frequencies remain too small in the Western North Pacific at 25 km, where significant biases of humidity and vorticity are found compared to the reanalysis. Despite the few minor weaknesses we identified, our results demonstrate that the IPSL model is a suitable tool for studying TCs on climate time scales. This work thus opens the way for further studies contributing to our understanding of TC climatology.

Development of a diagnostic model for pre-washout screening of primary aldosteronism.

Primary aldosteronism (PA) diagnosis is affected by antihypertensive drugs that are commonly taken by patients with suspected PA. In this study, we developed and validated a diagnostic model for screening PA without drug washout. We retrospectively analyzed 1095 patients diagnosed with PA or essential hypertension. Patients were randomly grouped into training and validation sets at a 7:3 ratio. Baseline characteristics, plasma aldosterone concentration (PAC), and direct renin concentration (DRC) before and after drug washout were separately recorded, and the aldosterone-to-renin ratio (ARR) was calculated. PAC and ARR were higher and direct renin concentration was lower in patients with PA than in patients with essential hypertension. Furthermore, the differences in blood potassium and sodium concentrations and hypertension grades between the two groups were significant. Using the abbreviations potassium (P), ARR (A), PAC (P), sodium (S), and hypertension grade 3 (3), the model was named PAPS3. The PAPS3 model had a maximum score of 10, with the cutoff value assigned as 5.5; it showed high sensitivity and specificity for screening PA in patients who exhibit difficulty in tolerating drug washout. PA screening remains crucial, and standard guidelines should be followed for patients to tolerate washout. The PAPS3 model offers an alternative to minimize risks and enhance diagnostic efficiency in PA for those facing washout challenges. Despite its high accuracy, further validation of this model is warranted through large-scale clinical studies.

Development of electricity simulation model of urban houses and evaluating surplus electricity of photovoltaics (PV) considering housing stock transformation

The Japanese government is advocating for the introduction of decentralized renewable energy devices such as photovoltaics (PV) and high thermal performance houses in urban areas. Due to variations in housing stock among different urban areas, the implementation of these houses and devices would have diverse effects on the supply and demand sides, potentially leading to diverse overproduction of PV electricity, referred to as surplus electricity. Existing research focuses on increasing the utilization of surplus electricity and quantifying the surplus electricity from individual buildings at a regional scale. Considering the lack of research that quantifies surplus electricity through shared PVs under the urban-scale contextual changes (widespread introduction of high-performance houses). Therefore, this study aims to address this gap by utilizing an archetype house simulation model to simulate and compare two scenarios (with or without houses retrofitting) regarding electricity demand for each street-level area, maximum power and duration time of surplus electricity in areas where surplus electricity would occur in the target city Fukuoka in 2025. The results indicate that, under the current residential PV stock and existing introduction rates, the peak surplus power occurs around noon in May, approximately 396 out of 1051 areas in Fukuoka would generate surplus electricity during 2025. Houses retrofitting would increase the surplus electricity (include maximum power and duration time) in these areas. Additionally, this study identifies the residential characteristics of areas that generate surplus electricity, including construction year, household composition, and the proportion of detached houses, whether with retrofitting or not. The results suggest attention to surplus electricity increasing in areas with a high proportion of detached houses when considering houses retrofitting.

Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine–acetic acid solution

In recent years, near-infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution by integrating near-infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z-score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near-infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.