Objective Of Project Research Articles

Understanding and using experiences of social care to guide service improvements: translating a co-design approach from health to social care.

Local authorities need to find new ways of collecting and using data on social care users' experiences to improve service design and quality. Here we draw on and adapt an approach used in the healthcare improvement field, accelerated experience-based co-design, to see if it can be translated to social care. We use loneliness support as our exemplar. To understand how loneliness is understood and experienced by members of the public and characterised by social care and voluntary sector staff; to identify service improvements around loneliness support; to explore whether accelerated experience-based co-design is effective in social care; and to produce new resources for publication on Socialcaretalk.org. Discovery phase: in-depth interviews with a diverse sample of people in terms of demographic characteristics with experience of loneliness, and 20 social care and voluntary staff who provided loneliness support. Production of a catalyst film from the public interview data set. Co-design phase: exploring whether the accelerated experience-based co-design approach is effective in one local authority area via a series of three workshops to agree shared priorities for improving loneliness support (one workshop for staff, another for people with experience of local loneliness support, and a third, joint workshop), followed by 7-monthly meetings by two co-design groups to work on priority improvements. A process evaluation of the co-design phase was conducted using interviews, ethnographic observation, questionnaires and other written material. Accelerated experience-based co-design demonstrated strong potential for use in social care. Diverse experiences of participants and fuzzy boundaries around social care compared to health care widened the scope of what could be considered a service improvement priority. Co-design groups focused on supporting people to return to pre-pandemic activities and developing a vulnerable passenger 'gold standard' award for taxi drivers. This work generated short-term 'wins' and longer-term legacies. Participants felt empowered by the process and prospect of change, and local lead organisations committed to take the work forward. Using an exemplar, loneliness support, that does not correspond to a single pathway allowed us to comprehensively explore the use of accelerated experience-based co-design, and we found it can be adapted for use in social care. We produced recommendations for the future use of the approach in social care which include identifying people or organisations who could have responsibility for implementing improvements, and allowing time for coalition-building, developing trusted relationships and understanding different perspectives. COVID-19 temporarily affected the capacity of the local authority Project Lead to set up the intervention. Pandemic work pressures led to smaller numbers of participating staff and had a knock-on effect on recruitment. Staff turnover within Doncaster Council created further challenges. Exploring the approach using a single pathway, such as assessing eligibility for care and support, could add additional insights into its transferability to social care. This trial is registered as Current Controlled Trials ISRCTN98646409. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR128616) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 27. See the NIHR Funding and Awards website for further award information.

Variability Census of Legacy Catalogs. III. 8600+ New δ Scuti and γ Doradus Stars* *Dedicated to my wife Jingyun Zhang.

I present the identification of over 4600 new δ Scuti and 4000 new γ Doradus stars, which represent the third phase results of a variability vetting census project in BD, HD, SAO, and PPM catalogs. Across project phases I–III (scanning 468,862 catalog objects), I have identified over 9690 δ Scuti, 7680 γ Doradus, 2600+ Maia variables, 1030+ eclipsing binaries, 20+ RR Lyrae stars, 180+ heartbeat candidates, 9800+ rotating variables (including subtypes of 370+ ELL, 800+ ACV, 100+ SXARI, and hundreds of solar-like oscillators), and several other types as new variables. Notably, more than 120 eclipsing binary systems have been identified with pulsating primary components of either DSCT or GDOR type, while 24 more binaries feature overlapping Maia-type pulsation. Moreover, over 2200 of these variables exhibit hybrid δ Sct-γ Dor pulsations.

Combining BIM and Strategic Management for Efficient Planning and Execution of Construction Projects

Construction projects are faced with a range of challenges. These can be addressed significantly by combining Building Information Modeling (BIM) and strategic management (SM) right from planning to execution phases of construction projects. This study argues that these two problem-solving mechanisms can be leveraged in combination for the attainment of efficient planning and execution of construction projects. It draws evidence from secondary data, sourced from the internet and subjected to systematic review and thematic and content analyses. The descriptive analysis done proves BIM and SM to be indeed capable of ensuring efficient planning and execution of construction projects, when duly deployed and the constraints to their application tackled sufficiently. The study concludes that the combination of BIM and SM produces more results and guarantees efficient planning and execution of construction projects much more than when either of them is used alone. Organizations and institutions are charged to resort to combining these strategies as well as others in finding solutions to problems such as those confronting construction projects.

Selection of agile project management approaches based on project complexity

AbstractManaging software development projects is a complex endeavor due to the constant emergence of unforeseen events that deviate from initial expectations. A competent project leader is not just someone who follows the planned course but also adept at handling and minimizing inconveniences, ultimately striving to achieve results that align as closely as possible with the desired outcome. However, individuals involved in technological development often cling to familiar tools that have previously yielded positive outcomes, even when those tools may not be the best fit for the current project context. The Agile Manifesto has significantly transformed project management, infusing the discipline with a fresh perspective. Nevertheless, there remain several challenges to overcome. In this article, we aim to provide a guide that addresses these difficulties and minimizes their impact. We explore the selection of key factors that adequately describe a project's complexity, which can subsequently be used in conjunction with the Cynefin framework to categorize management strategies, techniques, and tools based on their applicability to specific complexities. Additionally, we offer insights on adapting project management approaches throughout the project life cycle in response to changes in reality, utilizing the dynamics outlined by the Cynefin framework. Finally, we present suitable strategies, techniques, and tools for agile project management based on the complexity context assigned by the Cynefin framework.

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

Increasing intensity of enterovirus outbreaks projected with climate change

Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.

Development of a prototype to evaluate and monitor energy efficiency aiming at power quality 4.0

In research and development projects, the need to implement prototypes to speed up the development and delivery process is paramount, offering tools to validate scenarios and anticipate challenges. The aim of this research is to experimentally develop a prototype for monitoring electrical power systems using a gateway that converts MODBUS-RTU communication protocols to TCP, providing useful information for technicians and managers of a company in the Manaus Industrial Estate (PIM) in the field of reflective tapes. To achieve this goal, it was necessary to specify the technical requirements for panel assembly and communication via MODBUS, acquire and research materials in accordance with the technical specifications and technological requirements, implement the monitoring system with an IMS Controller for reactive power compensation, and configure and parameterise the MODBUS protocol via the RTU to TCP converter. The prototype is expected to be able to take accurate measurements and, in the data acquisition process, obtain readings of electrical quantities such as: voltage per phase; current per phase; power factor per phase; active power per phase; reactive power per phase; apparent power per phase; frequency; voltage THD and voltage harmonics. The prototype must act intelligently to drive the capacitor bank to control and monitor reactive power. The development of the prototype included crucial stages such as the specification of technical requirements, the acquisition of suitable materials, and the implementation and configuration of the system. The specification ensured that the components were aligned with the project objectives, while the procurement of materials ensured the quality and efficiency of the system. The implementation of the Automatic Reactive Power Compensation Controller was vital for reactive power compensation and the configuration of the MODBUS protocol ensured precise communication between the components. To summarise, this project demonstrates the importance of prototypes in technological advancement, offering practical and efficient solutions for industry. The prototype developed to monitor electrical power systems through a MODBUS-RTU to TCP protocol converter gateway represents a significant advance, providing essential data and contributing to informed decision-making, improving the performance and management of the company's energy resources.

LEAN SIX SIGMA APPROACH FOR SIMULTANEOUS QUALITY IMPROVEMENT OF COURSES IN HIGHER EDUCATION INSTITUTIONS DURING COVID-19 PANDEMIC

The purpose of this study is to assess whether Lean Six Sigma can improve the quality of teaching-learning processes in higher educational institutions during the Covid-19 pandemic. The integration of e-learning technologies with lean six sigma (LSS). DMAIC stands for five phases of sig sigma project: define, measure, analyze, improve, and control was applied to implement quality assurance in the teaching-learning process. Action research was adopted in 30 courses of a particular engineering programme. The methodology was applied as a course improvement tool, and the challenges encountered during the implementation were also studied. The LSS DMAIC methodology could successfully improve the quality of various courses simultaneously through online mode during the Covid-19 pandemic. The adopted methodology could assist in effectively responding during the present stage of uncertainty and provide stakeholders with guidelines to deal with the post-pandemic era. In terms of practical implications, this study discusses a new approach to improving the quality of the teaching-learning process in higher educational institutions. The LSS DMAIC is applied with the help of online educational and social media platforms for the simultaneous improvement of courses in a particular engineering programme.

Determination of Recreatıon Areas in Acarlar Longoz

The protection of nature and the environment, the preservation of the diversity of ecosystems without jeopardizing their integrity, and their sustainable development are important for sustainability. The Acarlar Floodplain is an extraordinary tourism and recreation area in terms of its ecological and landscape features. In the Acarlar Floodplain, which has recently become an important tourist destination, it is important to determine the attractions for daily recreation without neglecting the conservation value. The aim of this study is to identify suitable areas for hiking and birdwatching in the region using the weighted overlay method. The results show that most of the region is restricted areas for targeted activities, especially birdwatching, due to the different conservation status of Acar Longoz. The 1st degree suitable areas for hiking activities cover 226 ha and 4169 ha fall under the 2nd degree suitable category. The 1st degree suitable areas for birdwatching cover 16 ha, while the 2nd degree suitable areas cover 487 ha. The results provide essential inputs and an exemplary scientific methodology for the development of the current ecotourism project and wetland management plans.

OPTIMIZATION OF THE DEVELOPMENT PROCESS OF MONOLITHIC MULTI-MODULE PROJECTS IN JAVA

In recent years, there has been an increase in the complexity of Java software development and a change in the scope of projects, including an increase in the number of modules in projects. The multi-modularity of projects, although it improves manageability to a certain extent, but often creates a number of problems that can complicate development and, a problem that will appear in the future, require more resources to support. This article will analyze the main problems of monolithic multi-module Java projects and will try to consider a number of possible solutions to overcome the above problems. The article discusses the peculiarities of working with multi-module monolithic projects using Java as the main programming language. The purpose of this article is to identify features and obstacles using the above architectural approach of the software, analysis of the main possible issues of working with the monolithic multi-module Java projects, as well as providing recommendations for eliminating these obstacles or describing the features of the process that could help engineers in supporting this kind of projects. In other word the main goal of this work is to create recommendations, provide modern best practices for working with monolithic multi-modular software architecture and the most popular modern technological solutions used in corporate development. The proposed recommendations allow the team, primarily developers and the engineering side, to avoid possible obstacles that lead to the loss of efficiency of the monolithic software development process. The most important advantage, from the recommendations given in the article, is the optimization of resource costs (time, money and labor) for the development process. As a result of the article, a general list of recommendations was obtained, which allows the developer to better analyze what changes in the project should (if necessary) be made to optimize the development, assembly and deployment processes of a monolithic Java project, as well as advice before designing new software to avoid the main obstacles of monolithic architecture in the future.

Improving Coordination to Strengthen Operationalization of Malaria Surveillance and Routine Data Quality: Landscape Analysis of Current Surveillance-Related Initiatives.

Improving the visibility and global coordination of malaria surveillance and data quality improvement initiatives is required to optimize sharing of best practices, tools, and approaches and to promote efficient, effective, and equitable distribution of resources. With these aims in mind, Rollback Malaria's Surveillance, Monitoring and Evaluation Working Group established the Surveillance Practice and Data Quality Committee in May 2021. As a priority initiative, the committee conducted a landscape analysis of implementing partners' (IPs') malaria surveillance-related projects. A questionnaire that included questions on current project objectives, activities, geographic scope, and lessons learned was distributed among committee members and other IPs. Three years since its inception, information has been submitted regarding 49 projects by 25 IPs and funded by 17 donors. To present and share the landscaping results, an interactive dashboard was published to the Rollback Malaria's Global Malaria Dashboard website (endmalaria.org) in March 2021. It is the first time that multiple stakeholders have shared such information regarding surveillance projects.

Stress-dependent Mohr–Coulomb shear strength parameters for intact rock

Rock strength is imperative for the design and stability analysis of engineering structures. The Mohr–Coulomb (M-C) criterion holds significant prominence in geotechnical engineering. However, the M-C criterion fails to accurately capture the nonlinear strength response and neglects the critical state of rocks, potentially leading to inaccuracies in the design phase of deep engineering projects. This study introduces an innovative stress-dependent friction angle and cohesion (SFC) for the M-C criterion to capture the nonlinear strength responses of intact rocks, spanning from non-critical to critical states (brittle to ductile regions). A novel method for determining these stress-dependent parameters at each corresponding σ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{3}$$\\end{document} is initially introduced. Subsequently, an examination of the confinement dependency of the friction angle and cohesion is conducted, leading to the derivation of the SFC model. The SFC-enhanced M-C criterion, utilizing parameters obtained from triaxial tests under lower σ3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma_{3}$$\\end{document}, demonstrates the capability to delineate the complete non-linear strength envelope from brittle to ductile regions. Validation through triaxial test data confirms that the predictions of the SFC-enhanced M-C criterion accurately correspond to the strength characteristics of the tested rocks.

A Recognition System for Devanagari Handwritten Digits Using CNN

A Recognition System for Devanagari Handwritten Digits using CNN, a novel approach to recognizing transcribed digits in the Devanagari script using Convolutional Neural Networks (CNN). This framework represents a significant contribution to the field of pattern recognition and language processing objective of the research project is to perform a literature review, identify an algorithm for a digits recognition system implement the Devanagari digits recognition system for educational activities. In the first phase, a dataset of 150 transcribed digit images is curated, allocating 75% for training (113 images) and 25% for validation (37 images). A Convolutional Neural Network (CNN) is designed with five convolutional layers, each utilizing 3 × 3 filters with 16, 32, 64, 128, and 128 feature maps, respectively. The experiments conducted involve varying the number of epochs, with results captured at 5, 10, 20, and 100 epochs. This comprehensive evaluation aims to understand the model's convergence and performance over different training durations. The outcomes of this phase contribute to the fine-tuning and optimization of the model for subsequent phases. In the second phase, the dataset is expanded to 100*10 (1000) images, each resized to 28 × 28 pixels through cropping. The CNN architecture remains consistent, with the previously determined layer configuration. Similar experiments are conducted, assessing the model's performance over 5, 10, 20, and 100 epochs. This model with a data size of 1000 demonstrates superior accuracy (100% on mini-batches) compared to the 150 model, with consistently high validation accuracy, while both models exhibit decreasing trends in mini-batch and validation losses, favoring the larger dataset, and maintaining a constant learning rate at 0.0100, albeit with a slightly longer time elapsed for each epoch due to the increased data size. 98.37398 accuracy in the phase 2 experiment in 100 epochs. Similar research and contributions and Devanagari’s character and word recognition system.

Projection of snowfall and precipitation phase changes over the Northwest China based on CMIP6 multimodels

The change of precipitation phase could have profound influence on water cycle and ecological environment in Northwest China (NWC). It has been observed that snowfall is inclined to transition into rainfall under climate warming. However, the evolution process of precipitation phase in the future over the NWC remains poorly understanding. In this study, the performance of snowfall simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated by results of observation-based rain/snow separation, subsequently analyzing the changes in snowfall and precipitation phase under different climate scenarios over the NWC. The results indicated that most of CMIP6 models overestimate snowfall while underestimate the Snowfall to Precipitation Ratio (SPR) over the NWC. The performance of CMIP6 models is poorer in high-altitude mountainous region but relatively more robust in low-altitude areas. Superior performance of five models are selected for analysing changes of snowfall and precipitation phase in the future. Under the SSP1-2.6 scenario, the trend of changes in snowfall is not obviously in the future. Conversely, snowfall is projected to decline at rates of −1.04 mm·10a-1 and −2.94 mm·10a-1 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, with significant declines in high-altitude zones. The SPR will decrease at rates of −0.19 %·10a-1, −0.64 %·10a-1, and −1.50 %·10a-1 under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenario, respectively. The magnitudes in changes of snowfall and SPR show significant differences across sub-regions in the future, with low-altitude areas exhibiting upward trends in snowfall and SPR under certain conditions. Winter snowfall in the future shows an increased trend, while declines are observed in spring and autumn snowfall overall, with notable disparities between scenarios. The correlation between snowfall biases and biases of temperature and precipitation indicate that temperature is a pivotal factor contributing to precipitation phase bias. The findings are helpful for enhancing the simulation precision of hydrological processes and providing scientific strategies for future water resource management over the NWC.

Washing machine filters to mitigate microplastics release: Citizen science study to estimate microfibers capture potential and assess their social acceptability

The accumulation of microplastics in the environment, particularly due to the significant contribution of synthetic clothing washing water, leads to a need for developing source-based retention solutions. The project's objective is to assess the capability of washing machine filters to capture microfibers from domestic wastewater under real laundry conditions. This involves developing a protocol for quantifying microfibers tailored to washing machine lint samples, estimating the potential microfiber capture by washing machine filters, and evaluating the social acceptability of using such filters in households. Volunteers were recruited and they used their washing machines normally, collecting lint in the filter over a 6-month period and documenting information for each load. Various reagents were tested using plastic controls and lint samples to select the most efficient one for removing organic matter without affecting microplastics. Microscopic visual alterations, chemical signatures via FTIR, and the efficacy of the digestion protocol were assessed through mass balance and visual counting. After discarding digestion protocols affecting plastic integrity, those ensuring the most efficient mass loss of organic matter were identified as H2O2 30 % for 5 days at room temperature (RT), NaOCl 3 % for 24 h at RT, and H2O2 30 % at 40 °C for 24 h. Polyester, acrylic, and polyamide controls were tested with NaOCl 3 % for 24 h and H2O2 30 % for 5 days, showing no changes in SEM images. The FTIR successfully recognized the chemical signature. More significant alterations on positive samples (cotton and cellulose) were observed after NaOCl exposure. The mean mass of microfibers remaining after digestion was estimated at 4.62 mg per liter of washing water or 61 mg/kg of washed garment. The survey revealed that 67.8 % of volunteer participants found the filter installation challenging, and 21.4 % had to hire a plumber, highlighting potential challenges associated with implementing washing machine filters on a large scale.

Mapping tidal restrictions to support blue carbon restoration

Blue carbon ecosystems (BCEs), encompassing mangroves, saltmarshes, and seagrasses, are vital ecosystems that deliver valuable services such as carbon sequestration, biodiversity support, and coastal protection. However, these ecosystems are threatened by various anthropogenic factors, including tidal restrictions like levees, barriers, and embankments. These structures alter the natural seawater flow, often converting coastal ecosystems into freshwater environments. Identifying tidal restrictions and assessing their suitability for tidal restoration in areas amenable for coastal management is a crucial first step to successfully restore BCEs and the associated ecosystem services they provide, i.e., managed realignment. This study presents a novel approach for detecting tidal restrictions in the state of Victoria, Australia, using high-resolution LiDAR data, geospatial analysis techniques, and a multi-criteria scoring system. Our model successfully identified 90 % of known tidal restrictions from an existing dataset, while also detecting an additional 118 potential tidal restrictions, representing a 35 % increase. The model performance analysis revealed trade-offs between precision, recall, and noise ratio when using different noise reduction thresholds, highlighting the importance of selecting an appropriate threshold based on project objectives. The multi-criteria scoring system, which considered factors such as proximity to BCEs and current land use, enabled the selection of tidal restrictions based on their hydrological suitability for restoration. The results of this study have significant implications for BCE restoration efforts not only in Victoria, but more broadly across Australia and globally, providing a systematic approach to identifying and targeting areas with the greatest potential for successful restoration projects. While the approach is low-cost and user-friendly, it is dependent on the availability of LiDAR data for the study area. This can make it accessible to researchers and practitioners worldwide, allowing for its adaptation and application in diverse regions to support global efforts in restoring BCEs through tidal restoration.

Projected Increase in Heatwaves under 1.5 and 2.0 °C Warming Levels Will Increase the Socio-Economic Exposure across China by the Late 21st Century

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

Investigation of The Use of Nano Powder On Roller Compressible Fibrous Concrete Roads

Despite the fact that our country's road network is inferior to that of industrialized countries, the volume of motor traffic is increasing. Thus, optimal solutions are imperative during both project and construction phases of highway investments. Concrete superstructures offer an alternative to flexible superstructures in various regions globally, including airport runways, warehouses, urban roads, streets, highways and industrial buildings. The objective of this study was to enhance the compressive and flexural strength of concrete and provide resistance against abrasion. To achieve this, a roller compressible concrete (RCC) mixture design was developed by incorporating basalt fiber, nano TiO2, and nano Fe2O3 additives. The experimental results were subjected to statistical analysis via the technique of analysis of variance (ANOVA). The findings of the study indicated that the strength performance of the optimal RCC mixture design reinforced with nano powder and fibers was superior to that of conventional concrete. Upon examination on a material basis, it was determined that the compressive strength exhibited an increase when the water/binder ratio was utilised within the range of 0.38–0.4. As the utilisation of nano-Fe2O3 and basalt fiber materials increased, their compressive strength exhibited a corresponding enhancement. The incorporation of basalt fiber into the composite material resulted in an increase in flexural strength at all ratios where it was utilised. It can be concluded that basalt fiber has the greatest potential for enhancing flexural strength. The optimal water-to-binder ratio for flexural properties is between 0.34 and 0.38. While nano powders can have a positive impact, they cannot achieve the maximum level of strength. For improving abrasion resistance, it was established that reducing the water/cement ratio, using fly ash, basalt fiber, and nano Fe2O3 in the mixture decreased abrasion. However, the usage of nano TiO2 did not have a significant positive effect. The material with the least effect on compressive, flexural and abrasion resistance was identified as nano TiO2. When materials other than nano TiO2 are utilised in accordance with the established optimal mixture calculation, the durability of the resulting RCC road will reach the requisite standard.

Developing the hybrid BIM-BEM and jellyfish search optimization system for optimizing energy consumption and building installation costs

In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operational phases. The combination of BIM and BEM offers advantages for the various phases of a construction project. However, there are currently very few studies that can integrate multi-objective optimization algorithms into the BIM-BEM process to achieve automatic optimization and effectively manage many aspects of building development. In this study, an EnergyPlus integrated multi-objective jellyfish search (EP-MOJSO) system was developed, utilizing an optimization algorithm to find the best thermal insulation layers for an Aluminum composite material (ACM) wall. The goal is to reduce the energy consumption and total cost in a BIM-BEM environment. In the case study, the authors successfully applied the system to a real building, resulting in a 10.7% reduction in total cost and a 65 kWh/m2/year reduction in EUI. It is expected that the results of the study will open up new ways of using algorithms for multi-criteria optimization in BIM models to optimize various project factors such as energy and total cost and thus make an important contribution to sustainable building design.

Pengembangan Model Project Based Learning (PjBL) Berbantuan Augmented Reality (AR) untuk Meningkatkan Keterampilan Proses Sains dan Sikap Ilmiah Peserta Didik Kelas XI pada Materi Struktur Sel

Developments in the field of education in the industrial era 4.0 demanded a change in students' perspectives on the quality of learning such as learning in biology subjects. Biology learning can foster students' thinking skills to solve everyday problems through observation and observations that are based on a scientific attitude and can develop scientific process skills. To achieve this, it is necessary to choose the right model. Student-centered models such as project based learning are very suitable for today's learning supported by adequate technology and increase student learning interest such as augmented reality. This study aims to see an increase in science process skills and scientific attitudes of students from the development of augmented reality-assisted project based learning models. The research method with the ADDIE development model and the type of research and development research. The research instrument consisted of interviews, observations and tests, the population of all students in class XI IPA and samples XI IPA 1, XI IPA 3. The results showed that for KPS the average pretest score for the experimental class was 49.9 and the average posttest score was 80.5 while in the control class the average pretest score was 48.7 and the posttest average score was 60 and for scientific attitude the average experimental pretest score was 60, the posttest was 86.9 while the control class averaged the pretest score was 58.9 posttest was 81. Followed by analysis t test and obtained a sig(2-tailed) value of 0.0000.05, so it was concluded that the development of the AR-assisted PjBL model can improve KPS and scientific attitudes of students. The recommendation from this research is that this project-based learning model manual is used to improve problem-solving skills in different materials.