Annual Usage Research Articles

Trends and Determinants of Palliative Care Utilization Among Patients With Metastatic Upper Tract Urothelial Carcinoma in the National Cancer Database

Objective To identify patterns of palliative care usage and identify determinants of palliative intervention using a large contemporary cohort of patients with metastatic upper tract urothelial carcinoma (mUTUC). Methods: The National Cancer Database was queried from 2004 to 2020 for patients with mUTUC. Patients with a prior malignancy, non-mUTUC, and missing follow up or palliative care information were excluded. Demographics and baseline characteristics were compared between patients with mUTUC who received palliative care and those that did not. Trends in annual palliative care usage were assessed via logistic regression. Univariate and multivariate logistic regression models were used to identify predictors of receipt of palliative care. Results: Four thousand and forty-four patients with mUTUC were included in the final cohort, among which 908 received palliative care (22.5%) and 3136 did not (77.5%). We found that the utilization of palliative care increased significantly from 2004 (15.0%) to 2019 (23.1%), P < .001. Additionally, on multivariate analysis we found that a recent year of diagnosis, receipt of a prior nonsurgical treatment paradigm, and an overall survival <6 months were independent predictors of palliative intervention, all P < .001. On the other hand, undergoing treatment at a minority serving hospital and older age were associated with lower likelihood of receiving palliative care, both P < .001. Conclusions: There is a low but increasing trend of utilization of palliative care among patients with mUTUC. Expansion of palliative care services, particularly among older patients and those at minority-serving hospitals, remains a key opportunity to improve quality of life and enhance patient-centered care among those with mUTUC.

Promoting the Economic Sustainability of Small-Scale Farmers Through Versatile Machinery in the Republic of Korea

The increasing use of tractors and implements is replacing manual labor, but adds financial burdens on small-scale farmers due to rising costs. Many farmers have turned to leasing and renting machinery to mitigate these expenses, while repair and maintenance costs remain significant. Government interventions aim to alleviate these burdens, but income disparities between urban and rural areas persist, and the impact of machinery use on climate change and the environment poses further challenges. Strategies like omitting some operation steps and adopting versatile machinery are proposed to cut costs and promote economic sustainability for small-scale farmers. Therefore, this study assessed the economic benefits of using versatile machinery in farming, especially for small-scale rural farmers. Farming processes were divided into field preparation and crop season activities. Field preparation included rotary tillage, ridge formation, and mulching, whereas crop season activities included harvesting and transportation. Annual usage and production cost analyses per hectare, including labor, fuel, and interest, alongside purchasing cost surveys, were conducted. Versatile machinery reduced annual usage costs for field preparation and crop season activities by 63.54% and 71.71%, respectively. This effect was more pronounced for farms under 2 ha, especially those employing manual harvest and transportation. Small-scale farmers, such as those cultivating hot pepper farms, are strongly encouraged to adopt versatile machinery to mitigate expenses and labor costs. The significance of adopting studied methodology will be amplified with the rising cost of labor. Consequently, utilization of versatile machinery in field farming for small-scale farms is projected to increase incomes not through enhanced production, but by significantly reducing the annual usage costs associated with agricultural machinery. This approach not only alleviates financial burdens but also enhances the sustainability of farm management, ensuring long-term viability and environmental stewardship.

Performance of a hybrid thermosyphon cooling system using airside economizers for data center free cooling under different climate conditions

Thermosyphon cooling system is an efficient data center cooling system for energy saving, but its commonly used free cooling technology (water-side economizer) has multiple heat transfer processes, which limit the utilization rate of natural cold sources. In this study, a hybrid thermosyphon cooling system (HTCS) that uses air economizers to assist the water-cooled row-level thermosyphons (terminal equipment part) for free cooling is proposed and investigated in a case data center. The previous cooling system (PCS) designed for this case data center, which used the same terminal equipment part and water-side economizers, is employed for comparison. To analyze the application potential of HTCS under different climate conditions, the energy models of HTCS and PCS are built for comparative studies on energy efficiency, environmental and economic potential in five typical climate cities. The results show that the HTCS has 575–4589 h longer free cooling periods than that of PCS in five typical climate cities, which leads to the annual energy consumption and Power Usage Efficiency of HTCS in most typical climate cities except Kunming are 13.7–24.7 % and 0.014–0.041 lower than those of PCS, respectively. Meanwhile, HTCS's annual CO2 emissions and life cycle costs are 344.2–1248.4 tons and 9.5–25.4 % lower than those of PCS in five typical climate cities except Kunming, which illustrates HTCS has good energy-saving and economic performance for data centers.

의사결정나무 분석에 대한 국내 연구 동향 탐색

This study explores the basic system and applicability of decision tree techniques by examining domestic research trends in this field. We analyzed 1,075 academic papers using domestic decision tree analysis from 2001 to 2023, categorized by year, journal, topic field, author, paper frequency, and frequency of algorithms used. Key findings reveal that the frequency of papers began with one in 2001, increasing to 87 in 2023. The distribution across fields was as follows: social science (377 papers, 35.07%), natural science (233 papers, 21.67%), and engineering (230 papers, 21.40%). Among the algorithms, CHAID was used most frequently (315 times, 36.42%), followed by CART (310 times, 35.84%), ensemble techniques like random forest (124 times, 14.34%), and C5.0 (94 times, 10.87%). The Quest algorithm was rarely used (10 times, 1.16%). The annual usage trends indicate a growing preference for ensemble techniques to enhance decision tree prediction rates. The paper's keywords included “decision trees” (687), “data mining” (239), “machine learning” (105), “logistic” (98), and “neural networks” (68). Notably, keywords related to health, such as “suicide,” “depression,” “high blood pressure,” and “health,” emerged prominently, signifying the extensive use of decision trees in medical research. Based on these findings, we propose implications and directions for follow-up studies.

Digital Flexible Ureteroscope: Evaluating Factors Responsible for Damage and Implementing a Mandatory Certification Program for Usage.

Objective: Digital flexible ureteroscopes (DFUs) play a crucial role in endourological procedures, and scope breakages are often avoidable with proper measures in place. We aimed to evaluate the effect of mandatory training on DFU durability and to assess the influence of various factors on instrument damage. Materials and Methods: Mandatory training involving instructional videos on DFU care was introduced for all processing and operating room staff handling DFUs. Only certified personnel were allowed to handle DFUs, with meticulous usage documentation. The average annual usage of each DFU was compared pre and post program implementation. We examined factors such as patient demographics, procedure details, and operator experience impacting scope life using negative binomial regression. Results: The average usage of DFU increased by 21%, from 6.38 to 7.74 cases. We found that post-graduate year stood out as a significant predictive factor (estimate = 3.28, p = 0.04). Moreover, the streamlined model revealed that previous ureteral-stent use (estimate = 0.94, p < 0.001), struvite stones (estimate = 3.08, p = 0.01), and a higher number of stones (estimate = 0.11, p = 0.04) were associated with an increased number of procedures before DFU breakage, whreas in situ lithotripsy in the lower calyx was associated with a reduced number of procedures before DFU breakage (estimate = -1.11, p = 0.003). Conclusion: Implementing a mandatory training program showed an increase in DFU durability by 21%, suggesting a potential reduction in annual repair and replacement costs by the same percentage. Furthermore, outcomes were more favorable when experienced practitioners conducted treatments, especially in cases involving struvite stones.

Drone Insights: Unveiling Beach Usage through AI-Powered People Counting

Ocean beaches are a major recreational attraction in many coastal cities, requiring accurate visitor counts for infrastructure planning and value estimation. We developed a novel method to assess beach usage on the Gold Coast, Australia, using 507 drone surveys across 24 beaches. The surveys covered 30 km of coastline, accounting for different seasons, times of day, and environmental conditions. Two AI models were employed: one for counting people on land and in water (91–95% accuracy), and another for identifying usage types (85–92% accuracy). Using drone data, we estimated annual beach usage at 34 million people in 2022/23, with 55% on land and 45% in water—approximately double the most recent estimate from lifeguard counts, which are spatially limited and prone to human error. When applying similar restrictions as lifeguard surveys, drone data estimated 15 million visits, aligning closely with lifeguard counts (within 9%). Temporal (time of day, day of the week, season) and spatial (beach location) factors were the strongest predictors of beach usage, with additional patterns explained by weather variables. Our method, combining drones with AI, enhances the coverage, accuracy, and granularity of beach monitoring, offering a scalable, cost-effective solution for long-term usage assessment.

Institutional Conversion to Energy-Efficient Ultra-Low Freezers Decreases Carbon Footprint and Reduces Energy Costs.

Introduction: The storage of biospecimens is a substantial source of greenhouse gas emissions and institutional energy costs. Energy-intensive ultra-low temperature (ULT) freezers used for biospecimen storage are a significant source of carbon emissions. ENERGY STAR-certified ULT freezers have the potential to decrease the carbon footprint. Objective: Quantify the impact of an institutional-scale freezer conversion program on carbon emissions and energy costs. Methods: A ULT freezer energy use prediction model was developed to identify and replace the most inefficient freezers in the research building for this pilot, and eventually institution-wide. Multiple linear regression factors included the number of years of use, storage volume, and ENERGY STAR certification status. Electrical usage and carbon emissions were quantified before and after replacement with ENERGY STAR models. Logistical methods were developed to decrease the risks of exposure of frozen samples to ambient temperature during content transfers. Institution-wide energy costs were derived by converting electrical burden to electrical costs. Carbon footprint assessment from ULT freezer operation was computed using the U.S. EPA Greenhouse Gas Equivalencies Calculator. Results: The pilot project revealed an annual reduction of 310,493 kilowatt hours of electrical usage, equivalent to 134 metric tons of carbon emissions. Annual electrical costs were reduced by $55,889 resulting in an 8-year payback on the initial investment. Using the pilot results, we modeled the benefit of the freezer exchange across the entire institution. The modeling predicted that conversion of the institution's remaining 1119 conventional ULT freezers to ENERGY STAR models would lower annual electrical usage by 7,911,549 kilowatt hours (3423 metric tons of carbon emissions), resulting in savings of over $1.4 million annually. Conclusion: Our methods make a large-scale initiative to replace energy-inefficient ULT freezers logistically possible, reduce carbon footprint, and demonstrate an attractive return on investment while proactively protecting valuable research materials.

The Slow Pace of Green Transformation: Underlying Factors and Implications

Concerns about climate change are a hot topic in the current debate about a sustainable future, and despite more than 30 years of international conferences, including the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Climate Change Conference (COP), the annual usage amount of fossil fuel-based energy sources has remained largely unchanged, and the green transition to a carbon-free energy system is progressing at a much slower pace than anticipated. This paper presents an original approach that consists in addressing the green transition’s dilemmas by analyzing the complex interplay of strongly interwoven forces hindering the rapid adoption of so-called green energy sources scrutinized from a three-fold perspective: socio-psychological; political–strategic and territorial; and technological. Moreover, these forces are ranked according to the magnitude of their impact on the anticipated transition to green, and it is estimated by logistic fit extrapolation that the total share of the contribution of low-carbon sources might reach a maximum of about 25% among all energy sources in 2050. A final original picture is presented, summarizing how all the involved forces are acting upon the expected transition as well as their consequences.

Real-world treatment patterns in patients with systemic lupus erythematosus: associations with comorbidities and damage

ObjectiveTo assess treatment patterns and the association between long-term glucocorticoid (GC) and hydroxychloroquine (HCQ) use and damage accrual in patients with systemic lupus erythematosus (SLE).MethodsA retrospective study including patients with...

Environmental Implications of Fly Ash Management and Utilization: A Review of Laws, Policies, and Practices.

In the environmental realm, ‘fly-ash’ refers to the byproduct of burning pulverized coal in thermal power stations. One of India's most resource-intensive and polluting industries, coal-fired power generation, contributes considerably to atmospheric pollution. Public power stations that use coal or lignite to generate energy account for 77% of the nation's total electricity production. This sector is responsible for over 65% of India's total coal use. This study employs a multifaceted approach combining theological, exploratory, analytical, and descriptive methods. It relies on secondary sources from diverse web resources to analyze fly ash's ecological and health impacts, explore utilization methods, and discuss potential legislation to prevent careless disposal. The coal-based power industry has added 32 GW, or over 64% of the new massive capacity, to its operations in the last decade, leading to a rise in coal consumption. There was a greater than 71% increase in annual coal usage, from 367 million tonnes in 2009–10 to almost 629 million tonnes in 2018–19. The increasing usage of coal in India has led to fly ash being one of the country's most significant industrial solid wastes. Fly ash is a residue or byproduct made when coal is burned; it is harmful to both humans and the environment. The problem is particularly acute in India because of the poor calorific value, high ash content, and low grade of the coal used there.

Innovative silica aerogel fiberglass walls and roofs: A path to lower carbon emissions and higher energy savings

ABSTRACT Buildings account for a significant portion of global energy use and consumption. Buildings have substantial energy consumption due to the use of heating, ventilation, and cooling systems. It is evident that the insulation materials used in the design of building envelopes can efficiently reduce the cost of air conditioning by reducing external heat gains and losses. Additionally, the use of environmentally friendly and cost-effective can be beneficial from an environmental standpoint. This paper aims to evaluate the potential for cost savings in the air conditioning of buildings incorporated with silica aerogel fiberglass insulation in hollow concrete bricks (wall) and hollow roof tile (roof). Silica aerogel fiberglass insulation is extensively used for heat insulation purposes, including building insulations, due to its superior thermal insulation and flame retardant properties. The thermo-physical properties of silica fiberglass insulation and building materials were determined experimentally as per ASTM D 5334 standards. Twenty distinct building design concepts are investigated in this study, each featuring insulation-integrated concrete blocks composed of silica aerogel fiberglass: (WEM1) insulation layer in the outer side, (WEM2) insulation layer in the center, (WEM3) insulation layer in the inner side, (WEM4) insulation layers in the outer-middle, (WEM5) insulation layers in the middle-inner, and (WEM6) insulation layers in the outer-inner sides. Additionally, two arrangements of insulation-stuffed roof tiles, REM1 above the RCC and REM2 below the RCC. In addition, novel building designs integrated with insulation were analyzed numerically, related to environmental conditions that refer to two different scenarios in India (hot dry and composite climates). Thermo-economic analysis of novel wall and roof designs with silica fiberglass insulation materials on air-conditioning costs, carbon dioxide emissions, and payback time are compared with conventional roof and wall patterns. The thermo-economic analysis was carried out based on the number of degree-hours of heating and cooling to determine the building’s annual energy usage. The building model arrangement (WEM6-REM1), which consists of an insulation composite wall (WEM6) and insulation roof tile (REM1), demonstrated the greatest annual air conditioning cost savings of 3.4 $/m2/year and 1.74 $/m2/year when compared to conventional building design for climatic conditions of Kurnool and Gauhati. The building model arrangement (WEM6-REM1) composed of silica fiberglass aerogel insulation exhibits the highest reduction in carbon emissions of 65 kg/kWh and 33 kg/kWh, when compared to conventional building design analyzed in both hot-arid; (Kurnool) and composite (Gauhati) climatic conditions. The placement of insulation in the roof alone (REM1) has the shortest payback duration of 2.15 years. The results of this study point to a viable approach to enhancing building design decision-making and narrowing the performance gap in sustainable and energy-efficient strategies.

Environmentally sustainable opportunities for health systems: Metered-dose inhaler prescribing, dispensing, use and waste at a tertiary academic centre.

The Canadian health sector's carbon footprint is among the highest in the world and is responsible for 4.6% of Canada's total greenhouse gas emissions, a quarter of which is linked to pharmaceuticals, with metered-dose inhalers (MDIs) contributing disproportionally high amounts. To describe MDI prescribing, dispensing, use and waste patterns at a Canadian tertiary care academic hospital. In a retrospective point-prevalence cohort study, 100 consecutive patients discharged from medical and surgical services who were prescribed at least 1 MDI during their admission were included. Data were collected to describe patient demographics, MDI prescribing, dispensing, use and waste patterns. Use and waste data were applied to annual purchasing data to estimate annual usage and waste. Financial cost was computed using local purchasing estimates and carbon cost was calculated using published estimates. In 100 consecutively discharged patients, 315 MDIs were dispensed in total, of which 96 were unused. This represents 61,440 actuations dispensed, with 56,773 (92%) of doses unused or wasted. Waste data were applied to annual estimates, with a calculated annual carbon footprint of 315.8 tons of carbon dioxide equivalent (tCO2e). We estimate that a 20% waste reduction would result in carbon savings of 68.5 tCO2e. If 20% of salbutamol prescriptions were switched to the dry powder inhaler alternative, terbutaline, a 14% reduction in waste would be required to offset the additional monetary cost. This study suggests that 92% of MDI doses are unused and wasted. Opportunities for waste reduction exist and would be associated with both financial and carbon savings.

Assessment of Well Location for Agricultural Water Use in Coastal Areas Using OpenGeoSys

The use of groundwater for agriculture and fisheries in South Korea accounts for 53% of the total annual usage and is on the rise. However, the amount of agricultural groundwater available near coastal boundaries is affected by seawater intrusion. Therefore, this study uses numerical simulations to estimate the available amount of agricultural groundwater near coastal boundaries, considering the location and depth of well installations. Increasing the horizontal distance of the well installation from the coastal boundary was demonstrated to reduce salinity intrusion concentration by approximately 20% more than adjusting the depth of well installation. This research is expected to serve as supplementary material that can be utilized when installing agricultural wells in coastal areas in the future.

Characterization and Energy Performance of WO3 Doped and Undoped Photochromic Films

When exposed to light sources, photochromic (PC) materials change their optical properties and can lessen the transmission of UV and infrared radiation. This results in optimal thermal comfort and a pleasing visual contrast between the internal and external settings. This study uses computer modeling to analyze the annual energy usage in a home with natural ventilation in order to compare the effectiveness of photochromic films with commercial glass. The study is carried out using the EnergyPlus program in the cities of São Carlos and Cuiabá ‐ Brazil. Experiments and numerical simulations with data from doped and undoped tungsten trioxide (WO3) PC films are used in the study. Given the rise in energy usage and the pursuit of thermal comfort, this method is essential for assessing the thermal performance of buildings. Evaluations included a comparison of air conditioner performance and energy savings analysis, which leads to a noteworthy annual reduction in energy usage of up to 216.55 kWh and a 40% improvement in visual comfort. It is determined that PC film's dynamic behavior is the best option for comfort in terms of heat, illumination, and visual comfort.

Optimizing Window Configurations for Energy-Efficient Buildings with Aluminum Alloy Frames and Helium-Filled Insulating Glazing

This research investigates building energy consumption in the Fujian region of China, characterized by warm winters and hot summers. The study focuses on window configurations and their impact on heat exchange and solar gain management. Initially examining three aluminum alloy window frames, the study utilizes the Multi-Quality Metric Calculator (MQMC) software V1 to assess the benefits of filled insulating glass. The reference values for the heat transfer coefficient, visible transmittance, and sun shading coefficient are established. Subsequently, Ecotect software V5.6 is employed to conduct a comprehensive year-round energy consumption simulation analysis, identifying an optimal window layout tailored to Fujian’s climate. In the Fuzhou simulation, aluminum–plastic co-extruded windows exhibit the lowest cooling energy consumption, while aluminum alloy windows have the highest. Summer cooling energy consumption, comprising about 75% of the total annual energy usage in hot summer and warm winter regions, significantly influences overall energy consumption. Windows made of aluminum–plastic co-extruded material with superior thermal insulation qualities can greatly reduce building energy consumption. The results contribute valuable insights to sustainable building practices and energy-conscious designs in regions characterized by warm winters and hot summers.

Implementation of the ABC Analysis to the Inventory Management

In an ever-changing market, inventory management is becoming a major problem that requires thorough planning, supervision, and control. If companies are well managed and have good planning and control, they can last a long time. Poor inventory management is very detrimental to the business as it can cause the company to fail to meet customer or consumer demands, which can lead customers to choose other companies that can meet their demands. Plan your stockpiles to maintain balance, avoid excess or deficiency, and control storage costs. ABC analysis, one of the most common inventory management techniques, divides inventory into three categories (A, B, and C) based on total annual product usage or total inventory cost based on the Pareto principle. The goal of this study is to use ABC analysis in the company's inventory management to identify products in categories A, B, and C based on profitability and sales factors. The research phases include data collection, data preprocessing, exploratory data analysis, ABC analysis, key product analysis, and explanatory analysis. According to the research findings, Segment A accounts for 18% of the total product and generates 78% of the overall profit. Segment B contributes 23% of the total number of items and 16% of all revenue.

Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices

The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and characterize, MP pollution and its allied threats to human health in the indoor urban environment, where 80 household dust samples were collected from the whole study area. The presence of MPs in household dust of the urban indoor environment was 25.8 ± 6.43 particles/g with a significant variety, whereas the fiber shape (73%), 0.5–1.00 mm ranged MPs size (58%), blue color (21%), and polystyrene polymer (34%) was the most ubiquitous MPs category. The pollution load index (1.61–2.96) indicated significant pollution due to the high abundance of MPs. Besides, other risks evaluating indices including contamination factor (1.00–3.51), and Nemerow pollution index (1.60–3.51) represent moderate to high MP-induced pollution. The polymer hazard index (119.54 ± 70.34) indicated significant risks for the selected polymers to the indoor environment living inhabitants. Machine learning approaches, especially random forest and support random vector machine were effective in predicting the number of MPs, where EC, salinity, pH, OC, and texture classes acted as controlling factors. Children and adults might be ingesting 4.12 ± 1.01 and 2.27 ± 0.57 particles/day through the ingestion exposure route, which has significant health effects. Polymer-associated lifetime cancer risk assessment results show that there are moderate risks for both adults and children, but children tend to be more susceptible to MP risks. The overall study found that Dhaka was the most severely MPs induced risky division among the others. This study reveals that high quantities of MPs in indoor environments could pose a serious health hazard' to different exposure groups.

Advancing building energy efficiency: A deep learning approach to early-stage prediction of residential electric consumption

Enhancing building energy efficiency underscores the critical need for innovative predictive models to mitigate environmental issues from high energy consumption, especially in residential areas with air-conditioning and heating ventilation systems. This study introduces the use of Long Short-Term Memory (LSTM) networks for early prediction of residential electric consumption, representing a significant innovation in the field. Unlike traditional Deep Neural Network (DNN) and Artificial Neural Network (ANN) models, Long Short-Term Memory networks efficiently process time-series data, predicting future energy usage with unmatched accuracy. The Long Short-Term Memory model exhibited superior training efficiency, requiring only 2.69 s for over 500 test cases, outperforming Deep Neural Network and Artificial Neural Network models, which took 5.26 and 3.88 s, respectively. Its predictive accuracy, evidenced by an R-squared value of 0.97, surpasses the 0.95 and 0.92 of Deep Neural Network and Artificial Neural Network models, respectively. This breakthrough enables accurate predictions of annual energy usage before construction starts and aids in identifying energy efficiency improvements early in the design process. Applying Long Short-Term Memory networks in this context marks a substantial advancement in predictive modeling for building energy consumption, equipping architects and engineers with a vital tool for designing energy-efficient buildings from the beginning. The innovation and quantitatively proven effectiveness of the Long Short-Term Memory model highlight its potential to revolutionize early-stage building design strategies, filling a crucial gap in the existing literature.

Contemporary roof pattern for energy efficient buildings: Air conditioning cost alleviation, CO2 emission mitigation potential and acceptable payback period

Buildings account for a significant portion of global energy use and consumption. Buildings have substantial energy consumption due to the use of heating, ventilation, and cooling systems. It is evident that the use of insulation materials and phase change materials (PCMs) in the design of buildings can effectively reduce the cost of air conditioning by reducing external heat gains and losses. Additionally, the use of environmentally friendly, cost-effective, and natural materials can be beneficial from an environmental standpoint. This paper aims to evaluate the potential for cost savings in the air conditioning of various PCMs and insulation-stuffed building roof configurations. In that respect, four types of phase change materials (HS29, HS36, FS42, and FS30) and four insulation materials (Therma cork, sawdust, aerogel, and sheep wool) were selected. The thermophysical properties of PCMs (solid and liquid state), insulation materials, and building elements were determined experimentally as per ASTM D 5334 standards. Ten various building roof models (contemporary roof pattern) are studied with four various PCMs and four various insulations. In addition, novel building roof design integrated with PCM/insulation were analysed numerically, related to environmental conditions that refer to two different scenarios in India (hot dry and composite climates). It further analyses the reduction in greenhouse gas emissions associated with different roof configurations. The aforementioned PCM/insulation materials were stuffed in the void spaces of the contemporary roof pattern. Thermo-economic analysis of novel roof design with PCM/insulation materials on air-conditioning costs, carbon dioxide emissions and payback time are compared with conventional roofs (CR) and contemporary roof patterns (CRP). The thermo-economic analysis was carried out based on the number of degree-hours of heating and cooling to determine the building's annual energy usage. HS29 PCM stuffed roof pattern (HS29RP) saved the highest total energy cost savings of 11.89/14.71 $/m2/year when compared with conventional roof and 11.35/13.89 $/m2/year compared with contemporary roof pattern for Jabalpur/Kota climatic conditions. HS29 PCM stuffed roof pattern (HS29RP) exhibits the maximum carbon emission mitigation of 215.32/275.37 kg/kWh when compared with conventional roof and 204.95/259.57 kg/kWh for climatic conditions of Jabalpur/Kota. While considering shortest payback span, sawdust stuffed roof pattern shows the minimum payback period of 0.69 and 2.15 years when compared with conventional roof and contemporary roof pattern. The findings of this research offer significant contributions to the development of energy-efficient building envelopes applicable to a wider range of buildings, including those with and without air conditioning systems.

Thermoelectric generator for energy production from renewable sources

Thermoelectric generators (TEG) produce electrical energy from a temperature difference between the cold and hot side of TEG modules (Seebeck effect); in principle, they can be used at temperature differences greater than 3 K [1]. Thermoelectric energy generation is primarily known for supplying self-sufficient sensors or in exhaust systems of motor vehicle internal combustion engines [2], where high temperature differences of up to 700 K are used. In this paper, basic investigations are carried out in order to expand the power range of thermoelectric energy generation and their potential for the energy transition. To this end, the area of application should be expanded to include medium and small temperature differences. The output characteristic of a TEG module has a power maximum, which increases quadratically with the temperature difference and linearly with the module area. At a temperature difference of 40 K, an output of 250 W/m2 can be generated. A solar module can generate 150 W/m2 in full sunlight; this output is only available for 12 % of the year given the 1000 full-load hours of sunshine that are usual in our latitudes. A continuous thermoelectric energy source could provide energy all year. Under optimal conditions, an annual usage time of 5000 hours and a service life of 10 years, electricity production costs of around 10 Cents/kWh can be expected [3]. This price is quite comparable to other renewable energy sources (photovoltaics, wind).