Potential For Improving Efficiency Research Articles

Knowledge, attitude, and practices regarding use of artificial intelligence for medical writings among doctors of Khyber Pakhtunkhwa, Pakistan: a cross-sectional study

Background and objectives: Artificial intelligence (AI) is a cutting-edge technology affecting all eras of science and technology and medicine, which is not an exception. Medical writings play a significant role in improving health standard and patient outcomes owing to their global dissemination. AI has widely affected medical writings. This study aims to determine the knowledge, attitude, and practices among the doctors of Khyber Pakhtunkhwa (KPK) regarding the use of artificial intelligence (AI) in medical writings. Medial writings cover a broad horizon of writings ranging from simple medical reports and prescription and extending to impactful article writings and evidence-based research papers as well as figure and graph generation for these papers using the input data. Materials and methods: It is a questionnaire-based cross-sectional study involving convenience non-probability sampling. The questionnaire contained a total of 29 items with subsequent portions of knowledge assessment containing 11 items, the attitude assessment comprised 10 items, and the practice assessment involved eight items. Being the first study of its kind, a conservational approach was adopted for sample size determination, and initially, the calculated sample was 385 using an online sample size calculator. The final sample size adopted was 500 to avoid any discrepancies owing to small sample size. The study setting was KPK, a province of Pakistan, and the study population was the medical doctors practicing in KPK. SPSS 28 was used for data analysis. Frequency, percentage, correlation, and regression analysis were used. Results: Out of 500 respondents, 135 were females and 365 were males. The overall data suggest a high level of knowledge regarding AI in medicine (91.2%). The use of AI was about 30.6%, highlighting a gap that needs to be addressed. The majority of doctors agreed on the impact of AI in medical writing (92.5%), indicating a positive attitude toward promotion within the community. The Pearson coefficient showed statistically significant values. Regression analysis with P-value < 0.001 also supported the hypothesis. Conclusion: The study revealed a positive perception of AI in medical writing, recognizing its potential for efficiency and quality improvement. However, respondents expressed concerns about AI authorship, ethics, privacy, and bias, leading to low adoption. The findings underscore the necessity for education, training, and clear guidelines to responsibly integrate AI into medical writing, ensuring ethical use and advancing the field with informed practices.

A study on the improvement of supercomputer energy efficiency based on green500 benchmarking data

With the rapid growth of AI-related industries, the need for reducing and optimizing energy consumption in large-scale computational resources, such as supercomputers, has become increasingly important. This study focuses on supercomputers listed in the Green 500, categorizing existing benchmarking evaluation variables into input and output factors. An energy efficiency objective function was introduced, and DEA was conducted using BCC and SEM models. The study analyzed the relative efficiency levels among supercomputers and identified factors and levels of potential efficiency improvements. The results provide insights into the performance factors of individual supercomputers and their potential for improvement. Furthermore, by comparing the energy efficiency evaluated by the Green 500 with the results of DEA, it demonstrated the potential for utilizing DEA as a new means for efficiency improvement. It also highlighted the necessity of a comprehensive evaluation that incorporates various performance factors, rather than a simple efficiency assessment based solely on energy consumption.

Analysis of the utilization of Seuseupan Weir water for irrigation in the Seuseupan Irrigation Area (Case study: Seuseupan Weir, Cirebon Regency)

The Seuseupan Dam is one of the most important water buildings in the Cirebon Regency. The purpose of this study is to analyze the need for irrigation water to obtain predictions of the maximum and minimum values of water needs, so that the availability of water can be used efficiently and effectively to increase agricultural productivity. The research contributes to understanding the effective utilization of water resources from the dam for irrigation purposes. The study provides valuable insights into water management practices, potential improvements in irrigation efficiency, and the overall impact on local agricultural productivity, as well as the financial implications of water management decisions for farmers and local economies. The findings may inform policymakers and stakeholders about the sustainability of water usage in the region, helping to optimize resource allocation and enhance food security.

Can artificial intelligence understand our emotions? Deep learning applications with face recognition

Abstract Objective The aim of this study is to evaluate the ability to detect emotions from human facial expressions via facial recognition technologies and analyze the effectiveness of deep learning models in this process. Method This research was conducted between 01.04 and 01.07.2024. The data of the study were taken from the open access site https://www.kaggle.com/datasets/msambare/fer2013 (Kaggle, 2024). Python 3.8 is used in this study. The FER-2013 (Facial Expression Recognition 2013) dataset is a comprehensive collection of facial images labeled with various emotions. The dataset contains 35,887 grayscale facial images. Each image has a size of 48 × 48 pixels. The dataset consists of images belonging to 7 emotion categories: anger, disgust, fear, happy, sad, confused, and neutral. Results In our experiments on the FER2013 dataset, we evaluated the performance of three different models: MobileNetV3-L, EfficientNetV2-L, and our proposed EfficientMobileNet. The evaluation criteria were based on sensitivity, specificity, accuracy, and F1 scores to assess the effectiveness of each model comprehensively. The EfficientMobileNet model outperformed MobileNetV3-L and EfficientNetV2-L in all measured performance metrics. EfficientMobileNet was the most successful model for predicting emotions, with an accuracy of 77.6%. Conclusion The impressive results obtained by EfficientMobileNet on the Fer2013 dataset show potential for wider application, especially in image classification tasks involving low-quality or small-scale images. This performance supports the idea of the potential for further improvements in neural network architecture and model efficiency and accuracy. Future work should focus on optimizing the model for more challenging datasets, studying the impact of different architectural adjustments, and investigating the scalability of EfficientMobileNet across various domains and applications.

LEAN TOURISM: AN EFFECTIVE WORKSHOP THROUGH VALUE STREAM MAPPING METHOD

This paper focuses on the application of the lean management model in the tourism sector, specifically in the hospitality sector. In line with this focus, a study was conducted in the reception department of a five-star hotel in Mugla/Turkiye. The study aims to explore the applicability of lean thinking in management, which has been used in the manufacturing sector for many years with thriving, sustainable results, and to address opportunities for improvement in the hotel business. The research methodology includes a comprehensive value stream mapping study of the operational processes in the reception department. The research, involving the hotel manager, reception unit manager, and employees, aimed to identify non-value-adding processes and eliminate them through quality circles. The value stream mapping process provided valuable insights into the customer journey from initial registration to room assignment and identified areas for improvement. Non-value-adding steps, such as unnecessary paperwork and delays in the process, were identified, resulting in potential improvements in operational efficiency and customer satisfaction. The results of the study underlined the feasibility of integrating lean principles into the hotel's reception department and highlighted the potential impact on process efficiency and customer satisfaction. The study also emphasized the importance of monitoring the implementation of the improvement recommendations and evaluating their impact. In addition, it emphasized the need to create a culture of continuous improvement in hotel businesses in the tourism sector. The study's constraints include its focus on a single hotel, the absence of lean transformation initiatives, and its limited scope to the reception department, serving as a pilot study. The findings underscore the importance of adopting lean management practices to optimize processes and elevate the hospitality industry's guest experience.

Spatially explicit assessment of water stress and potential mitigating solutions in a large water-limited basin: the Yellow River basin in China

Abstract. Comprehensive assessment of the long-term evolution of water stress and its driving factors is essential for designing effective water resource management strategies. However, the roles of water withdrawal and water availability components in determining water stress and potential mitigating measures in large water-scarce basins are poorly understood. Here, an integrated analytical framework was applied to the Yellow River basin (YRB), where the water crisis has been a core issue for sustainable development. The analysis suggests that the YRB has experienced unfavorable changes in critical water stress indicators over the past 56 years. Compared to the period from 1965 to 1980, the regional water stress index (WSI) and the frequency and duration of water scarcity increased by 76 %, 100 %, and 92 %, respectively, over the most recent 2 decades. Water withdrawal was the primary driver of the increased WSI before 2000; however, it has since contributed as much as water availability. Meanwhile, local water management and climate change adaptation were shown to be important in determining total water availability at the sub-basin scale. Water demand in the 2030s is predicted to be 6.5 % higher than during 2001–2020 (34.2 km3) based on the trajectory of historical irrigation water use and corrected socio-economic data under different Shared Socioeconomic Pathways (SSPs). To meet all sectoral water needs, a surface water deficit of 8.36 km3 is projected. Potential improvements in irrigation efficiency could address 25 % of this deficit, thereby alleviating the pressure on external water transfer projects. Such efficiency gains would enable the WSI of the YRB in the 2030s to be maintained at the current level (0.95), which would worsen conditions for 44.9 % of the total population while easing them for 10.7 % compared to in the 2000s. Our results have vital implications for water resource management in basins facing similar water crises to that in the YRB.

P1025 A mental health intervention can support increased capacity and revenue in the IBD outpatient setting

Abstract Background A national reference centre for inflammatory bowel disease (IBD) in the UK supports around 10000 people with inflammatory bowel disease. A mental health service development in our centre has provided access to medical treatments for IBD that have traditionally been used to treat mental health conditions. Fatigue, affecting an estimated 40% of patients with IBD, predicts increased use of outpatient services. In a psychiatrist-led clinic, modafinil, a medication used to treat narcolepsy, produced a mean 58% improvement in fatigue in a case series of 10 patients. This snapshot of patients treated in this clinic looks at the potential for cost and efficiency improvements to reduce the waiting list for consultant-led care. Methods The electronic health records of 10 individuals treated with modafinil (dose 50-400mg/day based on clinical response) were analysed. Contacts were compared for 12 months before and 12 months after the intervention. The mean average of any saved appointments was calculated. All patients had severe fatigue, defined as an IBD Fatigue Assessment Score >10/20, for six months or more. Anonymous demographic data were collected, and potential savings in appointments between follow-ups charged at £87.00 and new appointments charged at £150.00; the possible increase in capacity was calculated. Results Of the 10 patients analysed, the mean age was 33.8; 40% were male and 60% female. Twenty per cent had comorbid irritable bowel syndrome, 10% had comorbid Ehlers-Danlos syndrome, 60% were prescribed advanced therapy (AT) and 30% had been on one or more previous ATs. IBD outpatient appointments for the combined cohort were reduced from 70 to 27 for the 12 months before modafinil commencement to the 12 months of treatment, a reduction of 43(61%) appointments and a mean average of 4.3 If clinic templates were altered to new from follow-up, for these appointments’ revenue would be increased from £3,741.00 to £6,450.00, representing an increase in revenue of £2,709.00. If just 10% of the 4000 patients estimated to have fatigue at our centre benefit from this treatment, 1,720 appointments in 12 months would be saved, representing a cost saving of £149,640.00. These appointments could also be made available for new patients with a revenue increase of £108,360.00. With 1,720 appointments made available, this also offers increased access to specialist opinions for patients with IBD who need it Conclusion In addition to improving patient quality of life, modafinil shows potential health-economic benefits in IBD, including reduced burden on outpatient services and potential for increased revenue for specialist services. Replication of these findings in larger cohorts and randomised trials is needed.

Impact of dietary lysophospholipids supplementation on growth performance, meat quality, and lipid metabolism in finishing bulls fed diets varying in fatty acid saturation

BackgroundThe objective of this study was to evaluate the effects of dietary fatty acids (FA) saturation and lysophospholipids supplementation on growth, meat quality, oxidative stability, FA profiles, and lipid metabolism of finishing beef bulls. Thirty-two Angus bulls (initial body weight: 623 ± 22.6 kg; 21 ± 0.5 months of age) were used. The experiment was a completely randomized block design with a 2 × 2 factorial arrangement of treatments: 2 diets with FA of different degree of unsaturation [high saturated FA diet (HSFA) vs. high unsaturated FA diet (HUFA)] combined with (0.075%, dry matter basis) and without lysophospholipids supplementation. The bulls were fed a high-concentrate diet (forage to concentrate, 15:85) for 104 d including a 14-d adaptation period and a 90-d data and sample collection period.ResultsNo interactions were observed between dietary FA and lysophospholipids supplementation for growth and meat quality parameters. A greater dietary ratio of unsaturated FA (UFA) to saturated FA (SFA) from 1:2 to 1:1 led to lower DM intake and backfat thickness, but did not affect growth performance and other carcass traits. Compared with HSFA, bulls fed HUFA had greater shear force in Longissimus thoracis (LT) muscle, but had lower intramuscular fat (IMF) content and SOD content in LT muscle. Compared with HUFA, feeding the HSFA diet up-regulated expression of ACC, FAS, PPARγ, and SCD1, but down-regulated expression of CPT1B. Compared with feeding HSFA, the HUFA diet led to greater concentrations of c9-C18:1 and other monounsaturated FA in LT muscle. Feeding HUFA also led to lower plasma concentrations of cholesterol, but there were no interactions between FA and lysophospholipids detected. Feeding lysophospholipids improved growth and feed conversion ratio and altered meat quality by increasing muscle pH24h, redness values (24 h), IMF content, and concentrations of C18:3, C20:5 and total polyunsaturated fatty acids. Furthermore, lysophospholipids supplementation led to lower malondialdehyde content and up-regulated the expression of ACC, FAS, and LPL in LT muscle.ConclusionsResults indicated that supplementing a high-concentrate diet with lysophospholipids to beef bulls can enhance growth rate, feed efficiency, meat quality, and beneficial FA. Increasing the dietary ratio of UFA to SFA reduced DM intake and backfat thickness without compromising growth, suggesting potential improvements in feed efficiency.

First-in-human experience of a portable electrical drill with smart autostop for bedside external ventricular drain placement

BackgroundTimely external ventricular drain (EVD) placement may be a lifesaving neurosurgical procedure. The manual twist drill used for intracranial access represents an opportunity for potential improvement in efficiency, safety, and ease of use. A new generation of portable electrical drills with smart autostop mechanisms, such as the Hubly cranial drill (Hubly Surgical; Lisle, IL), aim to address these opportunities for improvement. ResultsTwo patients received EVDs using the portable electrical autostop drill (PEAD): A 54-year-old woman who suffered a postoperative hemorrhage and a 59-year-old woman who presented with early hydrocephalus secondary to hypertensive subarachnoid hemorrhage (SAH). Between both patients, a total of 9 and 2 access attempts were necessary to breach the inner table and visual dura. Access times in both cases, from skin incision to dural puncture, were less than 5 min. There were no apparent complications with the use of the PEAD in either case, and there was excellent placement of the EVD at the foramen of Monroe in both cases. ConclusionWe demonstrate the first successful use of a portable electrical drill with smart autostop in humans. The PEAD has potential to reduce procedure time and human error. Further development of the smart autostop drill may allow for more consistent and safer EVD placement.

Comparative analysis of different types of pulse width modulation techniques for multilevel inverters

Multilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation.

A comprehensive analysis of household air pollution due to traditional cooking in the himalayan belt

Abstract Household air pollution (HAP) and its health concerns are a significant challenge in the Indian subcontinent and other developing nations. Approximately 4.3 million deaths worldwide are attributed to indoor air pollution rather than outdoor air pollution, most of which occur in low- and middle-income countries. Traditional cookstoves, widely used in rural Himalayan areas of India, Nepal, and Pakistan, emit pollutants like particulate matter (PM), carbon monoxide (CO), and carbon dioxide (CO2). These stoves, which burn solid fuels such as wood, dung, charcoal, and agricultural waste, contribute to respiratory infections, cancer, premature mortality, and cardiovascular diseases. Improving biomass cookstoves can help reduce exposure to the harmful indoor air pollutants. This study evaluates household pollution levels in Uttarakhand, India, using direct data collection to determine the daily contaminant exposure. The findings show extremely high PM 2.5 and 10 emissions (2,100 μg/m³ and 5,000 μg/m³, respectively) due to inefficient cookstove design and incomplete fuel combustion. By comparing traditional and improved cookstoves using standardized testing protocols, the study also measures the efficiency and identifies the causes of high emissions. The results corroborated the hypothesis that traditional cookstoves have a very low thermal efficiency of ∼11 %, leading to the high emissions gathered from sensor data. This comparison provides specific insights into the reasons behind high emissions and suggests targeted improvements. The findings suggest potential improvements in cookstove efficiency, offering a foundation for further research and stakeholder involvement to enhance rural livelihoods in India.

AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost-Effectiveness, and Staff Satisfaction.

We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction. We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test. In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed. Of these, 92% of kidney stones were eligible for AI-assisted interpretation. Out of these AI-eligible stones, 45% were able to be auto-released by the AI-augmented test system without human secondary review. Furthermore, the new AI-augmented kidney stone test system resulted in an apparent 40% reduction in incorrect laboratory results. Additionally, the new AI-augmented test system improved laboratory efficiency by 20%, improved staff satisfaction, and reduced the average analysis cost per kidney stone by $0.23. The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.

Exploring determinants of VAT gaps using structural equation models: a MIMIC approach

This study explores the estimation of the VAT gap using the Multiple Indicators and Multiple Causes (MIMIC) model, a specific type of structural equation model. The VAT gap, a significant indicator of tax revenue loss and inefficiency in VAT collection, is treated as a latent variable driven by various causes and reflected through specific indicators. Drawing on data from European countries, the model incorporates variables such as economic openness, government spending, corruption perceptions, and the e-government development index, each serving as proxies for underlying VAT collection challenges. This study presents a first-time application of the MIMIC model to the Czech Republic, estimating the VAT gap of 2003 – 2020 and revealing fluctuations between 27 % and 35 % of total tax liability, with stabilization at 31 % since 2016. The findings underscore policy areas for potential improvements in VAT compliance and collection efficiency, particularly through enhanced digitalization efforts and governance quality in tax administration by highlighting key factors contributing to the VAT gap.

ERP Effectiveness in Improving Production Efficiency

This research specifically investigates how the implementation of ERP systems impacts the operational performance of manufacturing companies. By analyzing the literature and conducting case studies, this research uncovers potential efficiency improvements in various aspects, including reduced production time, optimized use of resources, and improved data quality. The results of this study are expected to provide valuable recommendations for manufacturing companies in implementing ERP effectively and achieving competitive advantage.

Numerical investigation and optimization of heterogeneous-homogeneous coupled condensation in steam turbines of tower solar power system

The significant potential for stage efficiency improvement is in steam turbines of solar thermal power plants, which operate in difficult atmospheric conditions contributing to the high pressure in the condenser and steam impurities. Presented study aims to examine the influence of three steam impurities on the condensing flow through turbine stage rotor. Firstly, the modified condensation model is validated using experimental data. Next, the flow characteristics and losses of pure steam and steam containing heterogeneous particles in three-dimensional turbine are investigated separately, and the effects of particles on the flow process and system performance are analyzed. Finally, the effect of backpressure on the condensation flow and the turbine performance is investigated. The results reveal that homogeneous condensation predominantly occurs at higher blade height, NaCl particles have the most significant impact on condensation. At a particle concentration of 10151/kg, the thermal efficiency of heterogeneous condensation on solid particles and tiny droplets increases by 1.6 % and 2.3 %, respectively, compared to homogeneous condensation. Conversely, NaCl particles exhibit a reduction of 0.2 %. Lastly, by strategically raising the backpressure, it is feasible to decrease the humidity on the final stage blades, enhancing thermal efficiency and ultimately optimizing tower solar power generation system operation.

Experimental Study of Turbulent Premixed Flames of Gas-to-Liquids (GTL) Fuel in a Fan-Stirred Combustion Bomb

This study experimentally investigates the turbulent flame speeds (St) of Gas-to-Liquids (GTL) fuel and a 50/50 blend with diesel across turbulence intensities (u') ranging from 0.5 to 3.0 m/s and equivalence ratios (Φ) from 0.7 to 1.3. Experiments were conducted using a cylindrical fan-stirred combustion bomb at an initial temperature (Ti) of 463 K and atmospheric pressure under near homogeneous and isotropic turbulence (HIT) conditions. A pressure transducer measured the St of the propagating GTL flame. High-speed imaging reveals that changes in flame brightness are associated with variations in flame temperature and soot incandescence. Compared to diesel, stoichiometric GTL and the 50/50 diesel-GTL blend showed peak combustion pressure reductions of 8.9% and 4.9%, respectively. Rich diesel fuel and lean GTL exhibited higher pressure rise rates, flame propagation, and St due to their Lewis numbers (Le) being less than one, enhancing flame-turbulence interaction. At u` of 0.5, 1.5, and 3.0 m/s, St for GTL increased by approximately 3.6%, 5.3%, and 2.8%, respectively, when compared to diesel, with these increases observed at Φ < 1.1. Transition from wrinkled to corrugated flamelets with increasing u` supports models predicting flame stability and potential industrial combustion efficiency improvements. Comparisons with numerical St results using the Zimont Turbulent Flame Speed Closure (Zimont TFC) model showed good agreement at lower (u' = 0.5 m/s) and mid-range (u' = 2.0 m/s) turbulence intensities but discrepancies at higher intensities (u' = 3.0 m/s), highlighting the need to refine numerical models for more accurate predictions across all turbulence levels.

Utilizing Deep Learning for Field-Level Information Extraction from German Real Estate Tax Notices

ABSTRACT Document processing and related tasks such as information extraction represent a large portion of business workloads and therefore offer high potential for efficiency improvements and process automation. Although semistructured documents such as invoices and receipts have been extensively studied, tax notices represent a largely unexplored area due to their information richness and layout complexity. This article presents a study investigating the potential of deep learning-based information extraction for German real estate tax notices. We propose a hybrid approach based on the LayoutLM transformer model that uses textual, positional, and visual information as well as a rule-based information extraction. We show how such a system can be developed and integrated into the business workflow of organizations on the example of a German tax consulting firm. We also discuss the implications and key challenges that organizations face when planning to adopt such technologies. Data Availability: Custom data and code are not publicly available. Relevant public source code is cited in the text. JEL Classifications: M15; M40; M41.

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection

ABSTRACT Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal–Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.

Estimating the potential for thermal energy efficiency in the industrial sector: A hybrid model integrating exergy analysis and long-term technology diffusion scenarios

Thermal energy for heating and cooling accounts for a significant portion of the total energy consumed in the industrial sector, and is predominantly provided by fossil fuels. Thus, effective thermal energy management is essential for reducing overall energy consumption, operational costs, and emissions in the industrial sector. While previous studies have explored energy efficiency in this sector, there is a lack of research specifically addressing the challenges of modeling thermal energy efficiency in industry. Therefore, this study proposes a novel hybrid methodological approach that integrates macroeconomic perspectives with detailed end-use analysis and exergy considerations, allowing for a more accurate assessment of recoverable energy in industrial processes in alternative technology diffusion scenarios over a long-term horizon. An empirical study focusing on the pulp and paper subsector in Brazil demonstrated the applicability of the proposed model and revealed significant potential for thermal energy efficiency improvements. The model identified that through optimal technology diffusion scenarios, the sector could achieve up to 25 % reduction in thermal energy consumption by 2050, with heat recovery systems accounting for approximately 40 % of these savings. Cost-effective technology adoption curves indicated that 60 % of this potential could be realized through economically viable investments with payback periods under 3 years. Overall, this study contributes to the field of industrial energy efficiency by offering a hybrid methodological approach that can be adapted to different industrial settings, and can help policymakers and industry stakeholders develop effective strategies to improve thermal energy efficiency and reduce emissions in the industrial sector.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.