Cost Reduction Research Articles

LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches

The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny—lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices.

Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost.

Engineering Premium Customer Acquisition: A Technical Framework for Value-Based Bidding Implementation

This technical article presents a comprehensive analysis of Value-Based Bidding (VBB) implementation frameworks and their impact on digital advertising performance. Through examination of data from 1,000 e-commerce campaigns encompassing 5 million customer transactions and $250 million in ad spend during 2023-2024, we demonstrate the effectiveness of VBB in premium customer acquisition. The article establishes a novel technical framework for VBB implementation, incorporating real-time data processing of over 1 million customer interaction points per second. Key findings reveal significant improvements across critical metrics: 92% attribution accuracy (a 14% increase), 85% cross-device matching success rate, and 73% improvement in offline conversion capture. The implementation of sophisticated customer segmentation strategies resulted in ROI improvements of 185% for high-value segments and 125% for medium-value segments. Furthermore, the article introduces advanced optimization algorithms that achieved a 23% reduction in customer acquisition costs while improving bid efficiency scores to 0.92. These findings provide a technical blueprint for organizations seeking to enhance their digital advertising effectiveness through value-based methodologies Value-Based Bidding (VBB) represents an advanced approach to digital marketing that focuses on maximizing return on investment. The system analyzes customer behavior and value to make smart decisions about advertising spend. Key performance indicators like Return on Ad Spend (ROAS – how much revenue you generate for each advertising dollar spent) and Customer Acquisition Cost (CAC – the cost to acquire a new customer) help measure success. The data shows impressive improvements, with ROAS increasing by 156% and CAC reducing by 23%, meaning more efficient spending and better results.

Novel sampling strategy for regular nucleic acid testing in low risk areas during epidemics

Large-scale regular nucleic acid testing (NAT) such as Regular-1/7, played a crucial role in the successful and sustained containment of COVID-19 in China, enabling rapid case detection in low-risk areas. However, Regular-1/7 strategies are extremely costly and time-consuming. To address these challenges and facilitate the broader implementation of large-scale NAT during outbreaks of highly contagious diseases with long incubation periods, we propose a novel sampling strategy called Weighted Hub and Time Sampling (WHTS). This strategy considers the intensity of social activity, the degree of social overlap, and the time interval since the last NAT for each individual. Based on a scale-free contact network model, we simulated the spread of scenario in a community and compared the performance of the WHTS strategy to the Regular-1/7 strategy in terms of cost reduction and shorter alert time. The results show that WHTS-1/14 strategy can save half of the cost, and WHTS-1/7 strategy can alert 1.0 days in advance, compared to Regular-1/7 strategy. Additionally, The WHTS-1/14 strategy also achieves promising results under various scenarios, even though it samples half as many people as the Regular-1/7 strategy. Our study offers a valuable reference for large-scale testing in future efforts to manage emerging infectious diseases.

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

Unlocking the drivers behind logistics service providers' participation in Morocco's logistics clusters

PurposeIn recent years, the concept of logistics clusters has attracted the attention of several researchers and practitioners. It is an agglomeration of different types of companies and operations, notably those providing logistics services, such as supply chain organization, storage and distribution. The paper focuses on this concept by examining the factors influencing LSPs to enter the logistics clusters.Design/methodology/approachAn exploratory qualitative study based on semi-structured interviews was conducted. Regarding the sample size, the author interviewed 31 professionals belonging to logistics clusters located in the Moroccan economic metropolis of Casablanca.FindingsThe results show that the cost reduction, the reputation of the cluster, the learning from other LSPs, the communal services, the geographical proximity and the role of public authorities are the main factors impacting the entry of logistics service providers into these clusters.Originality/valueThis theme has never been investigated. Therefore, this research expands the literature review and tries to examine this gap of literature by studying the factors that can affect the adhesion of logistics service providers to these clusters.

Adoption of Rooftop Solar Power as an Energy Option: A Household Level Study in Pune Smart City

Power generation using fossil fuels on a large scale contributes to global greenhouse gases and, therefore, impacts climate change. Renewable energy sources like solar power are needed to overcome such adverse impacts. Solar energy has received attention in the recent years due to both technological improvements resulting in cost reductions as well as the government policy push for renewable energy development and utilization. This research concerns with the adoption of rooftop solar power as an energy option at household level in the context of smart cities. We conducted a questionnaire survey on a sample of resident households in Pune smart city and analysed various parameters concerning households’ adoption. It has been found that the respondents are less willing to adopt rooftop solar power option for the lack of awareness of energy conservation and climate change issues, lack of financial incentives for the adoption and concern for operational issues that arise after the adoption; the respondents who are aware are more likely to adopt it in a timeframe of 1–3 years. Statistical analysis of various important relationships may be useful for future research on household adoption of renewable energy options, such as rooftop solar power.

Fatigue Assessment of Copper‐Brazed Stainless‐Steel Joints for Plate Heat Exchangers

ABSTRACTCyclic pressures can cause fatigue failure in the brazed joints and plates of the plate heat exchangers (PHEs). This study examines the fatigue behavior of PHEs made from 316L and 304L steels brazed with copper foils employing strain‐controlled fatigue tests to explore if 304L could replace 316L in the existing production line for cost reduction. Fatigue tests were conducted at four different load levels with a stress ratio of zero and a frequency of 5 Hz. Finite Element Analysis was used to assess strain distribution and estimate PHE lifespan based on generated strain versus number of cycles to failure curves. The microstructural analysis revealed that copper diffuses more easily into 316L than 304L, and using 50 μm thick foil causes more defects compared with 100 μm foil. It was shown that 316L joints have a significantly increased fatigue life compared with 304L. Both 316L and 304L met the 15‐year lifetime requirement set by manufacturers.

Influence of Credit Limitations on the Growth of Small Enterprises in Fragile States: The case of Bosaso, Somalia

In recent years, the development of small enterprises has emerged as a vital component of economic growth and poverty alleviation in many countries. This study aimed to investigate the influence of credit limitations on the growth of Small Enterprises in Bosaso, Somalia. To achieve this objective primary data was collected and analyzed employing content analysis and multiple regression analysis. The study revealed that a majority of small enterprises in Bosaso have access to credit, with Microfinance Institutions (MFIs) and banks being the primary sources. However, a significant minority still lacks access to credit, mainly due to weak banking infrastructure and insufficient collateral. Small enterprises face numerous challenges when seeking loans, including insufficient guarantor, Political instability, limited financial infrastructure, high interest rates, and limited collateral. These challenges hinder their ability to access credit, limiting their growth potential. Also, the regression analysis indicated a positive and significant relationship between credit availability and small enterprise growth. Conversely, the access to loan and the cost of credit were found to have negative impacts on growth. On the other hand, Small Enterprises in Bosaso employ various coping strategies to overcome credit limitations, including informal financing, asset-based financing, diversification of income streams, cost reduction measures, and seeking grants or donor funding. Based on these findings, the authors made several recommendations to address the challenges and enhance the growth prospects of small enterprises in Bosaso, Somalia.

Dynamic Modeling and Optimization of Energy Storage in Peer-to-Peer Energy Trading Systems

The power system landscape has evolved from isolated end-users to interactive communities due to advances in information and communication technologies. This paper explores peer-to-peer energy (P2PE) trading and sharing within a community, where customer incentives for energy exchange enhance collective profits. A two-stage optimization (TSO) framework is proposed: the first stage determines customer participation in P2PE, balancing individual and collective benefits, while the second stage optimizes economic aspects of P2P trading using a payment bargaining model. A case study demonstrates significant cost reductions and improved renewable energy utilization, with notable profit increments for participants. The study highlights the effectiveness of Nash bargaining theory and privacy-preserving algorithms in optimizing social welfare and economic interactions. Limitations include a focus on wind energy and simplified assumptions about energy storage. Future research should incorporate diverse renewable sources, dynamic modeling, and multi-community interactions.

BN SO14 - Evaluation of local cholecystectomy service and outcomes (Local Data for GECKO study)

Abstract Background Cholecystectomy is a common procedure performed for different biliary pathologies. Patients have their procedure as elective, emergency, or prioritised bases (Hot Gall bladder service). Method GECKO is a prospective, international, observational collaborative cohort study Consecutive patients undergoing cholecystectomy, between 23rd Oct – 19th Nov 2023 with follow-up at 30-day and one-year postoperatively Results Over a 4-week period , 38 cholecystectomies were performed, with 47.5% of patients having prior cholecystitis or cholangitis and multiple admissions. Half of the surgeries occurred within 6 months, and the longest wait was 13 months. Procedures were split equally between elective and emergency cases. Indications included acute cholecystitis (13), biliary colic (11), and others. All surgeries were laparoscopic, with 2 subtotal cholecystectomies. Complications included bile or stone spillage (12) and bleeding (7). Postoperative follow-up showed 16 patients discharged within 24 hours, 3 readmissions, and a 37% complication rate. No bile leaks or CBD injuries were reported. Conclusion Emergency and hot cholecystectomy services not only cut down waiting lists but also reduce costs of reoccurring admissions and treatments, with no significant associated complications. Selective gall bladder histological examination may help cost reduction with no significant recognised risks in the literature.

Algal Biosensors for Detection of Potentially Toxic Pollutants and Validation by Advanced Methods: A Brief Review

The presence of potentially toxic pollutants, such as pesticides and metal ions, even at low concentrations, can significantly impact aquatic environmental health. This pollution is a globally widespread problem and requires fast and reliable analysis, especially for in-situ identification/quantification. Atomic absorption spectrometry and plasma-based spectrometry techniques have been considered the most analytical tools used to monitor potentially toxic metal ions in aquatic media and other related matrices. The dynamics of global climate change and its correlation with pollution, especially from anthropogenic sources, have encouraged the development of other faster analytical tools for monitoring these pollutants. A noteworthy alternative for determining potentially toxic pollutants is using algae-based biosensors, resulting in a cost reduction and simplification of environmental analysis, enabling a more reliable comprehension of the role of humans in climate change. These biosensors, which may not have the highest sensitivity in quantification, have demonstrated remarkable potential in the identification of potentially toxic pollutants and several field applications. Biosensors can be an excellent biotechnology solution for monitoring global environmental changes. Thus, this review highlights the main advances in developing and comparing algae-based biosensors and other analytical possibilities for the identification of potentially toxic pollutants and their possible applications in environmental analysis.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Studies on the ssDNA-Based Biosensor Regeneration and Miniaturization for Electrochemical Detection of miRNAs

Abstract One of the most significant disadvantages of biosensing systems is the limited possibility of their regeneration, which only allows for their single use for detection of most targets. The reduction of biosensor fabrication cost could thus be achieved by elaboration of protocol providing the highest recovery of sensing layer response. A further drop of production expenses could yield the biosensor miniaturization as it leads to consumption of chemicals required for receptor layer formation as well as execution of measurements. To address the above-mentioned challenges, we aimed to find the most adequate method of regeneration of single-stranded DNA-based layers specific to miRNA 141 molecule which elevated concentration might refer to progression of cancer. The studies indicated that 5 min. incubation of ssDNA-modified electrode in 4 M urea provided the highest response towards miRNA 141 among all tested regeneration procedures. Furthermore, the possibility of ssDNA immobilization on was shown. This enabled miRNA 141 detection within 0.1 nM – 1 µM concentration range with high selectivity. Moreover, ssDNA layers elaborated on miniaturized transducers were distinguished with sufficient stability after 24 h storage in 20 mM PBS and could be also regenerated using 4 M urea.

Human–African buffalo conflict: perceptions of damage, and mitigation strategies, in villages bordering Ngorongoro Conservation Area, Tanzania

Abstract Human–wildlife conflict is a critical and complex challenge in wildlife conservation. It arises when humans and wildlife interact and one or both parties suffer negative consequences from the interaction. This research assessed the extent of damage resulting from human–African buffalo Syncerus caffer conflict and explored mitigation strategies. We used a semi-structured questionnaire-based survey of 131 households randomly selected in Kambi ya Simba, Oldeani and Tloma villages surrounding Ngorongoro Conservation Area, Tanzania. Our results revealed that resource competition was the main factor inducing human–African buffalo conflict. The impacts reported most frequently were crop damage (especially during the wet seasons) and human casualties. Crops that were most often damaged included maize, beans, wheat, peas and coffee. To minimize buffalo crop depredation, farmers currently use traditional mitigation approaches such as guarding farms, lighting fires, using torchlight and vocal and other auditory deterrents. In addition, a local coffee estate installed electrified fencing around its plantation. Our findings demonstrate the impacts of human–African buffalo conflict on local communities and the importance of continuing human–African buffalo conflict monitoring to improve conservation action and increase the participation of the local community in conservation activities. To minimize human–African buffalo conflict, we recommend conservation strategies that improve the natural habitat of the African buffalo. Most importantly, providing communities affected by human–African buffalo conflict with modern and more effective mitigation methods, paired with increased community awareness of the use of these methods, could result in significant reductions in the human cost of human–African buffalo conflict.

Estimating production functions using costs when outputs are restricted

Conventional proxy-variable approaches to production-function identification assume that output quantities are flexibly adjustable, with firms endogenously choosing them in pursuit of profits. But in not so few industries outputs are exogenously restricted, constraining input allocations by producers. Model assumptions of most proxy estimators become untenable in such settings. We provide a new structural methodology based on a dual formulation of firm production, indirectly identifying technology and productivity from cost data while appropriately accommodating exogenous output restrictions. We showcase our methodology on a panel of U.S. natural gas-fired power-generation plants in 2005–2017 facing price-inelastic demand that restricts their outputs in the short run. We find that power plants were scale-efficient, with (short-run) marginal costs of net electricity generation declining over time. With little evidence of productivity growth in the industry during our sample period, we conclude that the reduction in marginal costs was fueled mainly by diminishing input prices and, potentially, convergence to the optimal long-run input allocation.

Repeated Fed-Batch Culture Strategy for the Synthesis of Polyhydroxybutyrate (PHB) Biopolymers from Sugar Cane Juice Using Azotobacter vinelandii.

In this research work, a main biopolymer group of polyhydroxyalkanoates (PHAs) in the form of polyhydroxybutyrate (PHB) was synthesised by a pure bacterial strain of Azotobacter vinelandii via repeated fed-batch fermentation. An agricultural crop, sugar cane, was used as the sole carbon source. Firstly, batch fermentation was investigated considering variations in incubation times (24 h, 48 h, and 96 h). The highest dry cell weight (DCW) and PHAs of 5.15 ± 0.04 g/L and 4.00 ± 0.04 g/L were obtained after 48 h of incubation time. The optimum time obtained was further varied to investigate the effect of the sugar concentrations in the medium. It was found that bacteria could grow very well and produced the highest DCW and PHAs (11.17 ± 0.15 g/L and 8.77 ± 0.06 g/L) when the culture medium with a 100 g/L sugar concentration was added. Later, repeated fed-batch fermentation was carried out to improve productivity. The results obtained revealed that PHA production was increased in the next cycle of the process. Furthermore, the final productivity (0.104 g/L·h) was increased 1.65-fold compared to the first cycle (0.063 g/L·h). Moreover, the culture strategy showed remarkable results, with reductions in both fermentation time and preparation cost.

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

A Comprehensive Review of Approaches in Carbon Capture, and Utilization to Reduce Greenhouse Gases

Addressing atmospheric CO2 levels is crucial for mitigating global warming and promoting sustainable fossil fuel use. This review explores various CO2 capture strategies, including pre-combustion, post-combustion, oxy-fuel combustion, direct air capture, chemical looping, and polymeric membranes. Each strategy is critically evaluated in terms of its advantages, limitations, and overall effectiveness. Additionally, this study discusses advanced separation techniques for captured CO2, emphasizing recent innovations in membrane technology integrated with cryogenic processes. This integration has the potential to economically extract CO2 from diverse industrial processes, offering significant benefits in terms of operational cost reduction and increased efficiency. A detailed market analysis is also presented to explore feasible CO2 utilization options, highlighting potential incentives and motivations for capturing CO2. Furthermore, the technological readiness level of various capture and separation techniques is assessed, offering insights into their development and progress over time. This comprehensive analysis aims to support the advancement of effective and economically viable CO2 management solutions, contributing to a more sustainable and climate-resilient future.

The Role of Middleware in Integrating Legacy Systems with Modern Policy Administration

This comprehensive article examines the critical role of middleware integration in modernizing insurance industry legacy systems. The article reveals that approximately 75% of insurers rely on systems that are 20-40 years old, consuming up to 70% of IT budgets in maintenance. Through a detailed examination of implementation strategies, benefits, and challenges, the research demonstrates how middleware solutions serve as a crucial bridge between legacy infrastructure and modern Policy Administration Systems (PAS). The article covers key aspects, including protocol translation capabilities, service orchestration frameworks, and data transformation excellence, while providing quantitative metrics on cost reduction (20-30%), operational efficiency improvements (40-50%), and system performance enhancements (99.9% reliability). The article also presents a structured framework for implementing best practices, incorporating assessment, architecture design, security considerations, and testing strategies validated through industry case studies and expert analysis.