Cost Reduction Research Articles

Intelligent automation computational modelling for contextual consulting services using Industry 4.0

<p>The methodology towards operational management and service delivery associated with consulting firms, irrespective of any business domain, are less revisited in perspective from the automation-based process management. Adoption of Industry 4.0 has been attempted by various researchers from the business process management; however, there are less evidence of any computational model towards it. Apart from this, existing models are accompanied by various loopholes which makes its further challenging to analyze it on practical environment. Hence, the proposed study introduces a novel computational and analytical framework which is capable of performing the predictive modelling in order to meet the contextual service development and delivery demands in distributed environment. The novelty of this model is its inclusion of contextual data aggregation, contextual constraint analysis, predictive maintenence, and self-adjusting machine which are core attributes of Industry 4.0 automation standards. The study outcome shows that proposed system offers 20% cost reduction and 89% of minimized service delivery time in contrast to existing related work. Same has been also observed in benchmarked outcomes with state-of-art models.</p>

Aligning interoperability architectures for digital agri-food platforms

Interoperability is a critical challenge in Agri-food digitalisation, aligning with European data strategies and fostering pan-European cooperation, innovation, cost reduction, and market competition. Within the last years, the European Commission funded several projects to address interoperability in digital agriculture. We compare the projects IoF2020, ATLAS, and DEMETER which address interoperability for Agri-food systems with varying architectures and approaches, supported by the OpenDEI project. Interoperability, defined as the ability of systems to exchange and use information, encompasses multiple dimensions, including legal, organisational, semantic, and technical aspects. The analysis in this comparison focuses on the semantic and technical interoperability aspects of each project, covering architectural approaches, contextualization, marketplace functionalities, and user management and security measures.The examined projects highlight a variety of solutions, including service-oriented, data-centric, and process-oriented approaches, with the optimal choice between them remaining open, and an emphasis on the need for data consistency and plausibility. Advancements in standards and technologies, such as NGSI-LD, OGC, GAIA-X, and DSBA's implementation-driven plan, indicate a shift toward open interoperability in the broader ecosystem.

Barcode-less Fruits Classification Using Deep Learning

<p>Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.</p>

The Applicability of Artificial Intelligence Marketing for Creating Data-driven Marketing Strategies

The purpose of this paper is to explore the applicability of AI marketing for creating data-driven marketing strategies. Notably, the paper illustrates the existing circumstances of artificial intelligence in marketing practice. Besides, this paper argues for awareness of AI for customer satisfaction, employing AI to improve positioning, applying AI for accurate decision-making, and utilizing AI for sales, cost, and risk reductions. Lastly, this paper compares the applicability of AI marketing within two major regions from four regions identified in the study. A two-step approach was used to address the research question. First, a systematic literature review was conducted to identify the knowledge gap. Second, primary research through a survey study was conducted. Respondents of the primary study were represented by 367 marketing practitioners with 22 different marketing professions, representing 11 countries from 18 different industries, mainly from the Baltic and Caucasus regions. Based on findings and analysis, conclusions, limitations, and concepts for the future study were highlighted. The findings synthesized AI drivers, barriers, and outcomes in marketing. Further, outcomes confirmed a positive relationship with unitizing AI marketing in long-term strategic marketing planning. The paper offers practical guidance to the companies or inspires marketing leaders to use AI in data-driven marketing strategies. It has a significant value due to the complexity of the current marketing environment, whether micro or macro. Marketing Practitioners are searching for added value to prove the applicability of AI marketing in everyday strategies for decision-makers.

Oahu as a case study for island electricity systems relying on wind and solar generation instead of imported petroleum fuels

A macro-scale electricity model was used to evaluate whether wind and solar generation, combined with short- and long-duration energy storage, could cost-effectively provide 100% of Oahu's electricity. A least-cost renewable electricity system was developed with 100% of hourly averaged demand met, based on 14 consecutive years of hourly wind and solar resource and electricity demand data. The system used current asset costs for wind generation, solar generation, short-duration battery storage, and long-duration hydrogen energy storage. Our results indicate that Oahu could transition to an electricity system reliant on wind and solar generation and battery and hydrogen storage with electricity costs lower than today's electricity costs. For Oahu, a least-cost wind-solar-battery electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.2458 per kWh. In comparison, from October 2022 through September 2023, electricity generation costs from the petroleum-dominated electricity system on Oahu were between $0.2126/kWh and $0.2987/kWh. This wind-solar-battery system would require 332 km2 (21.4%) of Oahu's land. Moreover, including hydrogen storage as a second form of energy storage led to reductions in system cost and land use. At current asset costs, a least-cost wind-solar-battery‑hydrogen electricity system that would have met 100% of hourly averaged demand would have a system cost of $0.1673/kWh and require 265 km2 (17.1%) of Oahu's land. This case study indicates that other isolated regions with similar resource profiles and a reliance on imported petroleum fuels could realize a decrease in electricity system costs when switching to renewables in conjunction with short- and long-duration energy storage, at current asset costs, while meeting demand in full over extended periods of renewable resource variability.

CADD Methods for Developing Novel Compounds Synthesized to Inhibit Tyrosine Kinase Receptors.

Growth factors and their receptor tyrosine kinases play a central role in regulating vital cellular processes such as proliferation, differentiation, division, and cell survival, and they are closely associated with the development of various types of cancer, particularly in the context of angiogenesis. Although several small chemical compounds targeting tyrosine kinase receptors have been approved by the FDA for cancer treatment by inhibiting angiogenesis, there is still a need for more effective medications. in silico studies are now crucial tools for the design of new drugs, offering considerable advantages such as cost and time reduction. In this review, we examined recent in silico research carried out between 2022 and 2024, focusing on new drug candidates synthesized to fight cancer, in particular by targeting tyrosine kinase receptors involved in the process of angiogenesis.

A modified feedforward-feedback time-frequency domain hybrid algorithm for multichannel active road noise control system

Multi-channel Active Road Noise Control (ARNC) systems require numerous adaptive filters to reduce noise at multiple positions inside the vehicle. However, road noise conditions are more complex, and the correlation between the reference signals and the target noise is often poor. When the driving conditions become slightly complex, the correlation between the reference signal and the target noise is often poor. As a result, traditional road noise control algorithms applied to multi-channel ARNC systems often suffer from high computational cost and poor noise reduction performance in non-steady-state and high-speed driving conditions. To address this issue,a reference signal selection method based on the genetic algorithm (GA) is proposed to obtain a reference signals combination with stronger coherence. On this basis, we introduce the time–frequency domain structure into the forward-feedback hybrid algorithm and a multi-channel time–frequency domain hybrid algorithm with a novel variable step size method (VSS-TF-HANC) is proposed in this paper.The feedforward part adopts a time–frequency domain structure and introduces a novel variable step-size strategy to reduce computational complexity and improve noise reduction performance. The feedback part adopts a control system based on the Internal Model Control (IMC) architecture to attenuate the interior noise components that have a strong response but poor correlation with the reference signals. Simulation results demonstrate that the proposed algorithm achieves excellent control results under complex driving conditions, with significantly better noise reduction performance compared to the control algorithms that solely employ the feedforward structure, while also exhibiting low computational cost.

Telemedicine integration into the eye health ecosystem in scaling of effective refractive error coverage in Kenya

This study explored the potential of telemedicine integration into the eye health ecosystem with an aim of scaling effective refractive error coverage in Kenya. This exploratory study was conducted telephonically and through online interviews with key opinion leaders, ophthalmologists, optometrists, ophthalmic clinical officers, optical technicians and beneficiaries of the optical technician’s services in rural areas. A telemedicine workflow was developed and validated based on the comments from the key opinion leaders using the Delphi technique. Quantitative and qualitative data were analysed using SPSS and NVivo Software respectively. All of the key opinion leaders agreed that telemedicine is relevant in the eye health ecosystem and recognition of primary vision technicians is critical for effective telemedicine integration. The reasons for the need of telemedicine integration were categorized into; good relationship, organized refractive error service delivery, convenience and availability and cost reduction. The possible factors influencing integration of telemedicine identified were categorized into cost, unwillingness, dominance, perception, lack of technical team, policies and network coverage. The limited human resources in the eye health ecosystem in developing countries cannot effectively deliver refractive error services to the growing population. Hence, integration of telemedicine and establishment of policies recognizing telemedicine are desirable to strengthen task shifting and scale effective refractive error coverage.

Cost reduction for upscaling voluntary sustainability standards: the case of independent oil palm smallholders in Central Kalimantan, Indonesia

Upscaling Voluntary Sustainability Standards (VSS) can generate ecological and social benefits at a scale that is meaningful to address pressing environmental issues such as climate change and biodiversity losses. Lack of resources and risks of implementation gaps may, however, hinder the success of upscaling VSS. This paper aims to fill the gap in the literature regarding options to reduce certification costs to upscale VSS amid the limited financial resources available for certification. The paper presents the result of action research involving 3,507 independent oil palm farmers who are members of six farmer groups in two districts in Central Kalimantan to achieve Roundtable on Sustainable Palm Oil (RSPO) certification. The research found that achieving economies of scale by increasing the total number of certified farmers can reduce the certification costs per farmer. By establishing a district-level entity, the costs related to audits, RSPO membership fees, and the establishment of farmer groups, such as developing standard operating procedures (SOPs), can be lowered. Implementation gaps were not found with the increase of farmers joining the project based on the training frequency and the external audit findings reports on farmers' compliance with RSPO principles and criteria. The findings of this study provide a basis for promoting the upscaling of VSS, including through the RSPO jurisdictional approach.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

Pushing the Operation Current Density of Alkaline Water Oxidation above 1 A cm2 via Electrocatalyst Engineering

AbstractRapid cost reduction of green hydrogen is essential for the large‐scale deployment of hydrogen‐based energy system. A major component in achieving this is the development of advanced water electrolyzers. Sluggish oxygen evolution reaction (OER) is the bottleneck for water electrolysis, and tremendous efforts have been devoted to develop electrocatalysts that accelerate the OER kinetics. Recent advances have highlighted the potential of precious metal‐free OER electrocatalyst capable of stable operation at current densities above 1 A cm2. This brings an opportunity of constructing new‐generation electrolyzer with lower cost and higher productivity. However, achieving such high operating current densities presents new challenges. This review summarize the recent progress of high‐performance OER catalysts, and identifies key factors that can be leveraged to enhance catalyst activity. These factors include interface/surface engineering, amorphous and crystal phase coupling, hierarchical structure design, structural reconstruction and phase conversion, high entropy catalyst, and defect engineering. Additionally, the pressing challenges that have been largely ignored in previous research is addressed, such as sustaining long‐term stability under crucial conditions, disadvantages of immobilized powder electrocatalysts, electrode scale design, the necessity of designing innovative electrolyzer cell designs and advance characterization techniques.

Numerical Calculation and Application for Crushing Rate and Fracture Conductivity of Combined Proppants

Proppant is one of the key materials for hydraulic fracturing. For special situations, such as middle-deep reservoirs and closure pressures ranging from 40 MPa to 60 MPa, using a single proppant cannot solve the contradiction between performance, which means crushing rate and fracture conductivity, and cost. However, using combined proppants is an economically effective method for hydraulic fracturing of such special reservoirs. Firstly, for different types, particle sizes, and proportions of combined proppants, various contact relationships between proppant particles are considered. The random phenomenon of proppant particle arrangement is described using the Monte Carlo method, and the deterministic phenomenon of proppant particles is processed using an optimization model, achieving computer simulation of the microscopic arrangement of proppant particles. Secondly, a mathematical model for the force analysis of combined proppant particles is established, and an improved singular value decomposition method is used for numerical solution. A computational model for the crushing rate and fracture conductivity of combined proppants is proposed. Thirdly, the numerical calculation results are compared and discussed with the test values, verifying the accuracy of the computational model. Finally, the application of combined proppants is discussed, and a model for optimizing the proportion of combined proppants is proposed. The onsite construction technology is introduced, and the cost and economic benefits of combined proppants are compared with those of all ceramic particles and excessive all-quartz sand. It is proved that combined proppants can balance performance and price, and are an economically effective method for hydraulic fracturing of special reservoirs. The research results can select the optimal proppant material and optimize the combination of different proppant types, which can help achieve cost reduction and efficiency increase in oil and gas development.

Exploring Front-of-House and Back-of-House Manager Perceptions, Attitudes, and Motivations of Restaurant Food Waste Mitigation: A Qualitative Study

This qualitative study explored and compared the perceptions, attitudes, and motivations towards restaurant food waste mitigation among front-of-house (FOH) and back-of-house (BOH) restaurant managers. Semi-structured, one-on-one interviews were conducted with FOH (n = 6) and BOH (n = 7) managers at restaurants in the southeast of the United States. The findings indicated that the participants were highly aware of restaurant food waste and its contribution to food waste; furthermore, they displayed negative sentiments towards it. Additionally, most participants’ awareness was heightened upon acquiring foodservice management positions. Cost reduction was found to be the primary motivation to reduce food waste among most participants; however, most BOH participants were highly motivated by an appreciation for and involvement with the food itself. Guided by the Upper Echelons Theory, the findings provide insight into the underlying cognitive base and values behind restaurant managers’ perceptions, attitudes, and motivations towards restaurant food waste mitigation. Theoretical and practical implications are discussed in detail.

A Reliability-Based Design Approach for the Flexural Resistance of Compression Yielded Fibre-Reinforced Polymer (FRP)-Reinforced Concrete Beams

Fibre-reinforced polymer (FRP) reinforcement has been employed as an alternative to conventional steel reinforcement in concrete structures, which is attributed to its excellent strength and corrosion resistance. However, one drawback is that FRP reinforcements are brittle and affect the ductility of concrete structures. One of the recent effective techniques proposed to overcome ductility issues is the compression yielding (CY) concept. The CY mechanism allows the structure to fail differently than the conventional FRP-reinforced concrete structure. Thus, the existing design recommendations as per the current codes for FRP-reinforced concrete structures are not appropriate. Hence, reliability studies are crucial for the development of a functional CY beam design in order to emphasise the structure’s lifetime performance and to guarantee safety requirements. In this study, a reliability-based design approach is developed for a compression-yielded FRP-reinforced concrete beam (CY beam) using load and resistance factor design (LRFD). Firstly, the flexural failure modes of CY beams are discussed. The uncertainties involved in the development of the probabilistic model for the CY beam are defined. A case study is consequently conducted for a CY beam with random variables that are associated with the statistical characteristics of material properties and load. The reliability analysis method employed in this research is the Hasofer–Lind method. The results suggest the importance of choosing appropriate design variables and stochastic parameters for CY blocks that contribute to a higher level of reliability. The reliability index and resistance factors of a CY beam are then evaluated using the Monte Carlo Simulation computational method. The reliability index value of 3.336 is obtained from the simulation, which indicates that the CY beam demonstrates ductile behaviour. The results not only demonstrate ductile behaviour but also contribute to a possible reduction in material costs and a substantial safety margin. When compared to conventional FRP-reinforced concrete beams, for different load ratios, CY beams showed higher resistance and better reliability levels.

Highly accurate single-color fluorogenic DNA decoding sequencing for mutational genotyping

We proposed a single-color fluorogenic DNA decoding sequencing method designed to improve sequencing accuracy, increase read length and throughput, as well as decrease scanning time. This method involves the incorporation of a mixture of four types of 3’-O-modified nucleotide reversible terminators into each reaction. Among them, two nucleotides are labeled with the same fluorophore, while the remaining two are unlabeled. Only one nucleotide can be extended in each reaction, and an encoding that partially defines base composition can be obtained. Through cyclic interrogation of a template twice with different nucleotide combinations, two sets of encodings are sequentially obtained, enabling the determination of the sequence. We demonstrate the feasibility of this method using established sequencing chemistry, achieving a cycle efficiency of approximately 99.5 %. Notably, this strategy exhibits remarkable efficacy in the detection and correction of sequencing errors, achieving a theoretical error rate of 0.00016 % at a sequencing depth of ×2, which is lower than Sanger sequencing. This method is theoretically compatible with the existing sequencing-by-synthesis (SBS) platforms, and the instrument is simpler, which may facilitate further reductions in sequencing costs, thereby broadening its applications in biology and medicine. Moreover, we demonstrate the capability to detect known mutation sites using information from only a single sequencing run. We validate this approach by accurately identifying a mutation site in the human mitochondrial DNA.

Optimization of Green Multimodal Transport Schemes Considering Order Consolidation under Uncertainty Conditions

As society becomes increasingly concerned with sustainable development, the demand for high-efficiency, low-cost, and green technology makes air–land multimodal transportation one of the effective means of fast freight transportation. In the actual transportation business, some orders will have overlapping transportation routes, and transporting each order separately will result in resource waste, high costs, and carbon emissions. This paper proposes a multimodal transportation scheme optimization model considering order consolidation to improve transport efficiency and reduce costs and carbon emissions. An improved genetic algorithm incorporating the ride-sharing scheduling method is designed to solve the model. The results show that order consolidation will reduce multimodal transport costs and carbon emissions but increase transportation time slightly, and the advantages in cost and carbon emission reduction will vary with origin–destination scenarios, which are ranked in order of single-origin single-destination, single-origin multi-destinations, multi-origin single-destination, and multi-origin multi-destination. For the fourth scenario, the cost and carbon emissions decrease by 16.6% and 26.69%, respectively, and the time increases by 5.56% compared with no consolidation. For the sensibility of customer demands, it is found that order consolidation has the advantage for price-sensitive, time- and price-sensitive, and time- and carbon emission-sensitive customers; however, it is specifically beneficial for time-sensitive customers only in single-origin single-destination scenarios.

Digital twin technology training and research in health higher education: a review

Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved user experience, and expanded capacity. In addition, modern automation technologies, which were implemented in industrial control systems or industrial automation control systems, are essential for ensuring the availability of societies’ critical cyber-physical systems (CPSs) and the services they provide, such as healthcare. This narrative literature review produces information that can be applied when planning and implementing an interdisciplinary biomedical and health informatics (BMHI) master’s education focused on the challenges of digitalization in the health sector. The review results that virtual human twins (VHTs) are revolutionizing healthcare by addressing people’s complex medical problems with real-time monitoring and precision care while digital twin (DT) technology can make the hospital’s operational processes resilient and efficient. Thus, future BMHI education must address these technologies with a multidisciplinary approach, including computer science, information science, engineering, basic sciences, health sciences, socio-behavioral sciences, and ethical, legal, and policy aspects. Collected and cumulative data is essential for cognitive DTs. A prerequisite for this data is information sharing between different CPSs. Better information sharing and the development of scalable cognitive DTs and VHTs, the provision of critical services, quality, and cost-effectiveness, as well as health, safety, and resilience, will improve. Similarities between peoples’ health information exchange and information needed for ensuring the resilience of CPSs exist. Since humans are in many ways more complex than CPSs, security engineers have a lot to learn from VHTs in maintaining the resilience of CPSs. Cross-sectoral research and cooperation with different disciplines are essential for the progress of both human health and the resilience of CPSs. Along with interdisciplinary research cooperation, educational cooperation should also be intensified.

Medical emergency team activation and cost reduction in cardiovascular surgery and nonsurgical patients: DOISNORE50 sleep protocol.

Medical emergency team activation and cost reduction in cardiovascular surgery and nonsurgical patients: DOISNORE50 sleep protocol.

Impact of routine bedside lung ultrasound implementation on chest x-ray requests for mechanically ventilated children: a before-after uncontrolled quality improvement project.

Lung imaging techniques are crucial for managing ventilated patients in pediatric intensive care units (PICUs). Bedside chest x-ray has limitations such as low sensitivity and radiation exposure risks. Recently, lung ultrasound has emerged as a promising technology offering advantages such as real-time monitoring and radiation-free imaging. However, the integration of lung ultrasound into clinical practice raises questions about its impact on chest x-ray prescriptions. This study aims to assess whether implementing lung ultrasound reduces reliance on chest x-rays for ventilated pediatric patients in the PICU. This before-and-after uncontrolled quality improvement project was conducted from January 2022 to December 2023 in a referral PICU. The study included three phases: retrospective evaluation, learning phase, and prospective evaluation. Patients aged under 14years, intubated, and ventilated for ≤ 30days were included. Lung ultrasound was performed using a standardized protocol, and chest x-rays were conducted as per clinical indications. During the study period, 430 patients were admitted to the PICU, with 142 requiring mechanical ventilation. Implementation of routine bedside lung ultrasound led to a 39% reduction in chest x-ray requests (p < 0.001). Additionally, there was a significant decrease in irradiation exposure and a 27% reduction in costs associated with chest x-rays.Conclusion: Routine bedside lung ultrasound is a valuable tool in the modern PICU, it reduces the number of chest x-rays, with reduced radiation exposure and a potential cost savings. What is known: • Bedside chest x-ray is the main imaging study in ventilated pediatric patients • Chest x-ray is a valuable tool in pediatric critical care but it is associated with irradiation exposure What is new: • Implementation of bedside lung ultrasound in pediatric critical care unites reduces the chest x-rays requests and therefore patient-irradiation.

The impact of digital infrastructure on the geographical supply chain layout of firms —— Evidence from Chinese microdata

This study investigates how the development of digital infrastructure in China affects the geographical layout of supply chains of listed companies. This work finds that (1) The development of digital infrastructure has led to firms prefer suppliers or customers nearby, rather than looking for distant supply chain partners. (2) The development of digital infrastructure can reduce the costs of the firms, and improve their productivity. However, firms with cost reduction and productivity improvement will not expand the geographical scope of their supply chains. From the perspective of the concentration of supply chains, firms reduce their dependence on the top five customers and suppliers, indicating that with the support of digital technology, firms are willing to seek new partners to achieve closer and more diversified supply chains. (3) Heterogeneous analysis shows that firms with longer operational time, located in developed regions and eastern regions, or in primary and secondary sectors will choose a closer supply chain partner under the influence of digital infrastructure. (4) Extended analysis shows that digital infrastructure in neighboring cities will weaken the preferences of firms in those regions to find supply chain partners nearby.