Reduction Of Expenses Research Articles

Memristor based on carbon nanotube gelatin composite film as artificial optoelectronic synapse for image processing

Photoelectric artificial synapses based on memristors is an effective method to realize neuromorphic computation. This study presents an optoelectronic responsive artificial synapse made of a composite material consisting of gelatin and carbon nanotubes. The memristor demonstrates characteristics of analog resistive switching, the ability to store multiple memory states, and impressive retention properties. It has the capability to induce an excitatory post-synaptic current by means of electrical pulses or pulsed light exposure. The excitatory post-synaptic current can be modulated by the number, amplitude and interval of electrical pulses, as well as the action time, interval and light intensity of optical pulses. The artificial synapse showcases the emulation of fundamental Hebbian learning protocols, including spike timing dependent plasticity and spike amplitude dependent plasticity. In addition, the charge transfer in the carbon nanotube gelatin composite optoelectronic memristor is investigated through first-principles calculations, shedding light on its operational mechanism. Experimental results show that these devices have the potential to be utilized for processing image information, resulting in a significant reduction of input data and training expenses when recognizing handwritten numbers. Overall, the optoelectronic synapse exhibits promising image processing prospects in the field of neuromorphic computing.

Reducing Medical Costs of Health Insurance: The COVID-19 Stress Testing and Portfolio Effects

Reducing medical costs is one of the three strategic aims of U.S. health care reform. This research quantifies potential expense reductions of U.S. health insurers and examines portfolio effects on expenses of differential health insurance mixes. We use the coronavirus disease 2019 (COVID-19) pandemic as a natural stress test for potential savings in medical services. We employ a two-stage residual inclusion generalized linear model and uncover differential expense reductions for four major health insurance markets: individual, group, Medicaid managed care, and Medicare Advantage. Our results could serve as a benchmark for reducing medical costs through redesigning insurance coverages following the most effective group insurance model. The results also provide policy implications on restructuring health insurance markets to increase efficiency. We empirically examine mutual impacts among major health insurance markets, and document explicitly three optimal health insurance portfolios in reducing expenses of health care: Medicaid managed care and individual plans, exclusively group plans, and Medicaid managed care and Medicare Advantage plans.

For a Pluralism of Climate Modeling Strategies

Abstract The continued development of general circulation models (GCMs) toward increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channeling of research and funding to meet this aspiration. Yet many other modeling strategies have also been developed and can be used to understand past and present climates, to project future climates, and ultimately to support decision-making. We argue that a plurality of climate modeling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policymaking. To support our claim, we use a philosophy of science approach to compare the increasing resolution and complexity of general circulation models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth system models of intermediate complexity. We show that each of these strategies prioritizes a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modeling strategies are all potentially useful and can be used in a complementary way.

Diagnosing Incipient Faults for a Faster Adoption of Sustainable Aerospace Technologies

Next-generation aircraft require the development and integration of a deal of innovative green technologies to meet the ambitious sustainability goals set for aviation. Those transformational efforts are associated with a tremendous increase in the complexity of the onboard systems and their multiphysics-coupled behaviors and dynamics. A critical aspect relates to the identification of the coupled faults resulting from the integration of those green technologies, which introduce damage identifiability issues and demand new approaches for the efficient and accurate identification of non-nominal fault conditions. Model-based fault detection and identification (FDI) methodologies have been proven essential to identify onboard the damages affecting the systems from physical signal acquisitions, but existing methods are typically computationally expensive and fail to capture incipient coupled faults, which prevents their adoption and scaling with the increasing complexity of novel multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed, and the exact identification of the incipient fault condition was achieved one order of magnitude faster than with standard algorithms.

Balancing Costs and Care: Nonoperative Appendicitis Treatment Outcomes in Pakistan.

Dear Respected Editor, In Pakistan, approximately 110,000 cases of appendicitis are reported annually [1], highlighting the condition's prevalence and significance. Traditional management predominantly revolves around widely practiced surgical intervention, such as appendectomy. [2]. Immediate appendectomy has become the standard treatment due to the significant morbidity and mortality difference between perforated and non-perforated acute appendicitis. However, the country's healthcare landscape presents a complex challenge due to varying regional access to medical resources. This variation in healthcare infrastructure exacerbates the difficulty of effectively managing appendicitis and underscores the need for solutions that strike a balance between clinical outcomes and economic considerations. While appendectomy remains a standard surgical procedure, it comes with inherent disadvantages, chief among them being the cost burden it places on patients and healthcare systems [3]. For patients in a low- to middle-income nation like Pakistan, the financial strain associated with surgery—which includes preoperative evaluations, surgical costs, hospital stays, and postoperative care—can be challenging [4]. Furthermore, the ripple effects affect the whole healthcare system, diverting resources that may otherwise be used for other urgent medical needs. A study by Siddiqui et al. emphasizes the need for a comprehensive analysis of economic factors in appendicitis management, particularly in low- and middle-income countries like Pakistan. The challenges in countries such as Pakistan require the exploration of alternative approaches that can optimize clinical outcomes while effectively managing healthcare costs without compromising patient care. [2] The recent study by Lee et al. introduces a promising alternative to conventional appendicitis management [5]. The research focuses on nonoperative treatment approaches, such as antibiotic therapy and observation, which have gained traction for their potential benefits Further, the study shows that the majority of the patients who had nonoperative treatment did not encounter a recurrence of appendicitis and were able to avoid surgical intervention for a period of around 20 years. Furthermore, there is a lack of data indicating any long-term dangers associated with nonoperative care. One of the prominent advantages lies in the reduction of immediate healthcare expenses, which can alleviate the financial burden on both patients and healthcare systems. By presenting evidence on the effectiveness and safety of nonoperative approaches, this study opens avenues for a paradigm shift in appendicitis management in Pakistan. The potential to achieve comparable clinical outcomes while minimizing the economic burden is a compelling proposition that warrants further exploration and consideration within Pakistan's healthcare challenges.

The application of a non-hydrostatic RANS model for simulating irregular wave breaking on a barred and sloping beach

In the current study, a non-hydrostatic two-dimensional vertical (2DV) numerical model with a shock-capturing technique is developed to simulate irregular wave breaking on a barred beach. The complete form of the 2DV Reynolds-averaged Navier-Stokes (RANS) equations is discretized using the finite volume approach. In the spatial discretization phase, a flux limiter function is employed for the velocity advection terms to prevent non-physical oscillations in regions with steep gradients. A novel method has been introduced during the discretization stage, leading to a significant reduction in computational expenses. For temporal discretization, the Leapfrog method, known for its second-order accuracy in time, is employed to address wave-damping issues. Model validation involves a comparison between simulation results and experimental data pertaining to irregular wave breaking, affirming the satisfactory performance of the model. To evaluate the accuracy of the model, the Root Mean Square Error (RMSE) was calculated. The assessment showed that the model is capable of estimating the significant wave height reported in the experiments used with an RMSE between 0.002 and 0.089, and the mean wave periods with an RMSE between 0.061 and 0.252.

Energy geo-structures: A review of their integration with other sources and its limitations

Ground Source Heat Pumps, in the framework of Shallow Geothermal Energy Systems, outperform conventional Heating Ventilation and Air Conditioning systems, even the high efficiency Air Source Heat Pumps. At the same time, though, they require considerably higher installation costs. The utilization of dwellings' foundations as ground heat exchanger components has recently demonstrated the potential to generate significant cost reductions primarily attributed to the reduction in expenses associated with drilling and backfill material (grout). These elements are referred to in the literature as Thermo-Active Structures or Energy Geo-structures (EGs). The current study employs a ‘mixed studies’ review (i.e., literature review, critical review and state-of-the-art review) methodology to comprehensively examine and assess the compatibility and integration of different renewable energy sources and environmentally friendly technologies with foundation elements deployed as EGs. These mainly include heat pumps, district heating and cooling networks, solar-thermal systems, waste heat, biomass and other types such as urban structures. Emphasis has been given on the advancement on this area, with the current study identifying and addressing two primary categories. The first category involves the integration of EG elements with sources that are able to supply green electricity, referring to renewable energy electricity obtained from on-grid or off-grid integration. The second category, involves a direct or indirect integration with sources that provide heat, or vice versa. The technical and non-technical barriers of such integrations have been discussed in detail, with the technical challenges generally involving engineering design, and system optimization, whereas non-technical challenges encompassing the economic, social, and policy domains.

Model-Informed Development of a Cost-Saving Dosing Regimen for Sacituzumab Govitecan.

The antibody-drug conjugate sacituzumab govitecan is approved for metastatic triple-negative breast cancer and has shown promising results in various other types of cancer. Its costs may limit patient access to this novel effective treatment modality. The purpose of this study was to develop an evidence-based rational dosing regimen that results in targeted drug exposure within the therapeutic range while minimizing financial toxicity, to improve treatment access. Exposure equivalent dosing strategies were developed based on pharmacokinetic modeling and simulation by using the published pharmacokinetic model developed by the license holder. The alternative dose was based on the principle of using complete vials to prevent spillage and on the established non-linear relationship between body weight and systemic exposure. Equivalent exposure compared to the approved dosing regimen of 10 mg/kg was aimed for. Equivalent exposure was conservatively defined as calculated geometric mean ratios within the 0.9-1.11 boundaries for area under the concentration-time curve (AUC), trough concentration (Ctrough) and maximum concentration (Cmax) of the alternative dosing regimen compared to the approved dosing regimen. Since different vial sizes are available for the European Union (EU) and United States (US) market, because body weight distributions differ between these populations, we performed our analysis for both scenarios. Dosing regimens of sacituzumab govitecan for the EU (<50 kg: 400 mg, 50-80 kg: 600 mg, and >80 kg: 800 mg) and US population (<40 kg: 360 mg, 40-65 kg: 540 mg, 65-90 kg: 720 mg, and >90 kg: 900 mg) were developed, based on weight bands. The geometric mean ratios for all pharmacokinetic outcomes were within the predefined equivalence boundaries, while the quantity of drug used was 21.5% and 19.0% lower for the EU and US scenarios, respectively. With the alternative dosing proposal, an approximately 20% reduction in drug expenses for sacituzumab govitecan can be realized while maintaining an equivalent and more evenly distributed exposure throughout the body weight range, without notable increases in pharmacokinetic variability.

The role and benefits of storage systems in distributed solar PV generation on public buildings in Brazil

This paper proposes a method for assessing the energy and economic impacts provided by the adoption of battery energy storage (BESS) in public buildings with integrated photovoltaic (PV) systems under current legislation. The method is applicable to prosumer units (PU) connected on the medium voltage grid and is based on techniques for measuring the electric energy demand and the surplus PV energy injected by the PU into the grid. Empirical data, including ambient temperature and solar irradiation, were employed to assess the solar radiation resource. In BESS simulations, PU power flows were utilized. The BESS defined operation (charging/discharging schedules) was aimed at the maximum use of the surplus PV energy and the largest reduction in electricity expenses (energy arbitrage). The suggested methodology was applied to a case study of a public building PU in Brazil. The results showed that, during peak hours, the adoption of the BESS would provide a 100 % reduction in measured power demands and consumed energy with a significant annual injection of power in the utility grid. During off-peak hours, the annual self-consumption of the PU would increase by nearly 30 %. This outcome underscores the benefits associated with time-of-use billing structure for public PU + BESS. Approximately 85 % of the total energy required to charge the BESS would be originated from the surplus of PV energy. The remaining 15 % would be supplemented by the utility grid. The findings show that currently, the insertion of BESS would not present financial attractiveness. However, it is anticipated that BESS costs will drop during the next few years. A sensitivity analysis was carried out which concluded that for a cost of US$408 (expected value for 2025) the BESS would present financial attractiveness.

Home Mechanical Ventilation in Children: Complications and Benefits—A Two-Year Experience at a Referral Children's Hospital

Background: The rising prevalence of chronic respiratory failure in pediatric patients necessitates a detailed evaluation of home mechanical ventilation (HMV). Objectives: This study assesses the advantages of HMV devices, focusing on cases from Imam Hussein Children's Hospital. Methods: This research involved 20 children who required long-term ventilation and were admitted to both general and intensive care units at Imam Hussein Children's Hospital in Isfahan. Before discharge, a team of HMV experts trained the parents on the proper use of the ventilation devices. Post-discharge, the educational needs of the parents were assessed continuously, with regular home visits conducted by a nurse and a representative from the ventilator supplier. Results: Approximately 73% of the children demonstrated therapeutic effectiveness following HMV implementation. About 35% of parents observed no adverse effects in their children. The average cost savings from reduced ICU rehospitalizations was significant at 347.59%, and there was a 65.09% reduction in the costs associated with general ward hospitalizations. Conclusions: The adoption of HMV has led to improved health outcomes, substantial reductions in medical expenses, and shorter hospital stays. It is advisable to plan for broader implementation of HMV in children who require long-term ventilation.

Application of Building Information Modeling (BIM) in the Planning and Construction of a Building

This paper aims to introduce Building Information Modeling (BIM) technology into the process of construction to achieve more efficient buildings, considering sustainability, economic improvements, reduced waste, and time optimization. The study involves the design, 3D modeling, planning, and monitoring of the work to assess the advantages and limitations of BIM through a real simulation. The methodology comprises theoretical foundations, 3D modeling, budgeting, and work monitoring. The project commenced with a 2D blueprint using Autodesk AutoCAD, followed by 3D modeling in Autodesk Revit. The resultant building, serving as an information system with a database and 3D modeling, allows for automated budgeting. The advantages of BIM encompass an interface between project and program, an interface between software, realistic renderings, precise material specifications, and family modeling. Limitations include the absence of material families, reformatting of Autodesk Revit tables when transferred to the Microsoft Excel program, generic family models with distorted renderings of reality, and heavy files. Despite these limitations, the implementation of BIM in buildings proves beneficial for projects. Collaborative work among stakeholders, optimization of project stages, and the reduction of expenses and rework can be achieved.

Deep learning and quantization for accurate and efficient multi-target radar inference of moving targets

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify ground-moving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints.

Development of an organophosphate and carbamate pesticide test kit by using a magnetic particle coated with esterase enzyme from honey bee heads

Organophosphates (OPs) and carbamates (CMs) play a vital role in Thai agriculture, public health, and pest control. However, their detection presents challenges due to expensive methods requiring specialized expertise. Addressing this gap, our study introduces an innovative, cost-effective method for detecting OPs and CMs in Thailand. We utilized the test kit by using magnetic particles coated with esterase enzymes from honey bee heads combined with a colorimetric approach. The developed test kit shows low limits of detection (LODs) at 0.001 mg/L of dichlorvos (OPs) and 0.004 mg/L for carbaryl (CMs). The efficiency of the developed test kits in comparison with the standard technique of gas chromatography with flame photometry detection (GC-FPD) shows a comparable result in fruit and vegetable residue analysis. This developed test kit proposes a cost-effective OPs and CMs analysis test kit for Thailand, promising expense reduction and simplified verification processes. Its applicability to fruits and vegetables signifies practicality in agriculture, marking a significant advancement in accessible and efficient pesticide residue monitoring. Despite challenges in pesticide use, our method holds promise for improving safety and efficiency in Thai pesticide management.Graphical

Integration of risk, flexibility, and resilience in the optimization of water–energy nexus

AbstractCo‐location of power plants and desalination systems allows for a reduction in operational expense through energy integration. Furthermore, augmenting fossil‐based power plants with solar energy provides a means of reducing the carbon footprint of electricity generation. It is also critical to protect the combined energy–water system against internal and external risk factors to maintain a reliable supply of both electricity and water. Therefore, a systematic approach for assessing and mitigating risks is needed. Because of the complex water–energy interactions, a superstructure representation is created and a quantitative risk assessment is conducted to show potential risk factors that target specific sub‐systems. A surrogate model of the flexibility index analysis is built in order to optimize the superstructure for both cost and flexibility objectives. Finally, the generated design is simulated against disruption scenarios to obtain its resilience against various risk factors. This approach is applied to a case study on the Kuwait water–energy plant to show how the developed approach can help decision‐makers create operational strategies to protect against risk in a cost‐effective manner.

Measurement of efficiency and its drivers in the Chilean banking industry.

This paper estimates efficiency measures for the banking system in Chile for the period 2000-2019. In contrast to previous studies, we use input-distance functions, introduce the nonparametric slack-based model, and choose the intermediate inputs approach in determining inputs and outputs. Our results suggest that the Chilean system has achieved relatively high levels of efficiency, although with no significant variation over the sample period. Ownership (government, foreign and public) and size had a positive impact on efficiency. On average, mergers and acquisitions seem to have targeted highly efficient banks in order to improve the overall efficiency of the controlling institution in the short run. Other sources of efficiency gains could be an increase in bond funding or a reduction in expenses and capital holdings. The latter could be induced by deepening the local derivatives market.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

Effect of Fare Charges on The Operations of Inland Waterways Transport in Lagos State

The study looks at how fare prices affect Lagos State's ferry operations, with a particular emphasis on commuter comfort and the distance traveled. Water transportation offers benefits like cheaper costs, increased capacity, and safety, but its potential has not been completely realized. This is because canal ports have defects that prevent the flexibility and convenience required for the flow of goods and services. The study focuses on the Lagos State Waterways Operations. A small number of carefully chosen canal terminals that link different regions of Lagos State make up the study's population. Purposive sampling was done at the Elegbata/Ebute Ero Terminal, Ebute Ojo Terminal, Ikorodu Terminal, and Badore Terminal. The snowballing technique was used to randomly select 818 passengers. Investigation revealed that daily fee charges have an effect on Lagos State's water transportation operations, and that commuters' comfort level and distance traveled were statistically significant for each of the seven independent components. This analysis showed how daily ticket costs affect the waterways transportation operations in Lagos State. The analysis shows how the low fees actually promote more traffic on Lagos State's waterways, raising the cost of upkeep and lengthening the time needed for servicing. It was recommended that efforts be made to reduce fare prices and provide suitable infrastructure at the ports in order to have efficient inland waterways operations that result in a complete reduction in operational expenses.

Application of energy scheduling algorithm based on energy consumption prediction in smart home energy scheduling

To effectively optimize household energy management, enhance user comfort, and ensure the stability of the power grid, this paper directs its focus towards household equipment and presents inter-household and indoor energy scheduling algorithms. Grounded in a two-dimensional dynamic device priority framework and predictive device operation modeling, this study demonstrates that the utilization of scheduling algorithms can avert grid overload scenarios during periods of low electricity prices. This results in more consistent hourly electricity consumption and a substantial peak-to-average ratio reduction of up to 44.86 %. Additionally, the application of the continuous wavelet neural network algorithm proves to be proficient in accurately forecasting home device operations. When the time was 17.5 h, the actual energy consumption of the water heater was 7.12 kWh, which was 0.78 kWh more than the predicted energy consumption. Utilizing scheduling algorithms, as opposed to not employing them, led to notable reductions in electricity expenses, demonstrating a cost savings rate of 15.32 % on the third day. Additionally, it facilitated decreased usage of the power grid. When the time was 18 o'clock, the power consumption of the grid using scheduling algorithms was 0.24 kWh, which was 0.25 kWh lower than that without scheduling algorithms. Following the implementation of the scheduling algorithm, minimal deviation in device startup times was observed during periods of high comfort, with offsets ranging from 0 h. The research methods employed demonstrate efficacy in orchestrating energy distribution among households and indoor spaces, thereby striking a harmonious balance between energy conservation and user comfort.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the LSTM paradigm of deep learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 80%, translating to an impressive 25% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities