Inventory Reduction Research Articles

Can cross-border e-commerce development improve supply chain efficiency? Empirical evidence from China

ABSTRACT Cross-border e-commerce (CBEC) is becoming critical for a strong supply chain. Using data from Chinese listed firms and a difference-in-difference (DID) model, this paper examines the impact of CBEC development on supply chain efficiency from the perspective of enterprise inventory. CBEC significantly reduces enterprises’ non-finished goods inventory and improves supply chain efficiency. The mechanism tests reveal three ways in which CBEC optimizes enterprises’ inventory and enhances supply chain efficiency: improving the performance of upstream-facing sourcing, increasing the efficiency of enterprises’ product transformation, and optimizing downstream-facing demand coordination. The heterogeneous results suggest that CBEC has a greater impact on the supply chain efficiency of firms with high operational volatility and capital intensity and low digitization levels. Further, the effect of CBEC on the reduction of non-finished goods inventory is more significant in regions with weak infrastructure development and location endowment. This study provides empirical evidence for the emerging development topic of new international trade patterns empowering industrial supply chains.

An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management

Chronic tinnitus often affects elderly or hearing-impaired individuals, which can disturb their daily lives by disrupting concentration and limiting communication. Clinically, sound masking using external sounds like white noise (WN) aims to mask tinnitus and relieve secondary symptoms. Even when symptoms are relieved, tinnitus often requires long-term management, and for patients to visit healthcare professionals regularly. Generally, it could make maintaining symptom management challenging due to the time and effort required for consistent follow-ups. EEG is considered as one of the objective marker for assessing tinnitus symptoms. In this study, we designed IoT-enabled EEG sensing (IEES) headphones, an innovative IoT device that provided customized music (CM) and EEG measurement. The headphones employed a pitch-matching (PM) method to create CM tailored to each patient at specific frequencies for tinnitus patients. To collect EEG measurements, the device incorporated OpenBCI electrodes and a sensing chip to monitor brain waves and evaluate the outcomes.. After 30 days of experiment, participants showed significant reductions in both tinnitus handicap inventory (THI) scores and visual analog scale for annoyance (VAS-A) scores. In comparison, tinnitus frequency showed a slight reduction. EEG measurements demonstrated an increase in alpha band activity. In questionnaires, patients reported high satisfaction with their experiences. These findings highlight the potential of the proposed method for chronic tinnitus assessment and symptom management.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Determinants of Carbon Inventory and Systematic Innovation Methods to Analyze the Strategies of Carbon Reduction: An Empirical Study of Green Lean Management in Electroplating an Factory

This study explored the relationship between lean management, carbon inventory, and carbon reduction, along with the main factors affecting factory carbon inventory and carbon reduction, and proposed a set of carbon emission reduction strategies based on green value stream mapping. An electroplating factory and its production line in Taiwan was selected. Fourteen carbon inventory records, three work circle meetings, and green value flowcharts were utilized to collect and analyze data. Furthermore, this study applied the DMAIC framework and TRIZ to solve the issue of carbon emissions. The key factor affecting factory carbon inventory and carbon reduction was found to be electricity waste, which could be reduced through energy-saving methods. After analyzing the green value stream mapping, effective carbon emission reduction can be implemented in the manufacturing process. The company was found to gradually progress towards carbon reduction after investigating seven major green wastes. This study confirms that lean management can help organizations achieve their carbon emission reduction goals and is valuable in improving the organization’s environmental performance and competitiveness.

Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) has recently emerged as a novel treatment option for patients with major depressive disorder (MDD), but clinical observations reveal variability in patient's responses to rTMS. Therefore, it is clinically significant to investigate the baseline neuroimaging differences between patients with (Responder) and without (NonResponder) response to rTMS treatment and predict rTMS treatment outcomes based on baseline neuroimaging data. MethodBaseline resting-state EEG data and Beck Depression Inventory (BDI) were collected from 74 rTMS Responder, 43 NonResponder, and 47 matched healthy controls (HC). EEG microstate analysis was applied to analyze common and differential microstate characteristics of Responder and NonResponder. In addition, the microstate temporal parameters were sent to four machine learning models to classify Responder from NonResponder. ResultThere exists some common and differential EEG microstate characteristics for Responder and NonResponder. Specifically, compared to the HC group, both Responder and NonResponder exhibited a significant increase in the occurrence of microstate A. Only Responder showed an increase in the coverage of microstate A, occurrence of microstate D, transition probability (TP) from A to D, D to A, and C to A, and a decrease in the duration of microstates B and E, TP from A to B and C to B compared to HC. Only NonResponder exhibited a significant decrease in the duration of microstate D, TP from C to D, and an increase in the occurrence of microstate E, TP from C to E compared to HC. The primary differences between the Responder and NonResponder are that Responder had higher parameters for microstate D, TP from other microstates to D, and lower parameters for microstate E, TP from other microstates to E compared to NonResponder. Baseline parameters of microstate D showed significant correlation with Beck Depression Inventory (BDI) reduction rate. Additionally, these microstate features were sent to four machine learning models to predict rTMS treatment response and classification results indicate that an excellent predicting performance (accuracy = 97.35 %, precision = 96.31 %, recall = 100 %, F1 score = 98.06 %) was obtained when using AdaBoost model. These results suggest that baseline resting-state EEG microstate parameters could serve as robust indicators for predicting the effectiveness of rTMS treatment. ConclusionThis study reveals significant baseline EEG microstate differences between rTMS Responder, NonResponder, and healthy controls. Microstates D and E in baseline EEG can serve as potential biomarkers for predicting rTMS treatment outcomes in MDD patients. These findings may aid in identifying patients likely to respond to rTMS, optimizing treatment plans and reducing trial-and-error approaches in therapy selection.

Furloughing Employees with Uncertain Return? Management of Labor Frictions and Inventory Under Demand Shock

We assess how a retailer can manage inventories and labor under an extreme condition such as a temporary negative demand shock due to a pandemic or an economic turmoil. This analysis incorporates labor market frictions whereby firms incur deadweight costs associated with hiring and firing employees, as well as the option to furlough labor. We examine the impact of these frictions on a retail firm's optimal operating policies around inventory level, furlough, and layoffs. We find that labor market frictions condition the inventory and the level of employment in two ways. First, they lead to underinvestment in inventories, which limits recovery and employment in the postshock period. Second, high labor market frictions motivate the firm to conservatively downsize workforce during the negative demand period leading to higher employment, when compared to downsizing without friction. A significant contribution of our study lies in delineating the optimal furlough decisions and quantifying the impact of the furlough option on inventory and labor decisions. We demonstrate the conditions under which it is optimal for the retailer to either (i) fully downsize labor and leverage the furlough option in the labor market, or (ii) maintain excess labor while also opting to furlough a portion of the workforce. During an extreme event such as a temporary negative demand shock, our results highlight the need for a coordinated effort when implementing governmental subsidy policies on alleviating labor and inventory reductions by accounting for labor market frictions and furlough support.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Centrally-chosen versus user-selected swaps: How the selection of swapping stations impacts standby battery inventories

Swapping depleted batteries of electric vehicles promises much better driving-to-total-travel-time ratios than plug-in charging. Nonetheless, large-scale battery swapping systems have not successfully established yet. One obstacle, on top of the high infrastructure cost, is certainly the additional invest into extra standby batteries that await their swaps at stations. Existing research is focused on systems in which users decide individually where they want to swap batteries. This system requires significant standby battery inventories to protect against uncertain swapping demand. This paper evaluates another system where users must register their trips on a central platform, so that battery swaps can be coordinated based on central optimization results. To benchmark central optimization and user choice regarding their impact on standby battery inventories, we formulate the min-battery swapping problem: For a given set of vehicle trips, this optimization problem minimizes the number of standby batteries, distributes them in a given station network, and derives detailed swapping plans to feasibly power all trips. First, we present a thorough analysis of computational complexity. Then, we provide an efficient mixed-integer programming formulation that is adaptable to different swapping policies and (when fed into a default solver) solves instances with up to 200 trips to proven optimality in just a few seconds. Our computational study reveals that central optimization promises a significant reduction of standby battery inventories. This potential is shown to increase if swaps of not yet fully-charged batteries are allowed and swaps are pooled at a reduced number of swapping stations.

Food waste prevention and reduction: Practices, cultural and personal determinants

Food waste is a pressing global issue with profound social, environmental, and economic implications, prompting an urgent need for a comprehensive understanding of its sources. In the current study, we explored the role of cultural and personal determinants in food management practices. We operationalized two food practices – prevention of food waste by inventory and meal management and reduction of food waste by leftovers management, and explored how they are shaped by personal and cultural determinants. We asked 1200 respondents, three types of questions about their (1) cultural and personal attributes, such as their ethical and religious values; (2) lifestyle and dietary habits, such as their frequency of eating out, and (3) sociodemographic characteristics. We modeled the association between these three types of questions and the two food management practices utilizing a hierarchical multiple regression model. The results indicate that ascribing significant importance to hospitality is associated with less proficiency in inventory and meal management. Those who place a high priority on hospitality preparedness are proficient in managing leftovers but less proficient in inventory and meal management. In addition, individuals with strong ethical and religious views against food wastage tend to manage their inventory and meals poorly but excel at handling surplus food. Finally, secular Ashkenazi Israelis tend to engage in better practices to prevent and reduce food waste. The results also stress the differences between practices to prevent food waste and those to reduce it, opening a promising avenue for future research.

Atmospheric pCO2 Response to Stimulated Organic Carbon Export: Sensitivity Patterns and Timescales

AbstractThe ocean's organic carbon export is a key control on atmospheric pCO2 and stimulating this export could potentially mitigate climate change. We use a data‐constrained model to calculate the sensitivity of atmospheric pCO2 to local changes in export using an adjoint approach. A perpetual enhancement of the biological pump's export by 0.1 PgC/yr could achieve a roughly 1% reduction in pCO2 at average sensitivity. The sensitivity varies roughly 5‐fold across different ocean regions and is proportional to the difference between the mean sequestration time τseq of regenerated carbon and the response time τpre of performed carbon, which is the reduction in the preformed carbon inventory per unit increase in local export production. Air‐sea CO2 disequilibrium modulates the geographic pattern of τpre, causing particularly high sensitivities (2–3 times the global mean) in the Antarctic Divergence region of the Southern Ocean.

Materials requirement prediction challenges addressed through SDM and MEIO

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

Fuzzy Logic Optimizes Global Inventory Management

Inventory control is crucial for balancing customer demand and inventory levels, especially in make-to-order production systems like those in refrigeration manufacturing. This study addresses the challenges faced by a company producing sandwich panels, where inefficiencies in inventory processing led to overstock and outstock issues. By applying the fuzzy inventory control method using Python, the study aimed to optimize inventory levels. The results showed a 6% reduction in inventory, from 191,307 m² to 182,619.4627 m², demonstrating the method's effectiveness. This approach can improve inventory management in similar industrial settings, aligning production with demand and reducing excess inventory. Highlight: Efficiency Boost: Fuzzy logic optimizes inventory, minimizing overstock and outstock. Precise Management: Python aids accurate inventory analysis for informed decisions. Cost Savings: Aligning inventory with demand reduces excess, enhancing profitability. Keywoard: Inventory control, Fuzzy logic, Optimization, Production management, Industrial engineering

A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach

In light of the intense need for quality health and well-being, the healthcare industry must improve its operations by strengthening its service delivery and reducing overall costs. A smart inventory management system for managing medicines in a hospital supply chain (HSC) is one of the best solutions as its advantages ensure availability of affordable medicines as well as inventory cost reduction. Significant effort is made on inventory management from literature; however, smart inventory systems for HSC considering the various complexities and uniqueness of healthcare systems are limited. This paper attempts to fill this gap by developing a stochastic semi-Markov decision process model which is solved by a multi-agent reinforcement learning method. Data collected from a multispecialty hospital in India’s eastern region serves to validate the proposed model. The results present the optimal order quantities and optimal inventory control policy for the HSC considering the uncertain medication demand at the hospital pharmacy and multiple point-of-care units, and demand dependencies among medicines prescribed to the patients admitted in the hospital. The study also has significant managerial implications for enhancing HSC’s inventory performance, improving the healthcare service delivery, reducing healthcare cost, thereby ensuring the affordable, accessible, and quality healthcare to the society.

Implementation of ABC - XYZ Analysis on Business Inventory Management using Machine Learning Model Technology By IJISRT

This paper presents a novel approach to optimize business inventory management through the integration of ABC-XYZ analysis with advanced machine learning models. Inventory management plays a critical role in the operational efficiency and profitability of businesses across various industries. Traditional methods such as ABC analysis and XYZ analysis have been widely used to classify inventory items based on their importance and demand variability. However, the effectiveness of these methods can be further enhanced by leveraging machine learning techniques to analyze historical data and make accurate predictions. In this study, we propose a framework that combines ABC-XYZ analysis with machine learning algorithms to classify inventory items and optimize inventory control policies. We demonstrate the effectiveness of our approach through a case study conducted on a real-world business dataset, highlighting significant improvements in inventory turnover, cost reduction, and customer satisfaction.

Does lean inventory management alleviate financing constraints?

PurposeThis study explores the intricate relationship between inventory leanness, financial constraints and digital transformation in listed Chinese manufacturing firms.Design/methodology/approachUsing a large panel data collected from 2,563 Chinese listed manufacturing enterprises over the period from 2012 to 2021, this research employs the instrumental variable method combined with two-stage least squares estimators to explore the U- shaped relationship between inventory leanness and financial constraints. Furthermore, the moderating role of digital transformation is demonstrated.FindingsContrary to traditional assumptions, our research uncovers a U-shaped relationship between inventory leanness and financial constraints, indicating that excessive inventory reduction can exacerbate financial constraints. Digital transformation plays a significant moderating role, particularly in highly digitalized environments.Practical implicationsOur findings have practical significance for top managers and policymakers. We advocate for a balanced approach to lean inventory management to mitigating financial constraints. The study emphasizes the pivotal role of digital transformation in alleviating the impact of inventory leanness on financial constraints, highlighting the need for digital transformation strategies.Originality/valueThis research provides a comprehensive analysis of inventory leanness, financial constraints and digital transformation dynamics. It challenges conventional thinking by revealing the nonlinear nature of the inventory leanness–financial constraints relationship. The concept of moderation highlights the moderating effect of digital transformation. This study offers practical guidance for practitioners and policymakers.

Dynamic models for developing reference scenarios of energy system in the low-carbon transition

The proposed study is aimed at the development of dynamic input-output models in relation to energy development systems. The main task is to find optimal scenarios, defined as sequences of optimal balances between production and capital investment. These models are rooted in Leontief's conceptual concept of input-output, which emphasizes the timing of the introduction of new production systems. The article discusses the economic interpretation and mathematical conditions of intersectoral models, and examines their application to problems related to energy. The model uses the concept of balanced equilibrium growth, in which the ratio of consumption to net release is the same for all resources, and the reserve of at least one resource is fully utilized. The growth rate of the system is the most important parameter that determines the trajectory of balanced growth. Solving the system of inequalities yields balanced growth trajectories in which the dominant root and the associated vector of characteristics plays a key role. A balanced equilibrium growth rate has the properties of a minimax with the presence of a saddle point, which indicates its importance in achieving market equilibrium while preventing a reduction in inventories. The practical application of the model is described. The results of calculations of pessimistic, reference and optimistic scenarios of electricity production in the IPS of Ukraine are presented. Official statistical information and economic forecasts are used, and various restrictions are taken into account. The model aims to minimize the total cost of electricity generation while respecting environmental and operational constraints. The proposed model provides a comprehensive basis for understanding the dynamics of resource reserves, consumption and growth of electricity production of the IPS of Ukraine. It offers decision-makers a valuable tool to optimize electricity generation strategies, taking into account a range of scenarios and constraints, for the sustainable and cost-effective operation of the energy system. Keywords: Dynamic input-output models, optimal scenarios, balanced equilibrium growth, growth rate of the system.

Penerapan Lean Manufacturing Menggunakan Value Stream Mapping pada VCT Rotor di PT. XYZ

XYZ is an automotive component manufacturing company with two lines of production: the Shock Absorber Line and the VCT Line. One of the products on the VCT Line is the VCT rotor, with issues surrounding waste. Utilizing Lean Manufacturing approaches using the Waste Assessment Model and the Value Stream Analysis Tools is an effective way to solve issues and enhance efficiency in the VCT rotor system and production process at PT. XYZ. Total time savings as a result of the process change is 9,108.25 seconds, and the total lead time reduction in inventory is 34 days. The highest type of waste found in the VCT rotor is inventory, which accounts for 23,29% of the total waste. Overproduction contributes to 23,23% of the waste, while defect is responsible for 19,04%.

Adapting lean manufacturing principles to the footwear industry: Kaizen, Kanban or a Mix?

This study presents the overview of Kaizen and Kanban implementation, as lean principles and highlights the achievements realised from deployment of the integrated initiatives for sustainable performance of organisations. Lean manufacturing includes various principles and techniques, with the same main goal: to banish waste and non-value-added activities at every production or service process and enhance customer satisfaction. To stay competitive, many Indian footwear manufacturers have sought to optimise their manufacturing processes to readily compete with overseas manufacturers. Therefore, this work highlights the barriers, pathways and potential innovations for sustainable Kaizen and Kanban implementation in organisations. It further brings out the integrated concept of Kaizen and Kanban, and the requirements for its holistic implementation. Methods such as process observations, interviews, plant tours and case studies were used for the investigation. The significant contributions through Kaizen and Kanban initiatives in the organisation, including just-in-time improvements, quality, inventory reduction, optimisation, teamwork and flexibility for sustained organisational development are elucidated and juxtaposed with existing literature. It pivots the approach advised by the various researchers, practitioners and censoriously appraises the desire for Kaizen and Kanban programmes in the organisation. Significantly, this study is valuable to researchers, project and quality managers, development professionals and operations managers in various organisations, especially the footwear industry.

The impact of videolaparoscopic surgery in the treatment of endometriosis on depression levels.

The aim of the study was to evaluate the impact of laparoscopic surgical treatment of endometriosis on the levels of health-related depression in patients using a validated questionnaire. A prospective study was carried out between September 2020 and May 2022 in a private hospital (São Luís, Maranhão, Brazil), which analysed depression using the Beck Depression Inventory-II, on 103 patients undergoing surgical treatment for endometriosis, evaluated preoperatively and 3 and 6 months after the procedure. Patients with unsuccessful clinical treatment for endometriosis and pain level ≥7 on Visual Analog Scale and who agreed to participate in the study were included. Demographic data were acquired by consulting medical records. The average age of the participants was 36±6.3 years; the majority of patients were brown (68.6%), married (66.6%), overweight (55.8%), had had hormonal treatments with progestogens (50.9%), low fertility (50.9%), severe endometriosis (39.3%), endometriosis surgery+myomectomy (29.4%) and one (1%) patient withdrew from the study. There was a statistically significant reduction in mean Beck Depression Inventory between the preoperative period and 6 months after surgery (p<0.0001). Surgical treatment of endometriosis appears to have a positive impact on the symptoms of depression in the patients evaluated.