Production Index Research Articles

Protective effect of Chlorella vulgaris on testicular damage, sperm parameters, androgen production, apoptosis and oxidative stress index in male rats following doxorubicin administration

Doxorubicin (DOX) is a chemotherapy agent associated with adverse effects on male reproductive health. Chlorella vulgaris (ChV) is a potent natural antioxidant with promising applications in maintaining health and preventing oxidative stress-related diseases. The present study aimed to investigate the protective effect of ChV on DOX-induced testicular toxicity. Twenty-five Wistar rats (230 ± 20 g) were randomly assigned to five groups (n = 5), including the control group, sham group (received normal saline by oral gavage daily and intraperitoneally (IP) once a week), DOX group (3 mg/kg; once a week; IP), ChV group (300 mg/kg/day; by oral gavage), and DOX (3 mg/kg; once a week; IP) + ChV (300 mg/kg/day; by oral gavage) group. After 8 weeks of treatment, the rats were euthanized and serum testosterone level, testes histomorphometry, gonadosomatic index (GSI), apoptotic gene expression, oxidative stress index, and sperm parameters were assessed. The results showed that DOX led to a significant decrease in histological indexes, testosterone level, GSI, sperm parameters, and Bcl-2 gene expression and increased expression of P-53 and Bax genes, and oxidative stress markers (P<0.05). The administration of ChV in the DOX+ChV group significantly improved testosterone levels, sperm parameters, testicular tissue apoptosis, antioxidant enzymes, and structural integrity of the testes (P<0.05). The findings suggest that the co-administration of ChV can be a promising therapeutic agent to reduce the adverse effects of DOX on male reproductive performance.

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

High-quality agricultural development in the central China: Empirical analysis based on the Dongting Lake area

High-quality agricultural development constitutes the primary point for forthcoming agricultural and rural endeavors. This study aimed to analyze the characteristics and obstacle factors of the high-quality agricultural development in central China, and the Dongting Lake area was taken as a case. Statistical data, land use data, and other data were adopted, and GIS spatial analysis and mapping technologies, obstacle model, and entropy weight method were used. The research results indicated that: 1) The comprehensive index has increased from 0.4290 in 2010 to 0.4717 in 2020. The change rates of the subsystem ranked from the highest to the lowest were as follows: cultural construction (36.25 %), ecological environment (15.18 %), social security (4.42 %), and material output (2.63 %). 2) The composite index has changed from higher in the west and lower in the east to a unconspicuous spatial difference. The material production index has shifted from higher in the north and lower in the south to a scattered distribution. The social security index has changed from higher in the west and lower in the east to higher in the northwest southeast and lower in the centre. The ecological environment index was higher in the west and lower in the east. The cultural construction index was higher in east and west and lower in the centre. 3) The top 4 obstacle factors were the high-quality development platforms, the agricultural driving force of per cultivated land, the number of agricultural geographical indication and the grain yield of per cultivated land. At the same time, the material output was the biggest obstacle. It is recommended to focus on the agricultural cultural resources in the eastern and western parts. This study can provide guidance and methodological references for similar studies, as well as scientific references for the sustainability of agriculture and optimization of related policies.

Effect of Natural Zeolite (Nz) of Growth Performance, Immunity Parameters and Gut Histology in Broiler Chicken

The spread of food-borne illnesses through poultry is a major issue in many countries, including Iraq. Bacteria can be present in poultry products if proper hygiene and sanitation protocols are not followed during production and processing. This research aimed to examine the impact of zeolite on performance, immunity parameters, and gut histology. A total 162 one-day old broiler chickens were randomly assigned to a control group and two natural zeolite-supplemented groups, each containing 54 birds (with three replicates/group and 18 chickens/replicate). The group (control) was without any additives, while the other two groups were fed diets containing 1% or 0.5% natural zeolite (NZ). The findings indicated that the groups receiving 1% and 0.5% natural zeolite (NZ) showed significantly (p&lt;0.05) higher BW and BWG compared to the group (control). Feed conversion ratio was affected by 0.5% natural zeolite (NZ) supplement compared to the control. There were no significant differences in production index (PI), spleen (%), bursa of Fabricius (%), total WBC, lymphocytes, heterophils and H/L ratio between the natural zeolite (NZ) groups and control. The research revealed that zeolite may have a beneficial effect on gut histology in broiler. Therefore, using zeolite as a feed supplement for poultry could improve performance

Performance evaluation of Indonesia's large and medium-sized industries using Data Envelopment Analysis method

Performance evaluation is essential for an industry's ability to operate successfully. Efficiency management is becoming crucial and important to enhancing the sustainability of the industry. The industry needs to measure its performance for the following reasons: (i) develop the economy and its operation efficiency in a sustainable way; (ii) supply data for decision-making units (DMUs); and (iii) optimize output using the fewest resources. Performance measurement indicators relate to the performance of the industry itself, such as: added value/cost of production factors, indirect taxes, number of workers, input costs, number of companies, added value/market prices, and production index. The purpose of this research is to measure the performance of large and medium-sized industries (LMIs) in Indonesia. LMIs have a strategic role as the main engine and driver of the economy. Measuring LMI performance is very necessary so that LMI can grow and develop sustainably. Data envelopment analysis (DEA) is a method for measuring performance. DEA is a non-parametric linear programming technique and used to determine comparisons between DMUs with different inputs and outputs. The research results indicate that there are three DMU classification categories, namely: Category 1 (ES = 1), Category 2 (ES = 0.9986-0.9998), and Category 3 (ES = 0.9971-0.9974). The percentages for each category are 50%, 37.5%, and 12.5%.

Economic Sentiment and Market Return of Indian Stock Market: An ARDL Approach

This study investigates the relationship between economic sentiment and the return of the BSE S&amp;P 500 Index over a decade, employing an autoregressive distributed lag (ARDL) model. Using 12 macroeconomic variables as proxies for economic sentiment, the analysis reveals a high degree of correlation between these proxies and market return. The findings demonstrate that market return is significantly influenced by its own lags, economic corporate premium (ECORPREM), foreign direct investment (FDI), gross domestic product (GDP), inflation rate (INFLAT), prime lending rate (PLR), and short-term interest rate (SHORTINT). Specifically, market return shows both positive and negative correlations with its various lags, while ECORPREM, FDI, GDP, and SHORTINT exhibit significant relationships with market return at different lag intervals. Additionally, INFLAT positively influences market return at specific lags, and PLR is positively correlated with market return in the current period. The study identifies that ECORPREM, FDI, GDP, INFLAT, PLR, and SHORTINT, with -values below 0.20, are statistically relevant in explaining market return. Conversely, variables such as exchange rate (EXRATE), foreign exchange reserves (FEXRES), index of industrial production (IIP), liquidity in the economy (LIQECO), oil prices (OILPRICE), and terms of trade premium (TERMSPRE) do not significantly impact market return, as indicated by their higher -values. These results provide valuable insights for retail investors, policymakers, and other stakeholders in refining their decision-making processes in the Indian stock market. The study also challenges the classical finance theory of investor rationality, suggesting avenues for further research in international contexts.

Analysis of Long-term Changes for Fisheries Production and Marine-Ecosystem Index in Jinhae Bay Considering Climate Change

Analysis of Long-term Changes for Fisheries Production and Marine-Ecosystem Index in Jinhae Bay Considering Climate Change

표준산업분류 기반의 공공데이터를 활용한 산업 시장규모 추정 방안에 관한 연구: 미래 모빌리티 산업을 중심으로

The advancement of information technology has increased the need for estimating the market size of new industries that did not previously exist. Companies require market size estimations to pursue new business ventures, but these emerging markets are not yet mature, making it difficult to define the boundaries of sub-industries and systematically establish industry-related data such as corporate sales and operating profits. Therefore, this study explores methods for estimating market size using public data. The economic indicator data, which are industrial statistics public data released by the National Statistical Office, include monthly, quarterly, and yearly industrial statistics. Specifically, these data encompass economic indicators such as the Mining and Manufacturing Production Index, the Mining and Manufacturing Shipment Index, the Domestic Supply Index, and the Value Added by Industry. This study analyzed whether market size can be estimated through standardized panel data analysis, focusing on the future mobility sector, and estimated the market size for 2021 and 2022 through statistical significance tests. The results of the study confirmed the existence of interrelationships among economic indicator data in the future mobility sector, enabling market size estimation. However, it was found that if the annual data for economic indicators are insufficient, the temporal correlations among indicators may not be reflected, leading to significant errors in long-term market size predictions.

DETERMINANTS OF CAPITAL STRUCTURE IN ENERGY SECTOR: EVIDENCE FROM BORSA İSTANBUL

This study aims to examine the determinants of capital structure in the energy sector of Turkey and their relation to relevant theories. The panel data analysis method was used to analyze annual data from 8 energy companies operating in Borsa Istanbul between 2014 and 2021. Results of the analysis showed that while the median debt ratio on a sector basis affects leverage positively; asset structure, growth opportunities, profitability, liquidity, industrial production and distribution index, and GDP annual growth variables negatively affect leverage. No significant relationship was found between the size of the business, non-debt tax shield, inflation, tax revenue, and leverage. It has been noted that the findings are in line with the previous studies, and the capital structure of the Turkish energy sector is generally compatible with the pecking-order theory.

Антикризові напрямки розвитку післявоєнного підприємництва в Україні

In the article, the authors researched the priority areas of development of post-war entrepreneurship in Ukraine. In order to assess entrepreneurial activity in the conditions of martial law, profitability indicators of enterprises were studied and their dynamics were determined. A special place in the research process is occupied by the analysis of registered legal entities and individual entrepreneurs. The analyzed indicators of the volume of industrial products sold and indexes of industrial products are quite significant and indicative in the assessment of business activity. State and donor programs, private initiatives of the EU to support entrepreneurs in war conditions are characterized in the work, and anti-crisis directions of entrepreneurship development are determined.

Examining the relationship between exports and economic growth in Türkiye: Fourier Toda-Yamamoto Granger causality test

This study aims to investigate the causal relationship between exports and economic growth in Türkiye from 2016:1 to 2023:12, focusing on the validity of the export-led growth hypothesis. The Fourier Toda-Yamamoto Granger Causality Test created by Nazlıoğlu, Gormus and Soytas (2016) was used with variables including total exports and industrial production index derived from monthly data. The data suggest a bidirectional causation between the two variables, indicating that each one is the cause of the other. The export-led growth hypothesis is confirmed in Türkiye for the relevant timeframes. The results of this study align closely with existing research on the topic compared to other studies, further suggesting that decreasing the reliance on imported intermediate items in Turkish export products will enhance the direct connection between exports and economic growth.

Methodology for estimating regional production capacity loss rate in industrial sectors caused by disasters: A case study of the 2016 Kumamoto earthquakes

The estimation of regional Production Capacity Loss Rate (PCLR) in industrial sectors after disasters is critical for disaster mitigation, resource allocation and restoration. Recent studies to estimate the PCLR in industrial sectors were based on hypothetical disaster survey data for resistance analysis and the linear regression model for recovery analysis, which may cause non-negligible bias. Moreover, these models did not incorporate the impact of lifeline disruption and restoration, thus further amplifying the estimation bias. To fill this gap, this study proposes a comprehensive regional PCLR estimation framework that incorporates the impacts of lifeline disruptions into resistance analysis and lifeline restoration into recovery analysis to improve the PCLR estimation accuracy. Specifically, this study utilizes lifeline resilience factors (LRFs) to quantify the impact of lifeline disruption on the production capacity rate immediately after the earthquake, and apply a semi-Markov recovery function to model the recovery rates considering the initial damage and lifeline availability influences. Then, the proposed methodology is applied to a case study of 2016 Kumamoto earthquakes to verify its effectiveness and accuracy. In this case study. Results shows that the non-manufacturing sector is more resistant than the manufacturing sector, yet lower recovery ability, ultimately resulting in greater production losses. The Mean Square Errors between the estimated PCLR and the official statistical of Kumamoto prefecture's Index of Industrial Production in each industry are less than 0.05, proves the validity of the proposed model and indicates the improved accuracy of loss estimation compared to previous studies. In addition, comparison results indicate improved accuracy of loss estimation compared to previous studies.

Understanding Investor Sentiment: Analyzing Its Influence on Stock and Cryptocurrency Markets During the Russia–Ukraine War

ABSTRACTThis paper examines the shifts in investor sentiment during the Russia–Ukraine war and its consequent impact on market volatility. By employing a comprehensive dataset that includes the S&amp;P 500 index, historical Bitcoin prices, the Investor Sentiment Index, the Industrial Production Index, and the US Consumer Price Index, this study applies several econometric models such as generalized autoregressive conditional heteroskedasticity (GARCH) models, regression analyses, vector error correction models (VECM), and the Granger causality model. The analysis spans from January 2021 to March 2023. The findings indicate that investor sentiment significantly influences returns in both stock and cryptocurrency markets, having a positive effect. These results underscore the importance for investors and policymakers to monitor investor sentiment during periods of conflict to understand its potential impact on financial markets. This research offers valuable insights that can guide investment decisions and inform policy interventions.

EFFECT OF CRYO-STORAGE ON STORABILITY OF PAPAYA SEEDS

A fruit crop of significant economic importance, papayas are often propagated from seeds. Based on their freezing injury even at low moisture content, papaya seeds are classified as "intermediate seeds"; and rapid loss of viability in storage is assumed to be the primary cause of poor germination; thus, the development of appropriate technology is necessary to prolong the storability of papaya seeds. One such effective technology for prolonged seed storability is cryo-storage. The purpose of the current study was to investigate how cryostorage affects papaya seed storability. For this investigation, TNAU Papaya variety CO 8 seeds were utilised. Five distinct treatments were applied to the seeds, which were then kept for approximately six months at liquid nitrogen's vapour phase (-140°C). The biochemical parameters such as protein content, free amino acid, total carbohydrates, free sugars, total lipids, and dehydrogenase activity were measured on a monthly basis along with seed germination and seedling vigour in the cryopreserved papaya seeds. Results indicated that papaya seeds that had been vitrified (seeds with a 10% moisture content that had been soaked in loading solution for 20 minutes, then immersed in Plant Vitrification Solution 2 (PVS2) for another 20 minutes, packed in cryovials and kept in cryo-storage at -140°C) compared to papaya seeds that were packed in cloth bags and stored in ambient conditions had higher seed germination, dry matter production and vigour index at six months after storage. In comparison to fresh seeds, the results also showed that, after six months of cryo-storage, biochemical proximate, protein content, total carbohydrate content, total lipid content, and dehydrogenase activity were maintained in the aforementioned treatment with minimum reduction, while electrical conductivity, free amino acid content, and free sugar content gradually increased with the storage period at minimum rate. In crux, vitrified papaya seeds packaged in cryovials and kept in cryo-storage at -140°C preserved their quality for six months, meeting Indian Minimum Seed Certification Standards. This could be useful for storing papaya seeds for longer periods of time. Keywords: Papaya, vitrification, differential scanning calorimetry, liquid nitrogen, cryo-storage

Potential Integration Of Machine Learning Algorithm And Manufacturing Execution System In The Lean Six Sigma Method To Improve Operational Excellence At ABC Farma Company

This research focuses on the implementation of Operational Excellence (OE) in the pharmaceutical industry, especially in companies that transformed from PT Askes (Persero) to the Health Insurance Organizing Agency (BPJS). One approach adopted is Lean Six Sigma (LSS) which is supported by the Manufacturing Execution System (MES) to improve the efficiency and quality of the production process. This research takes a case study on product A with a focus on improvements to hardness issues. Through the Define, Measure, Analyze, Improve, and Control (DMAIC) stages, traditional methods and advanced analysis based on Machine Learning (ML) algorithms are used to improve production processes. The results showed success in achieving significant improvements in the productivity index and quality of product A production processes, making a valuable contribution in the context of Operational Excellence research in the pharmaceutical industry. The LSS method, which has been modified by integrating with ML algorithms and MES, provides productivity aspects consisting of LT, PCE and TD, as well as quality aspects consisting of better Pp, Ppk and sigma levels. The modified LSS method also provides the potential for COGM savings of 2,66 billion Rupiah and reduces the risk of production process failure to less than 200 batches for every 1 million production batches. Based on this, it can be concluded that the development of the Lean Six Sigma method which has been integrated with ML and MES algorithms has the positive potential to increase OE at ABC Farma.

Analytical framework for industry-oriented leading cyclical indicators: Empirical investigation of EU economic cycles

AbstractThis research developed an analytical framework for industry-oriented leading cyclical indicators (CII), focusing on monitoring and forecasting economic cycles within the European Union (EU). Various methodologies for constructing these indicators were examined through an exhaustive sector analysis. A salient conclusion drawn is the non-feasibility of a one-size-fits-all composite leading indicator for all EU members. It underscores the imperative to tailor these indicators in congruence with the unique industrial characteristics of each country. The study provides empirical evidence that countries like Denmark, Germany, Austria, Estonia, Lithuania, Latvia, Finland and Sweden can benefit from high-caliber composite leading indicators tailored to their economies. Our analysis suggests that GDP is a more robust metric than the Industrial Production Index for predicting economic cycles for the EU countries.

Fitting data to a multiple structural measurement errors model

This paper proposes two new estimation methods to fit a multiple structural measurement error model when all variables are subject to errors. The new estimation methods were extensions of the Wald estimation method, one is the weighted grouping method, and the other is the iterative method. A Monte Carlo experiment is performed to investigate the performance of the new estimators compared with the classical estimation methods; the Maximum Likelihood Estimator and Method of Moment, in terms of root mean square error and its bias. The simulation outcomes demonstrated that the suggested estimators are more effective than conventional estimators. In addition, real data analysis is discussed to examine the relationship between the national gross domestic product, unemployment rate, and human development index after applying the two proposed estimation methods.

Transformation behavior of hazardous jarosite into recyclable hematite in a solution with high concentrations of K+ and Na+

Iron in the leaching solution with high K+ and Na+ concentrations was usually precipitated as the typical hazardous and toxic jarosite residues. However, this method of treatment has been greatly restricted by increasingly strict environmental regulations. Here we propose that iron can be precipitated from the solution with high K+ and Na+ concentrations as recyclable hematite products by adjusting the concentration ratio of sodium and potassium ions in the solution. The transformation behavior of jarosite into hematite in high concentration potassium ion and sodium ion solution was explained based on collision theory. The results indicated that in instances where the concentration ratio of Na+/K+ is ≥ 4:1, the iron present in the solution can be effectively precipitated as a recyclable hematite product, as opposed to forming the conventional hazardous jarosite residue, even under conditions where the potassium ion concentration reaches levels as high as 4 g/L. On the other hand, thermodynamic and molecular dynamics simulations indicate that at a temperature of 185 °C, the decomposition transformation of Na-jarosite (32.64 kJ and 7.25 eV) is more energetically advantageous compared to that of K-jarosite (61.07 kJ and 15.31 eV). The results were verified by the leaching solution from smelting industry. The iron content in the residues is above 58%, the sulfur content is below 4%, the zinc content is below 1%, and the total iron concentration in the supernatant is about 4 g/L, reaching the production index of the smelting industry. The green, environmentally friendly, and recyclable separation of iron in a solution with high concentrations of potassium and sodium ions is achieved, which is of great significance for the treatment of iron-containing solution and wastewater in the chemical industry and metallurgy fields.

Statewise performance of maize in India

The present study “Statewise performance of Maize in India” was undertaken to examine the growth pattern and instability of area, production, productivity and cost of cultivation. The data were collected on area, production, productivity and cost of cultivation of maize grown in India pertaining to the period from 2001-02 to 2020-21 (20 years) was spilt into sub-periods i.e. period I 2001-02 to 2010-11, period II: 2011-12 to 2020-21 and overall period 2001-02 to 2020-21. The results showed that, the compound growth rates for area were positive for all the maize growing states except Madhya Pradesh and Uttar Pradesh during period I, for the period II, five states showed negative growth rate except Tamil Nadu. All states are positive during overall period except Rajasthan and Uttar Pradesh. At overall level the compound growth rate of area of maize was highest in Maharashtra. The compound growth rate of production and productivity was highest in Tamil Nadu. The compound growth rate of cost of cultivation was positive for all the maize growing states. The highest coefficient of variation for area, production and productivity was observed in Maharashtra, Tamil Nadu respectively. Whereas, cost of cultivation of maize includes Cost A1, Cost B2 and Cost C2 was highest in Uttar Pradesh. The co-efficient of variation of cost of production was highest in Tamil Nadu. The highest Cuddy Della Valle instability index for area, production and productivity was observed in Andhra Pradesh, Madhya Pradesh and Tamil Nadu. Whereas, cost of cultivation of maize includes Cost A1, Cost B2, and Cost C2 was highest in Uttar Pradesh, Bihar and Rajasthan, respectively.

Impacts of Cropland Utilization Patterns on the Sustainable Use Efficiency of Cropland Based on the Human–Land Perspective

Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China’s major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources.