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A Review of Recent Advances in Laser-Based Medical Imaging Techniques
No full textThis review explores the recent advancements in laser-based medical imaging techniques, highlighting their significant contributions to modern diagnostic practices. Laser technologies, including fluorescence imaging, photoacoustic imaging, and femtosecond lasers, have revolutionized non-invasive medical imaging by offering high-resolution and precise visualization of biological structures and processes. These techniques not only enhance early disease detection, particularly cancers, but also support real-time guidance during surgeries, improving patient outcomes. Furthermore, the integration of artificial intelligence (AI) has further optimized diagnostic accuracy and analysis efficiency. Despite these advancements, challenges such as high equipment costs, the need for specialized training, and a lack of standardized protocols still hinder widespread adoption in clinical settings. This review discusses the ongoing innovations in laser-based imaging, the ethical considerations surrounding AI integration, and the potential for future developments, emphasizing the importance of continued research to maximize the benefits of these technologies for patient care
Effects of Nutrient Management on Growth, Agronomic Efficiency, and Economic Yield of Barley in Kandahar, Afghanistan
Get PDFBarley (Hordeum vulgare) is a major grain crop in the world, and Afghanistan. Indigenous nutrients are frequently the most limiting factors for crop output in the world's major agricultural areas, therefore good fertilizer use tactics generally result in significant financial gains for farmers. A field experiment was carried out at the research farm of Afghanistan National Agricultural Science & Technology University (ANASTU) Kandahar, Afghanistan. The experiment consisted of two barley varieties viz., Takhar Barley 013and Darulaman Barley 013 combined with 6 indigenous nutrient supply treatments. The set of treatment combinations were replicated three times in a factorial randomized block design. Among indigenous nutrient supply, agronomic use efficiency (AUE) of N (12.88 kg kg-N-1), P (25.75 kg kg-P-1), K (40.0 kg kg- K2O -1) and Zn (367.9 kg kg-Zn-1), PFP of N (29.2 kg kg-N-1), P (58.3 kg kg-P-1), K (116.7 kg kg- K2O -1) and Zn (833.4 kg kg-Zn-1), gross returns (109085.4 AFN ha-1) and net returns (50089.5 AFN ha-1) were significantly higher with application of recommended rates of fertilizer application (NPKZn) as compared to omission of nutrients. Whereas, N omitted plots recorded significantly, whereas AUE over other nutrient omitted plots. Therefore, Takhar Barley 013 genotype along with recommended rate of fertilizers was found more productive and economically remunerative for cultivation in Kandahar, Afghanistan
Farm-Level Economic Assessment of Watermelon Production in Nangarhar Province
Get PDFWatermelon is a significant cash crop that provides a good source of income and contributes to employment generation in Nangarhar province. This research aims to analyze the socio-economic characteristics and cost and return of watermelon. This study was carried out in Nangarhar province. The research is quantitative in nature, utilizing both primary and secondary data A purposive sampling design was used to select a sample of 130 respondents for the survey. Primary data were collected through a well-structured questionnaire. Descriptive statistics and cost and return analysis were used for data analysis. The results revealed that the average age of the selected farms was 39 years, and the percentage of respondents was educated, which fortunately included a percentage of bachelor's degree holders. The majority of the respondents, 59.76 percent, are living in households. At the same time, 42.62 percent of the farmers had between 5 and 10 years of watermelon production experience, with an average of 7 years. Furthermore, approximately 78 percent of respondents have annual incomes exceeding $100,000. The cost and return analysis showed that the average per jerib cost for large-scale farmers (17,274 AF) is lower than that for medium-scale farmers (18,057 AF) and small-scale farmers (19,307 AF). Moreover, fertilizer costs constitute the highest proportion of total production costs across all categories. Based on per-jerib costs, the expenses associated with the Black Master variety amount to 22,505 AF, which is approximately 6,000 AF higher than the Extreme F1 variety. The average per-jerib yield in Nangarhar province is 855 man (7 Kg), generating a gross income of 40,117 AF and a net income of 21,904 AF. The study recommended the extreme F1 variety as it is more profitable in the study area
The COVID-19 lockdown impact on air quality: A Case Study of Two Districts in Kabul City, Afghanistan
Get PDFThe COVID-19 pandemic spread rapidly worldwide, causing millions of deaths. To curb transmission, many countries imposed lockdowns, leading to a noticeable improvement in air quality. During the peak of the pandemic, Kabul, Afghanistan’s capital and one of the world’s most polluted cities, enforced a three-month lockdown. This study evaluates the impact of the lockdown on Kabul’s air quality by analyzing variations in pollutant concentrations (PM₂.₅, PM₁₀, CO, NO₂, SO₂, and O₃) across two districts before and during the lockdown. Air quality data from the National Environmental Protection Agency was evaluated using a paired-samples t-test. The results revealed a significant decline in pollution levels. Pre-lockdown, Kabul’s air quality was severely degraded, with PM₂.₅ (334–362 µg/m³) and PM₁₀ (491–518 µg/m³) exceeding standards by 4.5× and 3×, respectively, while NO₂ peaked at 565 µg/m³ (7× the limit). During Lockdown, concentrations of PM₂.₅, PM₁₀, NO₂, and SO₂ dropped, nearing permissible levels. This suggests that reduced human activities lead to cleaner air. These findings highlight the benefits of strategic emission controls, offering policymakers actionable insights for improving Kabul’s air quality
Impact of Changes in Agriculture Import Tariffs on Afghanistan's Food Availability and Macroeconomic Factors
Get PDFAfghanistan grappled with a severe food insecurity crisis, with two out of every five individuals experiencing acute food insecurity. The country heavily relied on imports, with cereals accounting for 66 percent of imports and wheat accounting for 73 percent of calories. This study scrutinized the influence of changing agricultural import tariffs on macroeconomic variables and food availability. The short-term impact of agricultural import tariffs was evaluated using GAMS software, utilizing a computable general equilibrium model and a social accounting matrix. Four diverse scenarios were investigated, focusing on changes in import tariffs for various agricultural products. The findings revealed that lower tariffs resulted in augmented imports and declined domestic output, whereas the opposite trend occurred with tariff upsurges. Scenario C analysed the effect of a 50 percent change on the agricultural import tariff rate of 6.12 percent. Wheat imports increased nearly 1 percent with a decline in tariffs, while they diminished by almost 22 percent with a surge in tariffs. All agricultural categories were affected, except for opium and forestry. The study volumes, lower labor and capital sharing rates, higher supply prices, and lower domestic output. To ensure food security, the government needed to advocate for scenarios that reduced tariffs, particularly through a comprehensive liberalization policy
Investigation of Factors Affecting Surface Water Change Using Satellite Data and Remote Sensing Techniques (A Case Study: BAND-E-AMIR National Park, Afghanistan)
Get PDFThis study employs multi-sensor remote sensing technology to analyze the spatiotemporal dynamics of surface water area in Band-e-Amir National Park, Afghanistan. In a context of global water scarcity and a critical lack of in-situ hydrological data, satellite imagery from Sentinel-2, JRC, and Global Surface Water datasets was processed using GIS and the Automated Water Extraction Index (AWEI) to quantify surface water extent. The Mann-Kendall statistical test was applied to determine significant trends. Contrary to declining water trajectories observed in other Afghan regions, the analysis reveals a significant increase in surface water area within the park from 2000 to 2020. Interannual variability is evident, with the surface water area measured at 708.7 hectares in 2016 and 636.6 hectares in 2023. Correlation with climatic drivers indicates that this positive trend is primarily attributable to a significant increase in precipitation, as derived from GPM satellite data.In contrast, land surface temperature data from MODIS-Terra showed no statistically significant trend, ruling it out as a major contributing factor. The findings demonstrate the critical utility of remote sensing for environmental monitoring in data-scarce regions and highlight localized hydrological resilience to climate change in Band-e-Amir, driven predominantly by precipitation patterns. This research provides a foundational assessment essential for the park's future conservation and water resource management strategies
Evaluation of Raw Milk Quality Using the Methylene Blue Reduction Test
Get PDFThe microbiological quality of raw milk remains a significant public health concern, especially in regions where unpasteurized milk is commonly consumed. Although raw milk hygiene has been studied previously, detailed assessments of bacterial load using the Methylene Blue Reduction Test (MBRT) under the specific environmental and hygienic conditions of Kabul, Afghanistan, are lacking. This study aimed to evaluate bacterial contamination in raw milk from the 13th district of Kabul city. A total of 52 milk samples were randomly collected from four locations—Qala-e-Naw, Pul-e-Khesk, Tank-e-Tel, and Qala-e-Qazi—and transported under strict hygienic conditions to the Food Technology and Hygiene Laboratory at the Faculty of Veterinary Science. Bacterial load was determined by the time required for methylene blue decolourization, which reflects microbial metabolic activity. Descriptive statistics were used to calculate contamination percentages and compare mean decolourization times to classify milk quality. Results revealed contamination rates of 42.33%, 30.76%, 15.30%, and 11.53% in Qala-e-Naw, Pul-e-Kheshk, Tank-e-Tel, and Qala-e-Qazi, respectively, with Qala-e-Naw exhibiting the highest contamination. These findings indicate substantial microbial risks in raw milk and underscore the need for improved milking hygiene, proper handling practices, and public education to ensure food safety and protect consumer health in the region
Study of The Magneto-Optical Kerr Effect in Thick and Ultrathin Composite Layers
Get PDFWhen a transparent isotropic material is subjected to an electric field, birefringence occurs, and the material acquires the properties of a uniaxial crystal. The difference in refractive indices parallel and perpendicular to the field is proportional to the square of the field intensity and the Kerr constant. The importance of this topic lies in investigating how magnetic fields affect the optical properties of thick and ultrathin composite layers, which are crucial for enhancing the performance and design of magneto-optical memories and advanced sensors. The goal of this research is to determine the exact relationships between the thickness of composite layers and their magneto-optical sensitivity to optimize composite materials. Also, the angle of polarization rotation of light due to the Kerr effect is quantitatively measured and optimized to achieve maximum magneto-optical response. In this research, simplified analytical expressions for the magneto-optical Kerr effect in composite layers are presented, and the MOKE formulas for and layers are investigated, accounting for the second-order nonlinear refractive index. , the time rotation constant , and the phase difference of the incoming light. The results show that the longitudinal and polar Kerr rotation angles in thick and ultra-thin layers exhibit a systematic dependence on the angle of incidence and agree well with theoretical calculations at specific angles. This research shows that by combining materials and controlling the thickness of the composite layers, the polarization rotation angle due to the Kerr effect can be significantly improved
A Comparative Study of the GM(1,1) Model and Curve Fitting Method for Forecasting Viral Hepatitis Incidence in Afghanistan up to 2030
Get PDFThis study aimed to forecast the incidence of viral hepatitis in Afghanistan using the GM(1,1) model and curve fitting methods, and to compare the predictive performance of both approaches using mean absolute error (MAE) and mean absolute percentage error (MAPE). Annual incidence data were obtained from the Hospital of Infectious Diseases through a formal request. Linear and nonlinear regression techniques, along with the first-order univariate grey prediction model (GM(1,1)), were applied to model historical trends. The model with superior predictive accuracy was used to project viral hepatitis incidence for 2015–2030. Both GM(1,1) and curve-fitting models accurately captured the incidence trends; however, GM(1,1) demonstrated superior performance (MAE = 557.95; MAPE = 6.76%) compared with exponential curve regression (MAE = 558.30; MAPE = 7.07%). Forecasts indicated 15,328.6 cases in 2025 and 39058 cases in 2030, with both models projecting a consistent upward trend, reflecting a growing public health burden. The projections highlight a growing public health burden of viral hepatitis in Afghanistan, emphasizing the urgency of effective prevention and vaccination programs. The findings can guide policymakers in resource allocation and healthcare planning, while also informing strategies to strengthen surveillance and early detection. Moreover, the study demonstrates that the GM(1,1) model is a reliable forecasting tool in contexts with limited or incomplete data, providing valuable support for evidence-based decision-making in public health. This study is the first to compare GM(1,1) and curve fitting for forecasting viral hepatitis in Afghanistan, using viral hepatitis data from Afghanistan. It provides context-specific projections through 2030 and demonstrates that GM(1,1) is a reliable tool in data-limited settings
Modelling and Forecasting Wholesale Potato Prices in Northern India Using SARIMA
Get PDFFarmers face many difficulties as a result of price fluctuation in agricultural commodities, mostly in developing nations like India. Potato prices are particularly unstable during the post-harvest period, which frequently forces farmers to sell at low prices because of urgent financial needs and delayed market information. The objective of this study is to use the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast monthly wholesale potato prices. Three important markets in Northern India i.e. Uttar Pradesh, a significant producer, Punjab, a distribution hub, and Delhi, a major consumption center were studied for price trends. The AGMARKNET portal was used to collect monthly wholesale price data from January 2010 to December 2024. The best fitted SARIMA models were determined using the lowest AIC and BIC values: SARIMA(2,0,0)(2,0,1)[12] for Uttar Pradesh, SARIMA(1,0,1)(1,1,1)[12] for Punjab, and SARIMA(1,0,1)(0,1,1)[12] for Delhi. Forecast results reveal clear seasonal patterns. Prices in Uttar Pradesh are expected to decline from Rs. 1986.61 in January to a low of Rs. 1629.92 in April, before rising again to Rs. 1821.96 in July. Similarly, the lowest forecasted prices are observed in March and April in Punjab (Rs. 1546.22) and Delhi (Rs. 1664.95), while the highest price is projected for October in Delhi (Rs. 2039.61). The observed patterns suggest that the post-harvest months, specifically from February to April, typically see a decline in prices attributed to market saturation. Conversely, prices tend to increase during the mid to late year period, likely influenced by a decrease in fresh arrivals and a heightened dependence on stored produce. The forecast emphasizes the importance of market-specific dynamics and illustrates the effectiveness of predictive models in assisting farmers marketing decisions. This enables improved planning by traders and policymakers to address seasonal price volatility.