The production of Sato using black glutinous rice offers a valuable opportunity to enhance its overall quality by leveraging the unique nutritional profile, bioactive compounds, and distinct sensory attributes of pigmented rice varieties. This study aims to develop an effective Loog-pang starter to produce black glutinous rice Sato. Yeast and fungal strains were isolated from Loog-pang Sato and Loog-pang Khao Mak samples collected from Roi Et and Yasothon provinces in Thailand. Fungal strains were selected based on their amylase production on soluble starch agar, while yeast strains were screened for their fermentation performance based on TDS reduction, ethanol production, and alcohol tolerance. The study successfully isolated eight fungal strains and 14 yeast strains. Fungal isolates FSA1 and FSA2 exhibited the highest amylase activity on starch agar after iodine treatment. Yeast isolate YKB1 demonstrated the highest alcohol tolerance when cultured in liquid media containing 10% and 15% alcohol. For Sato fermentation, this study utilized a ratio of fungal strain (FSA1 or FSA2) to yeast strain (YKB1). The highest alcohol production, at 16%, was achieved using Loog-pang made with the combination of FSA1 and YKB1. The yeast DNA sequencing analysis of YKB1 revealed a 95.57% similarity to Issatchenkia orientalis. The findings from this study present significant potential for enhancing Sato production by isolating yeast and fungi, enabling local producers to scale up production, improve production, and expand markets both locally and internationally.
Porang tuber is one of the commodities containing high glucomannan, which increases the body's immunity and reduces cholesterol, blood sugar, and body weight. However, porang tubers contain calcium oxalate, which hurts the human body. The study aimed to define the effect of soaking ratio, solvent concentration, and processing time on the purification technique to obtain high-purity flour and determine the best characteristics of porang flour. The porang tubers were extracted using the wet method, which consisted of the immersion ratio of adding sodium metabisulfite (Na2S2O5) and sodium chloride (NaCl) with ethanol solvent at different extraction times. The research resulted in the treatment significantly affecting all observed parameters except fat content in porang glucomannan flour. In addition, the best characteristics were obtained from the treatment of a 1:3 soaking ratio, 96% ethanol concentration, and 120 min of extraction time, which showed a flour whiteness value of 99.38, oxalate of 0.10%, viscosity of 52,758.5 cP, and glucomannan content of 88% on dry basis. Considering the results of all parameters mentioned above, treating a 1:3 soaking ratio, 96% ethanol concentration, and 120 min of extraction time improves the yield and quality of glucomannan flour by decreasing calcium oxalate content by 0.37% from 0.47% to 0.10%. Porang flour with these characteristics is suitable as a thickener or a source of thickening food additives.
Wastewater discharge containing organic dyes may pose a hazard to the environment, which necessitates that dye removal must occur prior to wastewater release into water bodies. Herein, copper oxide nanoparticles (CuO NPs) were prepared by a green precipitation method to enable decolorization of a cationic dye (methyl violet; MV) from aqueous media. Complementary tools were employed to characterize the CuO NPs adsorbent: spectroscopy (FTIR and UV-VIS), microscopy (FESEM and TEM), XRD, BET surface area analysis, and point of zero charge (pHPZC) via potentiometry. The FTIR bands at 722, 663, 569, and 465 cm-1 correspond to the vibrational modes of CuO NPs, along with the optical absorbance band at 275 nm that supports the formation of CuO NPs. The XRD and TEM analyses predicted single-phase CuO NPs with a monoclinic framework. BET was employed to assess the textural characteristics and accounted for the specific surface area (12.97 m2·g-1). Batch adsorption studies were carried out to assess the role of initial pH (3.58-10.53), CuO NPs dose (0.02-0.25 g/L), initial MV concentration (20-140 mg/L), contact time (5-90 min), and temperature (298, 308, and 318 K) on the dye removal efficiency. The adsorption capacity of CuO NPs for MV was determined to be 5.06 mg/g at 45°C. The pseudo-second-order (PSO) model described kinetic isotherms, and equilibrium adsorption data were adequately fitted by the Freundlich model. Thermodynamic results revealed that adsorption was spontaneous, endothermic, and entropy driven at the solid-liquid interface. The CuO NPs further displayed good reusability with high efficiency for six successive cycles of adsorption-desorption using 0.1 M HCl as a desorbing agent. These findings validate the efficacy of CuO NPs as a green and effective adsorbent for wastewater treatment processes for cationic dye removal.
Climate change poses profound global challenges, especially for agriculture and food security in developing countries. This study investigates the impact of climate change on household food security and assesses the effectiveness of farm-level adaptation strategies in mitigating its impacts in the Enebse Sar Midir District of the East Gojjam Zone, Ethiopia. Data were collected through a household survey of 184 rural households using structured questionnaires and analyzed using SPSS Version 26. The findings reveal that 85.9% of respondents observed changes in temperature, while 90.2% noted altered rainfall patterns. The key climate-related challenges affecting food security included drought (79.3%), erratic rainfall, and flooding. Household food security was assessed using indicators such as the months of adequate household food provisioning (MAHFP), Household Food Insecurity Access Scale (HFIAS), and household dietary diversity score (HDDS). The study showed that 33.7%, 42.9%, and 32.1% of households were food secure according to MAHFP, HFIAS, and HDDS, respectively, while the majority remained food insecure. Binary logit regression analysis revealed nine significant determinants of household food security, including age, family size, educational level, livestock ownership, and rainfall variability (p < 0.05 and p < 0.1). Moreover, 82.1% of households adopted climate adaptation strategies, such as soil and water conservation, modified planting time, and improved crop management practices. This result points out the critical need to strengthen household-level adaptation strategies and improve access to climate information to improve food security in drought-prone rural areas of Ethiopia.
Salacca zalacca (snake fruit) is rich in antioxidants, polyphenols, organic acids, and vitamin C. This study aimed to evaluate the effectiveness of body massage oil containing snake fruit extract, in conjunction with traditional Thai massage (TTM), on skin quality in healthy individuals. Seventy-one participants aged 18-35 years were randomly assigned to one of three groups: (1) control group (n = 23) receiving TTM without oil; (2) Treatment-1 group (n = 23) receiving TTM with pure coconut oil; and (3) Treatment-2 group (n = 25) receiving TTM with snake fruit extract-infused oil. All participants received 60-min massages once weekly for 12 weeks. Skin parameters including elasticity, moisture, melanin, and oiliness were assessed at the neck, back, arm, and leg regions. After 12 weeks, skin elasticity significantly improved at all assessed regions in all groups (p < 0.001), with no significant between-group differences. Skin melanin levels significantly decreased at the back and leg regions across all groups (p < 0.05), with no between-group differences observed. Skin moisture significantly increased at the leg region only in the Treatment-2 group (p = 0.003). Skin oiliness significantly increased at all measured regions in both oil-based groups (Treatment-1 and Treatment-2) (p < 0.05) and was significantly higher than in the control group (p < 0.05), except at the back region in the Treatment-2 group. Massage oil containing snake fruit extract demonstrated specific benefits in enhancing skin oiliness and localized moisture. However, it did not confer overall superiority over conventional coconut oil, while improvements in elasticity and melanin appeared to be primarily attributable to the massage technique itself. ClinicalTrials.gov Identifier: NCT06227260.
Agrochemical usage is common with cocoa farmers in Ghana; however, the inappropriate use or lack of protective equipment leaves farmers exposed to these chemicals. Pesticides are generally taken up by farmers through inhalation, ingestion, or dermally and distributed through the circulatory system to affect various organs. Organophosphate inhibits cholinesterase (ChE), causing a buildup of acetylcholine and overstimulation of cholinergic synapses. Plasma pseudocholinesterase (pChE), synthesized in the liver, serves as a biomarker for organophosphate exposure. We assessed the levels of serum cholinesterase and liver and kidney function biomarkers among cocoa farmers exposed to organophosphates. A total of 220 male farmers with a consistent track record of using agropesticides for at least 1 year or more were selected. A structured questionnaire was used to gather sociodemographic information. Following an overnight fast, 5 mL of blood was collected from each participant aseptically for assessment of biochemical markers of the liver, kidney, lipids, and cholinesterase. Reduced plasma pseudocholinesterase was defined as levels < 5000 U/L and estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration equation. The prevalence of reduced pseudocholinesterase (pChE) among the study participants was 23%. The mean level of aspartate aminotransferase (AST) (38.11 ± 14.15 vs. 35.72 ± 16.61, p = 0.036) and alanine aminotransferase (ALT) (29.04 ± 18.85 vs. 23.69 ± 11.09, p = 0.017) were significantly elevated among subjects with low serum cholinesterase levels. Triglycerides (TG) (1.68 ± 0.86 vs. 1.30 ± 0.66, p = 0.004) and very low-density lipoprotein (VLDL) (0.64 ± 0.35 vs. 0.77 ± 0.40, p = 0.004) were significantly elevated among the participants with low pChE levels. However, mean high-density lipoprotein (HDL) (1.22 ± 0.44 vs. 1.37 ± 0.35, p = 0.017) was significantly reduced among participants with low pChE levels. Coffee consumption OR = 2.24 [1.04-4.83, p = 0.039], duration of agro pesticide usage greater than 10 years OR = 4.70 [1.72-13.5, p = 0.003], and poor knowledge of the harmful effect of pesticides OR = 4.96 [1.97-14.1, p = 0.001] were all significantly associated with low pChE levels among the study participants. pChE levels showed a significantly negative correlation with ALT (R = -0.2, p = 0.0027), TG (R = -0.34, p ≤ 0.001), and VLDL (R = -0.31, p ≤ 0.001). HDL showed a significant positive correlation (R = 0.14, p = 0.044). There is a high prevalence of reduced pChE among cocoa farmers in Ghana and this was associated with alteration in liver and lipid biomarkers. Additionally, coffee intake, longer work duration, and poor knowledge of agropesticide side effects were associated with low pChE levels among the study participants. These findings highlight the importance of targeted occupational health interventions, including improved training on pesticide safety and consistent and proper use of personal protective equipment (PPE).
Hikikomori, a severe form of social withdrawal, has been predominantly studied in East Asia but remains underexplored in Middle Eastern contexts. As societal and cultural factors influence its manifestation, developing reliable diagnostic tools is critical for accurate identification and intervention. The Hikikomori Questionnaire-25 (HQ-25) serves as a self-reported screening measure, while the Hikikomori Diagnostic Evaluation Interview (HiDE-I) is used for clinical confirmation. This study aims to assess the diagnostic classification and predictive performance of the HQ-25 compared to the HiDE-I in an Omani sample, with a specific focus on refining cutoff thresholds for better classification accuracy. A cross-sectional study was conducted in Oman in 2024, enrolling 454 participants from clinical and community settings. Participants were classified as either patients (psychiatric service users) or attendees (nonclinical individuals). The HQ-25 was administered at four cutoff thresholds (≥ 42, ≥ 50, ≥ 62, ≥ 75). The HiDE-I was used as the clinical criterion standard, classifying cases as pathological, at-risk, or resembling hikikomori. Diagnostic metrics-including sensitivity, specificity, predictive values, and receiver operating characteristic (ROC) curves-were calculated. Table-based analyses demonstrated that at the ≥ 42 cutoff, the HQ-25 yielded 62.9% sensitivity and 57.6% specificity under the strict HiDE-I definition, and 80.0% sensitivity with 53.4% specificity under the confirmed HiDE-I definition. ROC analyses across all thresholds showed area under the curve (AUC) values ranging from 0.58 to 0.66 (strict HiDE-I) and 0.55 to 0.85 (confirmed HiDE-I), with the highest classification accuracy observed among psychiatric patients. The HQ-25 is a useful screening tool but insufficient on its own for diagnosing hikikomori. Incorporating both diagnostic tiers revealed its limitations and reinforced the need for structured clinical assessments to improve accuracy, especially in nonclinical settings.
Measles is a highly contagious disease, transmitted by respiratory droplets, whose spread is favored by disparities in vaccination coverage. This manuscript analyzes recent measles control strategies, analyzing their evolution, environmental repercussions, immune mechanisms, innovations in immunization, and sustainable solutions. Measles harms public health, and the socioeconomic landscape and advances in smart technologies offer innovative solutions for combating the disease. Obstacles to the fight against measles include low vaccination coverage, community resistance, and logistical problems related to vaccine distribution. The immune response to measles involves B and T cells, which are crucial in eliminating the virus and forming an immune memory. Improvements in measles vaccines include the development of genetically modified vaccines and advanced delivery technologies. To eliminate measles, the manuscript recommends exploring data-driven and digital surveillance tools as complementary support to traditional epidemiological systems.
Streblus asper (Moraceae) is traditionally used for neurological and febrile disorders, but its pharmacological basis remains unclear. This study evaluated the S. asper leaf methanolic extract (SAL-ME) for anxiolytic, antidepressant, sedative, and antipyretic activities using Swiss albino mice and in silico docking analyses. Behavioral assays included the elevated plus maze, hole-board, forced swim, tail suspension, hole cross, and open field tests, while brewer's yeast-induced pyrexia was used to assess antipyretic activity. SAL-ME (200 and 400 mg/kg) produced dose-dependent effects, significantly reducing immobility time (p < 0.001), increasing open-arm exploration (p < 0.01), and suppressing locomotor activity, indicating antidepressant, anxiolytic, and sedative actions. A significant antipyretic effect was observed at 400 mg/kg, with a marked reduction in rectal temperature within 3 h posttreatment (p < 0.01). Molecular docking analysis revealed notable binding affinities of octadecanoic acid, hexadecanoic acid, D-pinitol, α-D-glucopyranoside, myo-inositol, and butanedioic acid with target proteins associated with GABAergic, serotonergic, and prostaglandin-mediated pathways. Collectively, these findings suggest that SAL-ME exerts dose-dependent, multitarget pharmacological effects, supporting its potential as a phytotherapeutic candidate for CNS disorders and fever.
An electric field mill (EFM-100) short-range sensor has been installed on the rooftop of the six story building of Kathmandu BernHardt College, Tribhuvan University (TU), Bafal, Kathmandu for continuous monitoring of lightning events within a 38 km in aerial distance. It constantly monitors lightning activities and activates graphical display alerts on a connected computer. The EFM-100 monitor software displays a real time graph of the atmospheric electrical field, as well as the distance of lightning strikes within the specified range. Lightning strikes were recorded over a 3-month period, spanning 57 days, primarily during the both premonsoon and monsoon seasons, from May 19 to August 18, 2024. In addition, the effect of lightning events on atmospheric pollutants such as PM1, PM2.5, PM10, relative humidity, temperature, and particle density of ozone per cubic centimeter were analyzed. These atmospheric parameters were obtained from the Central Department of Environment Science, TU, Nepal. The results clearly show that the atmospheric pollutants PM1, PM2.5, and PM10, found to be higher during the lightning events. PM2.5 concentrations increased by an average of approximately 18%, PM10 by about 12%, and ozone by about 22% compared with prelightning background levels. Similarly, the atmospheric temperature increased by 1.5°C-2.0°C during the strong lightning events followed by a gradual decrease thereafter. These findings provide clear evidence of the magnitude of pollutants and thermal changes associated with lightning. It was also observed that relative humidity is inversely proportional to temperature, as it rises due to lightning phenomena. Furthermore, lightning activity was found to significantly increase with rising concentrations of ozone particles in the atmosphere.
In the rapidly evolving e-commerce landscape, retaining existing customers has become more cost-effective and strategically important than acquiring new ones. This study proposes a data-driven framework that integrates business intelligence (BI) tools, machine learning, and customer relationship management (CRM) decision support to improve predictive customer retention. The framework was developed using the publicly available Brazilian E-Commerce Public Dataset (Olist), which contains more than 100,000 orders and includes transactional, payment, delivery, product, and customer-review information. After SQL-based integration and feature engineering, customer segmentation was performed using K-means clustering on recency, frequency, monetary (RFM) variables, identifying three behavioral groups: loyal, at-risk, and occasional customers. For churn prediction, Random Forest and XGBoost classifiers were trained on customer-level behavioral, satisfaction, and service-related features. XGBoost achieved the best overall performance, with accuracy = 0.81, precision = 0.79, recall = 0.83, F1 - score = 0.81, and AUC = 0.85, outperforming Random Forest (accuracy = 0.76, precision = 0.74, recall = 0.71, F1 - score = 0.72, and AUC = 0.76). The resulting segmentation and churn scores were then exposed through Power BI dashboards and mapped into a proof-of-concept CRM decision framework for retention planning. Unlike studies that treat BI, machine learning, or CRM in isolation, this research presents an end-to-end analytical pipeline that links data preparation, predictive modeling, dashboard-based decision support, and scenario-level CRM action design. The framework provides a reproducible basis for e-commerce retention analytics and a practical foundation for future live deployment and A/B-tested CRM validation.
Understanding sustainable land management is important for agricultural production. However, soil fertility has been declining due to anthropogenic and natural factors. To curb these problems, soil and water conservation (SWC) practices have been implemented over the last 15 years in the study watershed, but their impact has not been studied in detail. Therefore, this study was conducted to study the effects of SWC practices on soil fertility and soil erosion in the Fetam watershed. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and sediment delivery ratio (SDR) model were used to analyze the sedimentation based on the required input parameters of the model such as land-use/land-coverraster data, Revised Universal Soil Loss Equation factors such as rainfall-erosivity data, erodobilityfactor, cover factor (C), and management factors (P) and raster digital elevation model including the boundary of the study area in vector form. A total of 32 soil samples (0-20 cm) were collected and disaggregated by landscape position: upper, middle, and downstream. After collecting soil samples, the soil physicochemical properties, such as texture and bulk density (BD), and chemical properties such as (pH), organic carbon (OC), total nitrogen (TN), available phosphorus (AP), exchangeable bases, and cation exchnage capacity (CEC) were analyzed using standard laboratory procedures in the treated (Hunkan) and untreated (Kindikan) subwatersheds. The t-test and analysis of variance (ANOVA) results show that the physical and chemical properties of soil differed significantly between treated and untreated subwatersheds and between upper and lower landscape positions (p < 0.05). The mean soil loss in treated and untreated microwatersheds was 14.43 ± 6.63 and 20.31 ± 10.28 t/ha, respectively. This indicates that the SWC structures implemented in the treated subwatershed contributed to reducing soil erosion rates. The treated watershed and lower landscape position had the highest OC (3.69 ± 0.23) and the lowest BD (1.05 ± 0.01). In contrast, the untreated watershed and upper landscape position had the highest BD (1.21 ± 0.01) and the lowest OC (2.35 ± 0.23). Overall, the soil fertility parameters were most favorable in the treated fields and lower-slope positions, suggesting that SWC measures implemented have significantly improved soil fertility and reduced soil loss. Therefore, it is necessary to scale up the SWC practices to other untreated areas of the watershed in the Blue Nile Basin of Ethiopia to achieve sustainable development.
Data center management, the foundation of contemporary cloud computing, has made energy saving a top priority. Among other difficulties, the placement of virtual machines (VMs) has a major impact on data center resource and energy usage. Assigning VMs to physical machines (PMs) is a challenging NP-hard problem, especially in large-scale infrastructures where it is computationally infeasible to find an ideal solution. To solve the VM placement problem, the proposed study formulates it as a restricted optimization problem with the goal of preserving performance while lowering energy consumption. The explosive growth of data centers has resulted in higher energy consumption and higher carbon dioxide (CO2) emissions, which are a primary cause of climate change. Globally, governments, energy-focused organizations, and business executives have taken notice of this expanding environmental impact. This study provides a comprehensive analysis of data center energy consumption patterns, environmental effects, and trends in energy consumption. It also suggests doable energy-saving measures, such as installing energy-efficient infrastructure and upgrading air conditioning systems. The paper also presents an improved genetic algorithm-based method that is tailored for energy-conscious VM deployment, successfully striking a balance between computing economy and convergence accuracy. The suggested solution highly increased data centers' energy efficiency by incorporating this strategy within a profile-based virtual resource management model. Additionally, policy suggestions for sustainable data center management are delineated, advancing the more general objective of ecologically conscious cloud computing. Experimental results demonstrate that the proposed method achieves up to 50% reduction in execution time, 48% fewer generations for convergence, and approximately 7% reduction in energy consumption compared to traditional first fit decreasing (FFD) methods. Additionally, the integration of task classification improves energy efficiency by up to 15% and reduces the number of active PMs. These findings highlight the effectiveness of the proposed framework in enabling scalable, energy-efficient, and environmentally sustainable cloud data center management.
In 2024, a study was conducted in the Almaty Region on 16 varieties of spring soft wheat to assess the impact of growth stages, cultivation conditions, and the severity of root rot infection on biometric parameters and crop yield. This study represents the first systematic evaluation of root rot dynamics across multiple growth stages and contrasting agronomic backgrounds in Kazakhstan. It was found that the maximum root rot prevalence (32.65%) and severity (8.78%) were observed during the tillering phase. By the harvesting phase, these indicators decreased, indicating the dynamic nature of pathogenesis. Cultivation conditions had a significant influence: Under natural conditions, disease prevalence was 26.79%, while on an infectious background, it increased to 31.60%. With fungicide application, prevalence decreased to 8.80%. Fungicide treatment proved effective in suppressing infection, though some varieties exhibited stress responses, leading to reduced productivity. The highest grain weight per plant (13.71 g) was recorded under natural conditions, while the lowest (8.31 g) was observed on the infectious background. Analysis of biometric parameters revealed significant differences between varieties in traits such as tillering capacity, stem length, spike length, and spikelet number. These findings provide a practical framework for improving integrated disease management strategies and for breeding wheat varieties adapted to regional agroecological conditions. These results highlight the importance of integrated disease management and genotype selection to sustain wheat productivity under biotic stress.
Brassica napus is a major oilseed crop used for edible oil and industrial applications. Because high-erucic acid (EA) levels reduce oil quality, identifying robust loci controlling EA is a priority for breeding low-EA rapeseed cultivars. Here, we performed a meta-quantitative trait locus (meta-QTL; MQTL) analysis using 57 published EA QTLs compiled from 12 independent mapping studies. A consensus genetic map spanning 487.24 cM was constructed with 399 markers across two linkage groups (LgA08 and LgC03). Of the 57 QTLs, 37 were successfully projected onto the consensus map and consolidated into 10 MQTL regions. MQTL confidence intervals (CIs) were substantially narrower than those of the projected QTLs (mean CI 3.75 cM vs. 8.56 cM), representing a 2.28-fold (56.19%) reduction. Candidate gene mining within MQTL intervals identified 67 genes, including eight prioritized candidates associated directly or indirectly with EA metabolism, located in MQTL1.1, MQTL1.2, MQTL1.3, MQTL2.1, and MQTL2.4. Notably, MQTL1.2 and MQTL2.4 contained KCS/FAE1-pathway candidates encoding 3-ketoacyl-CoA synthase (KCS), a key enzyme class in very-long-chain fatty acid elongation. The MQTLs and prioritized candidate genes identified here provide a valuable foundation for marker-assisted selection and future functional validation aimed at developing low-EA rapeseed cultivars with improved oil quality.
The present study aims to provide a thorough assessment of the level of knowledge and awareness of oral cancer among a general population of Egyptians and at correlating the main risk factors associated with the level of knowledge. One thousand twenty-eight questionnaires were distributed to the general public at 10 public places located in Cairo, Egypt. Questionnaire results were summarized via descriptive statistics followed by correlation which was performed on the answers to different questions. The overall knowledge of oral cancer among a population of Egyptian public was not strong, with 40% scoring 6-8 out of the maximum score 17. Only 14% of the participants acquired their knowledge from a healthcare provider. However, 47.2% did not know the symptoms of oral cancer. By common sense, 811 knew that tobacco is one of the causes of oral cancer. The Egyptian population demonstrated a low knowledge of oral cancer, a finding that is supported by the results of many previous studies conducted on other populations. This finding was significantly influenced by a sociodemographic factor, that is, a higher level of education and young age. Therefore, educational programs and specialized departments are recommended to work on providing and imparting deeper knowledge of oral cancer.
Assessing land degradation is essential for identifying susceptible regions and planning sustainable landscape management approaches. This research employed a combination of geographic information system (GIS) and multicriteria analysis (MCA) to delineate and evaluate land degradation within the Choke Mountain watershed of the upper Blue Nile. The Analytical Hierarchy Process (AHP) was employed to standardize all indicators and assign weights through comparison. A comprehensive analysis of physical, chemical, and biological indicators of land degradation was carried out. The results showed that about 50.64% of the watershed is at a high to very high risk of soil erosion, with an average loss of 44 t of soil per hectare each year. More than half of the watershed also exhibits moderate-to-high biological degradation levels, as evidenced by sparse vegetation cover and low levels of soil organic matter. About 70.7% of the area experiences only a mild physical degradation type. Biological degradation was rated as low in 37.4% of the watershed and moderate in 55.5%. The chemical degradation assessment revealed that most of the area (55.6%) has neutral soil pH values between 6.7 and 7.3. The integrated MCA results showed that 1.2% of the watershed is very low, 25.5% is low, 37.15% is moderate, and 36.15% is highly degraded in the Choke Mountain watershed. Overall, the main causes of land degradation in the Choke Mountain watershed are severe soil erosion, deforestation, and biomass deterioration. The most evident signs of land degradation are extensive biodiversity decline and soil erosion. Therefore, implementing comprehensive land management strategies is essential to prevent land degradation, enhance soil organic matter, and increase vegetation cover.
A 3D tooth model segmented with the use of deep learning (DL) method program (CephX) as well as MIMICS software (manual segmentation) will be evaluated for reliability in the presented work. In addition, the segmented model was compared with the intraoral scan (IOS)-generated 3D tooth model in terms of the Bolton ratio and arch dimensions. A total of 30 patients attending the College of Dentistry/University of Baghdad with records of IOSs and CBCT scans were included. CBCT has been transformed into a 3D digital tooth model segmented with the use of MIMICS software and an AI-based program (CephX), and the Bolton ratio and arch dimensions (length and width) were measured utilizing Geomagic Control X software. Statistical analyses, including the mean and standard deviation, were performed, and a paired t-test was used to assess the systematic bias between the three methods. Bland-Altman plots and intraclass correlation (ICC) analysis were used to assess the agreement between the three methods. The means of CephX, MIMICS, and IOS of all analyses were mostly similar, and the difference between them was greatest in the anterior Bolton ratio of IOS and CephX images. The systematic bias demonstrated no significant difference (p value > 0.05) between CephX and MIMICS, but CephX versus IOS and MIMICS versus IOS demonstrated significant differences (p value < 0.05) in several measurements, such as Bolton ratios and arch length. Agreement using ICC revealed good to excellent reliability overall, but moderate agreement in overall Bolton between MIMICS-IOS and between CephX-IOS, anterior Bolton and interpremolar distance between CephX-MIMICS, and poor agreement in anterior Bolton between MIMICS-IOS and between CephX-IOS was found. Bland-Altman plots showed that CephX and MIMICS were consistent, implying minimal systematic bias. Digital and AI-driven tooth segmentation (MIMICS and CephX), and IOS methods generally provide consistent and reliable measurements in terms of the Bolton ratio and arch dimensions. However, caution is advised when interpreting certain Bolton ratio values because discrepancies and lower agreement may occur.
This study evaluated the effects of various root canal irrigation protocols on the surface microhardness and push-out bond strength of a fast-set hydraulic calcium silicate-based cement at two different stages of hydration. Standardized 3-mm-thick root slices with uniform lumens were filled with RetroMTA (BioMTA, Seoul, Korea). The slices were randomly assigned to two setting intervals (1 or 14 days), after which their upper surfaces were irrigated using one of several protocols, including 2% NaOCl, 5.25% NaOCl, Dual Rinse HEDP mixed with either 2% or 5.25% NaOCl, 2% or 5.25% NaOCl followed by EDTA, or normal saline (as a control). The dislocation resistance of RetroMTA was then measured using the push-out bond strength test. For surface microhardness assessment, RetroMTA-filled polymethyl methacrylate molds were exposed to the same irrigation protocols, and their surface microhardness was measured using the Vickers microhardness test. The data were analyzed using one-way and three-way ANOVA tests. One-way ANOVA showed that neither the 1-day nor the 14-day RetroMTA groups differed significantly from the control group in push-out bond strength or surface microhardness (p > 0.05). Three-way ANOVA demonstrated that no two-way or three-way interaction effects were statistically significant. Moreover, NaOCl concentration, chelator type, and the time interval between RetroMTA placement and irrigation had no significant effects on push-out bond strength or surface microhardness (p > 0.05). Under the limitations of this experimental study, no significant differences were detected among the effects of Dual Rinse HEDP mixed with either 2% or 5.25% NaOCl, NaOCl alone, or NaOCl followed by 17% EDTA on the push-out bond strength or surface microhardness of 1-day and 14-day RetroMTA samples.
Long waiting times in banking services reduce customer satisfaction and operational efficiency, often resulting from misaligned staffing levels and fluctuating client traffic. The Cooperative Bank of Oromia in Holeta, Ethiopia, experiences significant service delays due to staffing inefficiencies and unpredictable customer arrivals. This study is aimed at optimizing staffing arrangements and minimize customer-waiting times to improve service delivery. Long short-term memory (LSTM) neural network was used to predict customer foot traffic, regression analysis assessed the impact of staffing on waiting times, and the M/M/C queuing model determined optimal staffing levels. Optimal staffing was identified as seven servers for the Holeta branch and six servers for the Goro Qeransa branch, with average system occupancy of 5.34 and 6.69 customers, respectively, leading to reduced waiting times. Aligning staffing with predicted customer flows can substantially improve service efficiency and customer satisfaction. Bank management should adopt data-driven staffing strategies based on predictive forecasting and queuing models, which can also be adapted for similar banking contexts beyond Ethiopia.