Balanced Distribution Research Articles

Improving the Quality of Low-grade Tobacco by Enzymatic Treatment and Co-fermentation with Yeast and Lactic Acid Bacteria.

Enzymatic treatment is a promising method to modulate the chemical composition, flavoring substances and to enhance the sensory quality of cigarettes. This study investigates the feasibility of enzymatic treatment in conjunction with co-fermentation with yeast and lactic acid bacteria to improve the quality of low-quality cigarettes. Amylase, flavourzyme, glucoamylase, protease, and their combinations were used for the enzymatic treatment of tobacco-sorghum by using the liquid-state fermentation method. The biochemical components and flavor substances of the fermented products were analyzed. The findings show that amylase and glucoamylase can effectively degrade starch into fermentable reducing sugars, facilitate microbial growth and proliferation, and significantly enhance the levels of flavoring alcohols and esters. Flavourzyme and glucoamylase, either individually or combined with amylase, achieved a more balanced distribution of flavor substances in the products. Additionally, flavourzyme was capable of increasing the content of guaiacol, solanesol, and 2-acetylpyrrole in the fermented products, thereby improving the richness and depth of the cigarette flavor. While the protease treatment group showed fewer flavor substances and higher nicotine content, which was detrimental to the quality of cigarettes. When integrating the fermentation products into low-quality cigarettes, flavourzyme and glucoamylase combined with amylase treatment received higher scores in sensory quality evaluations. This study provides a beneficial strategy for effectively improving the quality of low-quality cigarettes.

Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases

Abstract. Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments. However, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using nontraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach by training the model with grids corresponding to ground monitoring sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods' performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger absolute PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models due to the higher baseline concentration, though the relative error (approximately 20 %) is smaller compared to that in rural areas (over 50 %). The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with conventional neural network (CNN)-based deep-learning approaches like the residual neural network (ResNet) model, has successfully mitigated PM2.5 overestimation (by 5–30 µg m−3) and the corresponding exposure (by 3 million people ⋅ µg m−3) in the downwind area over 9 years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground monitoring sites identified through the testbed is essential for achieving a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of the associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community (Li et al., 2024a; https://doi.org/10.5281/zenodo.11122294).

Multi-objective evolutionary algorithm based on transfer learning and neural networks: Dual operator feature fusion and weight vector adaptation

In multi-objective evolutionary algorithms, one of the focal points is finding a balance between diversity and convergence. In decomposition-based algorithms, the role of weight vectors is crucial. Despite numerous studies dedicated to these aspects, there is a scarcity of utilizing transfer learning algorithms for dual-operator feature fusion and employing neural networks for accurate partitioning of the objective space population. To address the aforementioned issues, this paper proposes the following improvements: (1) Implementing a Balanced Distribution Adaptation (BDA) transfer learning algorithm to achieve dual-operator feature fusion, resulting in a transfer population guiding the adaptive adjustment of weight vectors. (2) Integrating the BDA algorithm with multi-objective algorithms requires labeling the data, a challenge in the multi-objective evolutionary algorithm. To tackle this issue, non-dominated sorting is introduced as a bridge connecting the BDA and multi-objective evolutionary algorithms. This serves as a method to combine the advantages of decomposition-based and Pareto-dominance principle-based multi-objective algorithms. (3) To overcome the impact of traditional Euclidean distance on population sparsity, a neural network is employed to determine the population's distribution in the objective space accurately. This ensures the precise identification of individuals to be removed from the current population and the areas where additions are needed. In order to fully validate the effectiveness of the proposed algorithm, four different sets of experiments are conducted in the experimental section, where three sets of benchmarking problems are compared to a variety of algorithms that have received much attention in recent years, as well as ablation experiments.

Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment

Purpose This study aims to evaluate the efficacy of various machine learning models in predicting heart failure incidence using medical data, focusing on the innovative aspect of a novel dataset. Unlike previous studies that predominantly examined features such as smoking status or age, this research explores novel features. The primary challenge addressed is the utilization of these new features, coupled with machine learning techniques, to accurately diagnose heart failure. Methods Five machine learning models, including logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors, were applied to analyze medical data from a dataset comprising over 900 individuals. The dataset encompasses diverse parameters such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, and electrocardiogram results, introducing a novel approach to feature selection. Results The evaluation of machine learning models unveiled varying performances in predicting heart failure. Logistic regression and support vector classifier exhibited the highest accuracy of 88%, followed by the decision tree classifier (below 85%), random forest classifier (84%), and K-nearest neighbors (82%). Additionally, the analysis revealed a balanced dataset distribution and highlighted sex-based disparities in heart failure incidence, along with significant correlations with factors such as age, chest pain severity, blood glucose levels, and physical activity. Conclusions The findings underscore the potential of integrating multiple machine learning models for early detection of heart failure, leveraging the inclusion of novel features in the dataset. However, careful model selection is crucial to account for discrepancies in accuracy among different models, emphasizing the importance of tailoring approaches based on specific project requirements.

Native and prosthetic septic arthritis in a university hospital in Saudi Arabia: A retrospective study.

Septic arthritis, whether native or prosthetic, poses a significant challenge in clinical practice due to its potentially devastating consequences. Despite its clinical importance, there remains a dearth of comprehensive studies and standardized diagnostic criteria, particularly in the Kingdom of Saudi Arabia. To investigate the epidemiology, microbiological profiles, and clinical characteristics of native and prosthetic septic joints in the Saudi Arabian population. Medical records of patients diagnosed with septic arthritis between January 1, 2015, and December 31, 2022, were retrospectively reviewed. Data regarding patient demographics, clinical presentation, microbiological cultures, treatment modalities, and outcomes were analyzed. In a retrospective review of 52 cases of septic arthritis, a balanced gender distribution was observed (1:1 ratio), with the knee being the most commonly affected joint (80.8%). Methicillin-resistant Staphylococcus aureus predominated in native joints (24.2%), while Brucella spp. was more prevalent in prosthetic joints (21.1%). Joint preservation was achieved in most cases (84.6%), with no significant difference in clinical features between native and prosthetic joints. However, certain comorbidities were more common in native joint cases, including renal impairment (P = 0.002), hemodialysis (P = 0.004), heart disease (P = 0.013), and chronic liver disease (P = 0.048). At the same time, osteoarthritis was more prevalent in prosthetic joint cases (P = 0.013). Vancomycin was the most frequently used antibiotic (26.9%), and most patients received antibiotics before joint aspiration (57.7%). Surgical intervention, predominantly arthrotomy, was required in most cases (32.7%). Notably, a significant association was found between joint type and mortality (odds ratio = 0.587, P = 0.048), as well as the Charlson comorbidity index and mortality (P = 0.001). This study highlighted distinctive microbiological profiles and etiological factors in septic arthritis cases in the Saudi Arabian population.

SENTIMENT ANALYSIS OF ELECTRIC CAR PRODUCT TRENDS IN INDONESIA USING BM25 AND K-NEAREST NEIGHBOR

The global and Indonesian shift towards electric vehicles (EVs) is driven by efforts to reduce emissions and promote sustainable energy. Social media, especially Twitter, functions as an important measuring tool regarding public sentiment towards electric vehicles in Indonesia, so that it can influence policy making. This research uses the BM25 and K-Nearest Neighbor (KNN) methods to analyze sentiment, which aims to improve EV adoption strategies. Conducted in 2023, this research applies data mining, specifically Knowledge Discovery and Data Mining (KDD), analyzing primary and secondary data descriptively and quantitatively starting with data collection from Twitter, followed by data crawling and initial text processing. Next, labeling, term frequency (TF) and inverse document frequency (IDF) calculations were carried out using the BM25 and KNN methods, with an Evaluation and Validation Diagram that visualized the process. The findings show that negative sentiment dominates at 48% (4800 data), followed by 34% (3400 data) neutral sentiment and 18% (1800 data) positive sentiment. The balanced distribution of sentiment highlights the diverse perceptions of society. BM25 and KNN pre-processing methods effectively reduce overfitting and underfitting, especially in negative and neutral sentiments. Accuracy testing without BM25 resulted in 58.6% to 60.25%, while integrating BM25 with KNN increased accuracy by 12.5% to 71% to 72.75%. Understanding sentiment provides a basis for decision making and policy development, as well as providing insight into public perceptions of electric vehicles in Indonesia. Implications include leveraging positive sentiment for marketing, adjusting strategies, refining pricing, addressing infrastructure and reliability issues, and collaborating with governments to increase adoption of electric vehicles in society.

Study on Rural Classification and Resilience Evaluation Based on PSR Model: A Case Study of Lvshunkou District, Dalian City, China

When implementing a rural revitalization strategy, it is of great significance to understand how to identify potential risks in different types of villages and how to achieve resilience governance. In this paper, a rural classification evaluation method is proposed from the perspective of resilience. The stress–state–response model and the TOPSIS method were used to analyze the resilience risks in different types of villages in Lvshunkou District. The results showed that the comprehensive resilience of rural areas in Lvshunkou District has the spatial distribution characteristics of “high center and low wings”, and the spatial differences of each resilience subsystem are different: the pressure resilience subsystem shows a trend of “increasing step by step” from the coast to the inland, the state resilience subsystem shows a trend of “decreasing step by step from the center to the two wings”, and the response resilience subsystem shows a trend of “blocky aggregation and balanced distribution”. This paper further proposes systematic reconstruction strategies for rural resilience related to the four aspects of rural space, industry, ecology, and governance, providing development ideas for measuring rural resilience indicators in Lvshunkou District and realizing long-term governance in different types of villages.

Efficient offloading and task scheduling in internet of thingth-cloud-fog environment

Efficient offloading and scientific task scheduling are crucial for managing computational tasks in research environments. This involves determining the optimal location for executing a workflow task and allocating the task to computing resources to optimize performance. The challenge is to minimize completion time, energy consumption, and cost. This study proposes three methods: latency-centric offloading (LCO) for delay-sensitive applications; energy-based offloading (EBO) for energy-saving; and efficient offloading (EO) for balanced task distribution across tiers. Scheduling in this paper uses a genetic algorithm (GA) with a weighted sum objective function considering makespan, cost, and energy for IoT-fog-cloud. Comparative studies involving Montage, Cybershake, and epigenomics workflows indicate that LCO excels in terms of makespan and cost but ranks the lowest in energy. EBO excels in energy efficiency, aligning closely with the base method. EO competes effectively with the base method in terms of makespan and cost but consumes more energy.<strong> </strong>This research enables the selection of the most suitable method based on the type of application and its prioritization of makespan, energy, or cost.

The integration of En route flow optimization, complex network clustering, and rule-based approach to airspace sub-sectorization for enhanced air traffic monitoring

This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization. The integrated framework combines the multi-commodity flow optimization approach, complex network clustering approach, and Minimum Bounding Geometry (MBG)-coupled Rule-based Approach for boundary design. A decomposition-based discrete particle swarm optimization (DPSO) is used to solve the clustering problem. The output of the flow optimization is used as a guiding standard for the DPSO. Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations (maxgen). The findings reveal that the multi-commodity flow approach captures system-wide flow operations. Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load. The MBG-coupled Rule-based Approach leads to compact and convex sub-sector boundary design. Major implications of this research include dynamic adaptability of the integrated framework, increased sensitivity of sector design to network evolution, and a computationally tractable framework. The higher controllability of the proposed framework also offers an increased acceptance among practitioners.

Analysis of Influencing Factors for Choosing Dermatology as a Future Career: A Cross-Sectional Study Among Medical Students at King Abdulaziz University.

Determining the influencing factors of choice of medical specialty is key to a balanced distribution of physicians across specialties. Dermatology, the specialty concerned with treating skin disorders, is known for being among the most wanted; however, studies identifying the factors that attract students to this specialty are lacking. Our study aimed to investigate the influencing factors of the choice of dermatology as a career in clinical-year medical students. We conducted a cross-sectional study with a sample of clinical-year medical students from King Abdulaziz University, Jeddah, Saudi Arabia, between 2020 and 2021. We collected data using an online self-administered questionnaire; we replicated a questionnaire present in prior research. We compared categorical data using the chi-square and Fisher's exact tests. Results: In total, there were 252 participants, with 30 (11.9%) choosing dermatology as a specialty. Over half showed an average grade of more than 4.5 (66.7%), and 83.3% were female. The significant influencing factors of students' choice of dermatology as a career were: the likelihood of dermatologists influencing patients' lives (p=0.000), opportunities to conduct research in dermatology (P=0.000), how Dermatology allows people to have a satisfying family life (P=0.001), and opportunities for part-time work in dermatology (p=0.000). Many factors influenced the choice of a future medical specialty in our sample. Focusing on these factors while guiding students to choose their specialty may enable a promising next generation of physicians.

The Montreal cognitive assessment: normative data from a large, population-based sample of Chinese healthy adults and validation for detecting vascular cognitive impairment.

The Montreal Cognitive Assessment (MoCA) is a valuable tool for detecting cognitive impairment, widely used in many countries. However, there is still a lack of large sample normative data and whose cut-off values for detecting cognitive impairment is considerable controversy. The assessment conducted in this study utilizes the MoCA scale, specifically employing the Mandarin-8.1 version. This study recruited a total of 3,097 healthy adults aged over 20 years. We performed multiple linear regression analysis, incorporating age, gender, and education level as predictor variables, to examine their associations with the MoCA total score and subdomain scores. Subsequently, we established normative values stratified by age and education level. Finally, we included 242 patients with vascular cognitive impairment (VCI) and 137 controls with normal cognition, and determined the optimal cut-off value of VCI through ROC curves. The participants in this study exhibit a balanced gender distribution, with an average age of 54.46 years (SD = 14.38) and an average education period of 9.49 years (SD = 4.61). The study population demonstrates an average MoCA score of 23.25 points (SD = 4.82). The multiple linear regression analysis indicates that MoCA total score is influenced by age and education level, collectively accounting for 46.8% of the total variance. Higher age and lower education level are correlated with lower MoCA total scores. A score of 22 is the optimal cut-off value for diagnosing vascular cognitive impairment (VCI). This study offered normative MoCA values specific to the Chinese adults. Furthermore, this study indicated that a score of 26 may not represent the most optimal cut-off value for VCI. And for detecting VCI, a score of 22 may be a better cut-off value.

Analyzing classroom interactions focusing on IRF patterns and turn-taking

This study aims to analyze patterns of interaction and turn-taking in the context of classroom learning using a classroom discourse analysis approach. This research focuses on understanding the communication dynamics between teachers and students and identifying dominant patterns during the learning process. The method used is qualitative, with data collection through observation using video recorder instruments in class XI SMK Muhammadiyah Mertoyudan. The research participants were a teacher and class XI students of SMK Muhammadiyah Mertoyudan, totalling 24 students, consisting of 1 teacher and 23 students. The data results show the sequences developed from the questions between teacher and student interactions. The results of this study support the view that the discursive role of the Feedback sequence in IRF (Initiation-Response-Feedback Sequence) is to develop dialogue between teachers and students. The findings of this study on the IRF pattern are that the teacher dominates the communication in the classroom 63,4% and students 36,6%. The findings indicate that the non-delivery of learning is the main problem of students' lack of understanding of the material, lack of students' ability to think critically, and responsiveness to students. These findings can be used to develop more inclusive and effective teaching strategies that pay attention to a more balanced distribution of talk time and interaction between teachers and students. This research contributes to the literature on Classroom Discourse Analysis by filling the knowledge gap on the classroom's interaction pattern and its implications for teaching practices with feedback in IRF. The results of this study can provide valuable insights for educational practitioners and help improve students' critical communication strategies in the classroom.

Forecasting sovereign CDS spreads with a regime‐switching combination method

AbstractWith the growing importance of the sovereign credit default swap (CDS) market, accurate forecasting of sovereign CDS spreads has gained significant attention. In view of the complex volatility in the series of sovereign CDS spreads, this study presents a novel combination forecasting framework, which introduces time‐varying weights to effectively combine diverse individual models. To identify optimal subsets of models, a mutual information approach is employed, while the regime‐switching method is utilized to integrate the selected models. The proposed method's efficacy is validated using data from 65 countries. Empirical findings underscore the superiority of the proposed approach over benchmark models in terms of both horizontal and directional prediction accuracy, particularly when the sovereign CDS data exhibits a balanced distribution between high and low volatility regimes.

The trade-offs effect of ecosystem health and socio-economic development on tea production

Ecosystem health (EH) maintenance and improvement are important goals in environmental and ecological management, and help realize regional sustainable development goals. EH assessment and the regulatory mechanism have remained popular research fields in landscape ecology science. However, the improvement in EH assessment methods is ongoing, the influences of EH and socioeconomic development on agricultural production remain unclear. In this study, we chose Fujian Province in China as the study area, first quantitatively assess EH based on the Vigor–Organization–Resilience–Services (VORS) model, and thereafter explore the response of tea production to EH and socioeconomic development using the structural equation model (SEM). We found that the mean EH value in Fujian Province was 0.52 in 2010, which was 6.12% and 10.64% higher than that in 2000 and 2020, respectively. The high-EH-value regions were distributed in the northwest, while the southeastern regions exhibited low-EH-value agglomerations. According to the temporal evolution characteristics of EH, the kernel density curve patterns exhibited single peaks, and the curves shifted from a broad to a narrow peak from 2000 to 2020, indicating a trend toward a more balanced distribution of EH spatially. SEM results showed a significant positive effect of EH on tea production with a coefficient of 0.86. However, there is a significant trade-off between EH and economic development, as indicated by a coefficient of −0.6. There are also negative indirect effects on tea development when mediated by economic development. However, the overall effect of EH on tea production is 0.8, indicating that EH is still effective in promoting tea production. The results of this study have implications for the integration of ecosystem management and agricultural production management in the region.

Clinical Features of Actinomycosis in Older Adults: A 10-Year Experience of a Single Institute in Beijing, China.

Actinomycosis is a rare infectious disease with non-specific clinical presentations often resulting in delayed diagnosis, especially in older adults. Diagnosing and treating actinomycetal infections in this population can be particularly challenging due to the lack of comprehensive case series studies focusing specifically on actinomycosis in older adults. The existing literature mainly consists of case reports, highlighting the need for more extensive research in this area. This study aimed to provide a profile of actinomycosis in older adults to guide future research efforts. Elderly patients aged 60 years and older who satisfied the inclusion criteria for actinomycosis at Peking Union Medical College Hospital from January 2014 to May 2024 underwent a retrospective analysis. The research centered on describing the clinical features and diagnostic techniques, distinguishing between different conditions, and treating clinically important instances of actinomycosis within this specific age bracket. This study involved 22 patients, with a balanced gender distribution of 11 males and 11 females, aged between 60 and 84 years, and a median age of 67 years. The disease predominantly affected the thoracic region (n=17), followed by the abdominal-pelvic (n=2) and orocervicofacial (n=2) regions, along with one case involving soft tissue (n=1). Microbiological methods confirmed the diagnosis in 17 cases (77%), while histopathological examination was employed in the remaining five cases (23%). General symptoms, such as fever and weight loss, were reported by 64% of the patients, whereas 32% exhibited symptoms localized to the infection site. Only one patient (4%) did not present any symptoms. The median duration from the onset of initial symptoms to diagnosis was 120 days (IQR 34.5-240). Nine patients were successfully treated with antibiotics, with only one patient experiencing a relapse during the follow-up period. Infections caused by actinomycetes are infrequent among the elderly and often exhibit non-specific clinical symptoms and imaging results. Among the various types of actinomycetal infections in this demographic, pulmonary actinomycosis is the most prevalent. Recognizing the wide-ranging capacity of actinomycetes to induce infections beyond our present knowledge is essential. It is important for healthcare practitioners to deepen their knowledge of actinomycosis to prevent delays in both diagnosis and treatment.

Exploring influential factors in childhood vaccine delays in Riyadh, Saudi Arabia.

Infectious diseases present a substantial worldwide health challenge, yet vaccines have played a crucial role in significantly decreasing illness rates. Despite their effectiveness, global vaccination coverage stands at 85%, leaving a considerable number of infants without routine immunization. Our study seeks to examine the occurrence of delayed primary vaccination and the factors influencing it in Riyadh, Saudi Arabia addressing a critical void in current research. A cross-sectional investigation was carried out in the primary care facilities of the first health cluster in Riyadh, Saudi Arabia. Information was gathered utilizing a well-structured questionnaire, modified from a prior study. The study encompassed children aged two years and younger, accompanied by their parents who brought their vaccination cards for the visit. Non-probability convenience sampling was employed for data collection. Subsequently, the data underwent thorough cleaning in Excel (Microsoft Excel) and was subjected to analysis using IBM SPSS 29 (IBM SPSS Statistics 29). The study involved 402 participants, primarily over 12 months old, with a balanced gender distribution. Noteworthy findings indicate a 20.9% prevalence of childhood vaccine delays, with reasons such as high-grade fever (33.3%) and travel (26.2%). Significant associations were found in children aged 6-12 months (25.8%, P = 0.039), families with over four children (31%, P = 0.010), perceptions of vaccine harm (50.0%, P = 0.013), and acknowledgment of neglect (96.4%, P = 0.001). The research illuminates the complex factors influencing childhood vaccine delays in Saudi Arabia. Stressing the significance of personalized interventions, it underscores the necessity to tackle challenges specific to age, parental perspectives, and access issues for improved vaccination outcomes.

Thermal analysis of batteries and prediction with artificial neural networks

PurposeIn this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.Design/methodology/approachIn this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.FindingsThe best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.Originality/valueWhen the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.

Quantifying Lumbar Foraminal Volumetric Dimensions: Normative Data and Implications for Stenosis-Part 2 of a Comprehensive Series.

Lumbar foraminal stenosis (LFS) occurs primarily due to degenerative changes in older adults, affecting the spinal foramina and leading to nerve compression. Characterized by pain, numbness, and muscle weakness, LFS arises from structural changes in discs, joints, and ligaments, further complicated by factors like inflammation and spondylolisthesis. Diagnosis combines patient history, physical examination, and imaging, while management ranges from conservative treatment to surgical intervention, underscoring the need for a tailored approach. This multicenter study, conducted over six years at a tertiary hospital, analyzed the volumetric dimensions of lumbar foramina and their correlation with nerve structures in 500 patients without lumbar pathology. Utilizing high-resolution MRI with a standardized imaging protocol, eight experienced researchers independently reviewed the images for accurate measurements. The study emphasized quality control through the calibration of measurement tools, double data entry, validation checks, and comprehensive training for researchers. To ensure reliability, interobserver and intraobserver agreements were analyzed, with statistical significance determined by kappa statistics and the Student's t-test. Efforts to minimize bias included blinding observers to patient information and employing broad inclusion criteria to mitigate referral and selection biases. The methodology and findings aim to enhance the understanding of normal lumbar foramina anatomy and its implications for diagnosing and treating lumbar conditions. The study's volumetric analysis of lumbar foramina in 500 patients showed a progressive increase in foraminal volume from the L1/L2 to the L5/S1 levels, with significant enlargement at L5/S1 indicating anatomical and biomechanical complexity in the lumbar spine. Lateral asymmetry suggested further exploration. High interobserver and intraobserver agreement levels (ICC values of 0.91 and 0.95, respectively) demonstrated the reliability and reproducibility of measurements. The patient cohort comprised 58% males and 42% females, highlighting a balanced gender distribution. These findings underscore the importance of understanding foraminal volume variations for lumbar spinal health and pathology. Our study significantly advances spinal research by quantifying lumbar foraminal volumes, revealing a clear increase from the L1/L2 to the L5/S1 levels, indicative of the spine's adaptation to biomechanical stresses. This provides clinicians with a precise tool to differentiate between pathological narrowing and normal variations, enhancing the detection and treatment of lumbar foraminal stenosis. Despite limitations like its cross-sectional design, the strong agreement in measurements underscores the method's reliability, encouraging future research to further explore these findings' clinical implications.

MERANCANG JARING INSANG PERMUKAAN UNTUK MENANGKAP IKAN DI TELUK AMBON DALAM

The use of gill nets in fishing is a well-established practice among fishermen due to several factors including ease of material acquisition, simple operational techniques, relatively affordable costs, and high selectivity in catching fish of specific sizes. This study aims to describe the design of fishing gill nets in Inner Ambon Bay. The research was conducted at the Ambon Fisheries Training and Extension Center (BP) in April and May of 2024. The design and construction of surface gillnets for mackerel scad consist of mesh size (inches), fish body circumference, net length, hanging ratio, weights, and distance between floats and weights. The design of the surface gill net with a 2-inch mesh size and a 45% hanging ratio has proven to be effective in catching mackerel scad (Decapterus sp) ranging from 17 cm to 26 cm in length. The use of polyamide (PA) material for the net body provides the necessary strength and flexibility, while the float made of polyvinyl chloride (PVC) and lead weights ensures balanced distribution for optimal functioning in the water. A 45% hanging ratio provides an ideal balance between net length and fish trapping ability, avoiding nets that are too curved or stiff, which can reduce fishing effectiveness.

Assessing PM2.5 Dynamics and Source Contributions in Southwestern China: Insights from Winter Haze Analysis

Despite enhancements in pollution control measures in southwestern China, detailed assessments of PM2.5 dynamics following the implementation of the Clean Air Action remain limited. This study explores the PM2.5 concentrations and their chemical compositions during the winter haze period of 2017 across four major urban centers—Chengdu, Chongqing, Guiyang, and Kunming. Significant variability in mean PM2.5 concentrations was observed: Chengdu (71.8 μg m−3) and Chongqing (53.3 μg m−3) recorded the highest levels, substantially exceeding national air quality standards, while Guiyang and Kunming reported lower concentrations, suggestive of comparatively milder pollution. The analysis revealed that sulfate, nitrate, and ammonium (collectively referred to as SNA) constituted a substantial portion of the PM2.5 mass—47.2% in Chengdu, 62.2% in Chongqing, 59.9% in Guiyang, and 32.0% in Kunming—highlighting the critical role of secondary aerosol formation. The ratio of NO3−/SO42− and nitrogen oxidation ratio to sulfur oxidation ratio (NOR/SOR) indicate a significant transformation of NO2 under conditions of heavy pollution, with nitrate formation playing an increasingly central role in the haze dynamics, particularly in Chengdu and Chongqing. Utilizing PMF for source apportionment, in Chengdu, vehicle emissions were the predominant contributor, accounting for 33.1%. Chongqing showed a similar profile, with secondary aerosols constituting 36%, followed closely by vehicle emissions. In contrast, Guiyang’s PM2.5 burden was heavily influenced by coal combustion, which contributed 46.3%, reflecting the city’s strong industrial base. Kunming presented a more balanced source distribution. Back trajectory analysis further confirmed the regional transport of pollutants, illustrating the complex interplay between local and distant sources. These insights underscore the need for tailored, region-specific air quality management strategies in southwestern China, thereby enhancing our understanding of the multifaceted sources and dynamics of PM2.5 pollution amidst ongoing urban and industrial development.