Identification Of Categories Research Articles

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making

BackgroundThe adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow. MethodsA systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability. ResultsThe Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical–legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations. ConclusionsThe digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.

ASSESSING MUSCULOSKELETAL DISORDERS (MSDS) OF WORKERS OF FIRED CLAY BRICKS INDUSTRY

Ergonomics is important for sustainable development in many fields, including architecture, health, product design, safety, and workplace design. Ergonomic assessments are essential in the workplace to prevent musculoskeletal disorders (MSDs). Currently, Indonesia has a standardized ergonomic assessment, SNI 9011:2021. This assessment standard is widely used to evaluate human posture. This study aims to evaluate the position of a traditional brick-maker in which the posture identification system is based on the SNI 9011:2021. This study employed a cross-sectional design with a sample of workers in conventional brick manufacturing. The primary findings of this study were visual ergonomic assessment for posture identification, identification of ergonomic assessment categories with high risk in brick manufacturing, and the prevalence of nagging pain among workers in the brick moulding and drying process. To reduce occupational ergonomic risk, particularly for brick-making workers, ergonomic intervention is required in the form of additional work aids such as chairs. This has been shown to reduce the evaluation score based on SNI 9011: 2021, thereby reducing the occurrence of musculoskeletal disorders

An online survey capturing the views of stakeholders on primary school food systems across the four UK nations

BackgroundIn 2020, the Generating Excellent Nutrition in UK Schools (GENIUS) Network was established to develop an understanding of the school food system across the four UK nations. This study explores stakeholders’ views (headteachers, teachers, parents and pupils) on what works well, the challenges, and what an ideal primary school food system includes.MethodsAn online ‘School Food Survey’ was created in Qualtrics XM including closed and open-ended questions about the primary school food system. The Qualtrics link was distributed to stakeholders with an interest in school food through key contacts and networks across the four UK nations (21st June to 21st July and September 2021). Responses from the open-ended questions were exported from Qualtrics into Excel and analysed using SPSS. Aspects of qualitative content analysis were applied to summarise, code and quantify responses. Identified codes were entered by stakeholder, for example, parents and their response to the question into a Matrix table to allow identification of categories, themes and interpretation.ResultsA total of 509 participants completed the survey: most participants were from Scotland (n = 281; 55%) and England (n = 213; 42%) and were parents (n = 394). There were some consistent views across stakeholder responses, for example, the range of healthy options, costs, and portion sizes offered to pupils. Parents views varied, with some expressing the range of healthy options worked well and others reporting too many unhealthy choices. The cost of school food and school food funding presented challenges for both parents and schools. For parents, an ideal school food system would include a wide variety of fresh healthy food choices that were made on site, use quality produce, be inclusive for all cultures and diets, and provide food portion sizes appropriate for pupils ages.ConclusionsThe findings iterate the diversity and some inconsistencies between stakeholders, emphasising the complexity and competing tensions school food systems encounter. Parental involvement and consideration of school-level and national factors are important when identifying challenges, what works well and describing an ideal primary school food system.

Neural network based on convolution and self-attention fusion mechanism for plant leaves disease recognition

In agriculture, early and timely detection and identification of plant disease categories can help growers take timely countermeasures. The use of deep learning techniques for plant disease category detection prevents further spread of the disease and helps to prevent crop production losses. In this paper, Based on the Next-Vit neural network model, we proposed a lightweight neural network CAST-Net based on the combination of convolution and self-attention, and we adopted self-distillation based on this model to achieve increased accuracy in classifying plant leaf diseases while reducing the number of model parameters and flops. Our model and method achieved 98.4% accuracy on the tomato subset of the data-enhanced PlantVillage dataset, a 4.9% improvement over the Next-Vit model, and 99.0% accuracy on the full PlantVillage set, a 6.9% improvement over the Next-Vit model. We also propose a new dynamic learning rate function that is applied to the training phase to prevent the loss from reaching the optimal value. The results show that our model and method have higher accuracy, fewer parameters, shorter training time and lower computational complexity than existing models.

The Artistic Use Defence in Trademark Dilution Cases – Hermès’ Legal Setback in Its Attempt to Prevent Others from Using Its Iconic Birkin Handbag

Abstract This opinion explores what constitutes a trademark parody in an artistic context and when it crosses the line of trademark infringement in dilution cases which occur beyond the traditional trademark protection from the likelihood of confusion. This is shown by analysing the latest two decisions regarding trademark parody handed down by the German Frankfurt am Main Regional Court in a case in which Hermès has suffered a setback in its attempt to prevent a fashion label from using its iconic Birkin handbag at the Berlin fashion show in 2023. While parody is considered a protected form of speech or artistic expression, no clear-cut legal rule separates a legitimate parody from an infringing use, in particular in cases of so-called mixed expressions where the reference to the protected trademark is not closely tied to commercial use but still happens outside of a purely artistic context. Compared to the statutory copyright’s flexible fair use defence, there is simply too little case law available to draw on in trademark dilution law. The few rigid defences German courts have cobbled together in trademark cases illustrate that trademark could benefit from a clearer identification of use categories. The following analysis of these decisions aims at highlighting that urgent trademark reforms are necessary in the area of art-related fair use defences. Furthermore, the following analysis will illustrate how big corporations like Hermès can fail when attempting to use trademark rights to control the meaning not only of wealth and exclusivity but also of conspicuous consumption and the equally conspicuous display of wealth, status and power in male-dominated domains.

Rapid detection of multi-scale cotton pests based on lightweight GBW-YOLOv5 model.

Pest infestation is one of the primary causes of decreased cotton yield and quality. Rapid and accurate identification of cotton pest categories is essential for producers to implement effective and expeditious control measures. Existing multi-scale cotton pest detection technology still suffers from poor accuracy and rapidity of detection. This study proposed the pruned GBW-YOLOv5 (Ghost-BiFPN-WIoU You Only Look Once version 5), a novel model for the rapid detection of cotton pests. The detection performance of the pruned GBW-YOLOv5 model for cotton pests was evaluated based on the self-built cotton pest dataset. In comparison with the original YOLOv5 model, the pruned GBW-YOLOv5 model demonstrated significant reductions in complexity, size, and parameters by 68.4%, 66.7%, and 68.2%, respectively. Remarkably, the mean average precision (mAP) decreased by a mere 3.8%. The pruned GBW-YOLOv5 model outperformed other classic object detection models, achieving an outstanding detection speed of 114.9 FPS. The methodology proposed by our research enabled rapid and accurate identification of cotton pests, laying a solid foundation for the implementation of precise pest control measures. The pruned GBW-YOLOv5 model provided theoretical research and technical support for detecting cotton pests under field conditions. © 2024 Society of Chemical Industry.

Ensemble learning based approach for traffic incident detection and multi-category classification

Traffic incident is one of the important causes of road congestion. Traffic incident detection plays a crucial role in the safety application of intelligent transportation systems, which provides timely information for traffic management departments and reducing losses. Despite many researches on incident detection approach, the identification of different incident categories is not enough. In addition, traffic incident detection is still a challenging task due to the problem of data imbalance and feature selection. In this study, we propose a two-stage traffic incident detection framework based on ensemble learning. In the first stage, a binary classification algorithm based on XGBoost (eXtreme Gradient Boosting) is established to detect whether there is a traffic incident, and 24 feature variables are determined by model feature selection. In the second stage, three resampling algorithms are utilized to reconstruct and balance the dataset. Through comparative analysis, SMOTE (Synthetic Minority Over-sampling Technique)-XGBoost is the best method for incident multi-category classification with precision of 87.27%, 78.52% and 92.54%, respectively. Moreover, the baseline comparison experiments are conducted to evaluate our model performance with real-word datasets. The proposed model achieves the highest average accuracy of 93.45% in the first stage and the macro-precision of 86.11% in the second stage. The results indicate that the proposed method outperforms baselines and the two-stage framework can accurately realize the incident detection and multi-category classification.

Decoding and preserving Indonesia's iconic Keris via A CNN-based classification

The present study explores the domain of Keris classification by employing advanced Convolutional Neural Networks (CNNs) as a potent technique for identifying subtle patterns and cultural characteristics inherent in these renowned Indonesian daggers. The study has presented encouraging findings about the identification of Pamor, Dhapur, and Tangguh categories. However, it is crucial to recognise and confront the inherent constraints associated with this research. The key constraints of the study pertain to the diversity of data, accuracy of labeling, generalizability of the model, and ethical considerations. The acquisition of a comprehensive dataset that effectively encompasses the whole range of Keris patterns offers a significant obstacle. Furthermore, it is crucial to pay careful attention to the accuracy of labeling, since it can be influenced by the subjective character of Keris classification. The important worry lies in guaranteeing the model's capacity to generalise to Keris images that have not been previously encountered, as well as its ability to comprehend and explain its decision-making process. The careful establishment of ethical frameworks is necessary to address ethical problems related to cultural sensitivity and the potential misuse of AI outputs in the realm of cultural heritage. Nevertheless, these constraints offer significant perspectives on potential areas for future investigation and enhancement. Future endeavours may prioritise the augmentation and broadening of the dataset, fostering collaboration with specialists in cultural domains, improving the interpretability of the model, and effectively addressing ethical considerations. The present study not only exhibits potential for expanding artificial intelligence in the domain of cultural preservation, but also contributes to a more profound understanding and recognition of the complex artistry and historical significance encapsulated within the Keris.

Identifying the Dimensions of the Domestic Tourism Demand Model in Tehran

Objective: The dynamics of domestic tourism have gained prominence, especially in the wake of global disruptions such as the COVID-19 pandemic, which shifted travel preferences towards more localized experiences. This study aims to identify and analyze the dimensions of domestic tourism demand in Tehran, focusing on the motivations, barriers, preferences, and impacts of tourism within the city. The objective is to provide a comprehensive understanding that can inform policy and strategic planning to enhance Tehran's appeal as a domestic tourist destination. Methodology: A qualitative research approach was employed, utilizing semi-structured interviews as the primary data collection method. The study targeted a purposive sample of local residents, domestic tourists, and professionals within the tourism sector in Tehran. Theoretical saturation guided the data collection process, ensuring a thorough exploration of themes. Data were analyzed through thematic analysis, allowing for the identification of key patterns, themes, and categories within the data. Findings: The study unveiled five main themes: Motivations for Domestic Tourism, Barriers to Domestic Tourism, Perceptions of Tehran as a Tourist Destination, Tourist Preferences and Behaviors, and the Impact of Domestic Tourism. Each theme comprised various categories with associated concepts, ranging from cultural interest, relaxation, and social connections to economic factors, safety concerns, and the broader economic, cultural, and social impacts of tourism. These findings underscore the complex interplay of factors influencing domestic tourism demand in Tehran. Conclusion: This research highlights the multifaceted nature of domestic tourism demand in Tehran, revealing a spectrum of motivations driving tourists, alongside significant barriers that need addressing. The study underscores the potential of domestic tourism as a catalyst for economic development, cultural preservation, and social cohesion in Tehran. By addressing identified barriers and leveraging the motivations and preferences of domestic tourists, Tehran can enhance its domestic tourism sector.

Green spaces system analysis and assessment of plantings’ ecological state in Minsk city applying geoinformation technologies

The article presents the results of public green spaces system of Minsk city exploration and assessment of their plantings’ ecological state, implemented with applying geoinformation technologies. To conduct the study, the information about spatial distribution features of green spaces system parameters, urban planning indicators and the ecological state of atmospheric air was selected and ranked (groups 1-9). Using this data set, in ArcGIS program with its additional Spatial Analyst module territory modeling was implemented and an original assessment map of Minsk city, which shows various favorability degrees areas (6-1) for green spaces system organization, was created. It was revealed that the sites of the lowest favorability degree (1) are concentrated in the southwestern part of the city (Oktyabrsky, Moskovsky, Frunzensky administrative districts). On their territory the ecological condition monitoring of public green spaces was carried out. Within green areas boundaries 74 field areas were laid on which 2220 trees were examined. The assessment of plantings was conducted in stages: an individual assessment of trees based on a set of dendrometric indicators, calculation of tree stands condition indexes, identification of public green spaces ecological state category. The completed assessment became the basis for measures proposal to optimize the green spaces system.

Identification of neutrophil phenotype categories in geriatric hip fracture patients aids in personalized medicine.

The number of geriatric hip fracture patients is high and expected to rise in the coming years, and many are frail and at risk for adverse outcomes. Early identification of high-risk patients is crucial to balance treatment and optimize outcome, but remains challenging. Previous research in patients with multitrauma suggested that neutrophil phenotype analysis could aid in early identification of high-risk patients. This pilot study investigated the feasibility and clinical value of neutrophil phenotype analysis in geriatric patients with a hip fracture. A prospective study was conducted in a regional teaching hospital in the Netherlands. At the emergency department, blood samples were collected from geriatric patients with a hip fracture and analyzed using automated flow cytometry. Flow cytometry data were processed using an automated clustering algorithm. Neutrophil activation data were compared with a healthy control cohort. Neutrophil phenotype categories were assessed based on two-dimensional visual assessment of CD16/CD62L expression. Blood samples from 45 geriatric patients with a hip fracture were included. Neutrophils showed an increased activation profile and decreased responsiveness to formyl peptides when compared to healthy controls. The neutrophil phenotype of all patients was categorized. The incidence of severe adverse outcome was significantly different between the different categories (P = 0.0331). Moreover, patients with neutrophil phenotype category 0 developed no severe adverse outcomes. Using point-of-care fully automated flow cytometry to analyze the neutrophil compartment in geriatric hip fracture patients is feasible and holds clinical value in determining patients at risk for adverse outcome. This study is a first step toward immuno-based precision medicine for identifying geriatric hip fracture patients that are deemed fit for surgery.

Intelligent Assistance Method for Preschool Education Dance Curriculum Based on Deep Learning

Abstract Preschool education is vital for shaping the foundation of young children’s development across cognitive, social, emotional, and physical dimensions. Dance courses are often included in preschool curricula to foster creativity, expression, physical coordination, and emotional regulation. Understanding and interpreting emotions conveyed through dance movements can be instrumental in enhancing children’s emotional intelligence and overall well-being. This paper investigates an innovative approach to intelligent assistance in preschool dance courses by utilizing deep learning methods for emotion recognition within dance movements. The proposed model integrates deep and shallow features to improve the precision of emotion detection, offering a promising means to facilitate personalized and interactive learning experiences. First, emotions in dance are extracted through a multi-layer convolutional neural network, and high-level features are fused with low-level features. Then, the fused features of different depths are used as the basis for emotion recognition. The convolutional neural network’s fully connected layer outputs the emotional probabilities of dance movements, allowing for the identification of emotional categories in dance movements. Finally, experiments are conducted using the fusion of features from different convolutional layers, validating the effectiveness of the proposed method in emotion recognition.

Predictive Model for Student Academic Decline During Virtual Education in the COVID-19 Pandemic

Academic decline has always been a primary concern for researchers in the field of education. Recently, the COVID-19 pandemic has shed new light on this phenomenon. This study aims to identify factors affecting academic decline during the COVID-19 pandemic and develop a model to predict academic decline during virtual education. The present research employed a mixed qualitative-quantitative methodology. The study population included all mothers, students, and teachers of middle and high school levels in the city of Sabzevar in 2021. A sample of 30 individuals was selected through purposive sampling due to data saturation, considering inclusion and exclusion criteria, and interviewed. Following the identification and validation of qualitative categories and factors, a questionnaire was designed and administered to 384 participants (mothers, students, and teachers of middle and high school levels). The unlimited population responded to the researcher-made 49-item questionnaire on the challenges of virtual education impacting academic decline during the COVID-19 pandemic. To test the research hypotheses, descriptive statistics and structural equation modeling (SEM) using SPSS 20 and PLS Smart software were utilized. The findings indicated that, following qualitative interviews, six main grounded theory categories emerged, encompassing 12 fundamental factors. These included two causal factors (lack of motivation and academic isolation), one central factor (academic decline), three contextual factors (quantity and quality of electronic devices, internet quality, and software and hardware deficiencies), three intervening factors (role pressure on parents, web usage culture, and learning environment), and two strategies (ineffective teaching and inappropriate assessment) and one outcome (reduced learning). Ultimately, the structural equation modeling validated the conceptual model designed based on grounded theory. The results indicated that 12 fundamental factors influenced academic decline during virtual education amidst the COVID-19 pandemic, leading to the development of a significant predictive model for academic decline in virtual education.

Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification

<abstract> <p>Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process.</p> </abstract>

К ВОПРОСУ О ГАСТРОНОМИЧЕСКОЙ РЕФЛЕКСИИ

The article is devoted to culturological gastronomic reflection, which is considered as a state of consciousness in relation to a specific sphere of cognition. The author studies the periodization of the development of reflection, in accordance with the periods of development of cognition methods. Thus, highlighting the stages, starting from the scholastic period and ending with activism, the author agrees with some authors in the naming and content of these periods. However, it is worth pointing out that this is dialectical-materialistic activism, which is based on the corresponding philosophical principles. As a result of the study of the concept and essence of reflection, the author concludes that the present stage of the development of reflection is based on an activity-based approach to the study of the cognition system, which means the implementation of activities for the formation of a certain knowledge. Since scientific reflection at the present stage is increasingly connected with social development and penetrates into its spheres, it is noted that the combination of reflection and dialectical-materialistic approach and worldview makes it possible to reflect not only in the general philosophical sense, but also within the practical application of reflection, in private scientific and everyday consciousness. The result of the application of cultural reflection in the field of gastronomy is the identification of categories of problems of science ("gastrosophy" is a term proposed by some scientists for the name of science) and its content: historical–descriptive, analytical, state and a number of others, which have their own range of private issues involving research and, of course, reflection.

Skin Cancer Detection Using CNN

Abstract: Skin cancer disease is recognized as one of the most perilous types of cancer & an increasing rate of mortality is attributed to insufficient awareness of symptoms and preventive measures. Consequently, it is of very importance to detect skincancer at the beginning of stage to prevent its spread. Various types of skin cancer exist, with some posing significant risks. Detecting malignant skin lesions, particularly those with pigmentation, necessitates advanced image detection techniques and computer classification capabilities. In the proposed model, we have used the HAM10000 dataset, comprising 10,015 images, for enhancing the accuracy of skin cancer detection. We have carefully selected a subset of this dataset and implemented augmentation techniques to improve the model's precision. Our analysis focuses on the CNN- based model. Our proposed system achieved an outstanding validation accuracy of 97.92 % with the CNN model. This research contributes to the early identification of specific categories of skin diseases, empowering medical practitioners to validate and administer appropriate treatments

Identification of Ideational Grammatical Metaphor in Saraiki Language and its Deployment in Different Registers of Saraiki and English: A Contrastive Study

Grammatical Metaphor refers to a set of lexico-grammatical choices offered by a language to create formality of expression and pithiness. Firstly, this linguistic feature was discussed by Halliday in 1985 who classified one of its types -the Ideational Grammatical Metaphor (IGM) into 13 types and regarded it as a scientific or academic discourse characteristic. The IGM phenomenon is now considered central in SFL (Systemic Functional Linguistics)- inspired language teaching and significant for translators in understanding the Metafunctional meanings across SL (Source Language) or TL (Target Language). However, this worthwhile linguistic phenomenon has been understudied in the context of Pakistani languages. This work studies the Saraiki language for its identification and deployment in different English and Saraiki registers. The study follows the SFL Grammatical Metaphor Theory of Halliday (1998; 1999; 2006) as a theoretical framework and Content analysis as a tool for the identification and categorization of IGMs. The text consisted of the undergraduate course books of Saraiki and English, and its sample is delimited to 15 excerpts from both languages. The research results in the identification of all thirteen IGM categories in Saraiki, as in English; while regarding the IGM deployment in different registers of both languages, it is observed that the extent of IGM usage in Saraiki written text (1.14 per Clause) is greater than in English (0.83 per Clause), and academic text of both languages is found to have more instances of Ideational Grammatical Metaphors than the works of fiction in these languages. The study contributes to the body of research on Grammatical Metaphor and SFL-inspired comparative studies on Indo-Aryan languages of the world.

Risk Evaluation and Management Involved in Supply Chain Management

Supply chain management holds a pivotal role in determining the success of businesses, and its effectiveness can significantly influence an organization's performance. Nevertheless, it is vulnerable to a wide array of risks that have the potential to disrupt operations, result in financial losses, and tarnish a company's reputation. This summary offers an insight into the crucial components of evaluating and managing risks within supply chain management. In this context, risk evaluation encompasses the identification and assessment of various risk categories that could impact the supply chain. These risks may include natural disasters, geopolitical challenges, fluctuations in demand, delays in transportation, and issues related to suppliers. A comprehensive risk assessment involves both quantitative and qualitative analysis to gauge the likelihood and potential consequences of these risks on the supply chain. Effective risk management strategies are indispensable for mitigating the adverse effects of these identified risks. Risk management in supply chain management encompasses several actions, such as risk avoidance, risk transfer, risk reduction, and risk acceptance. Developing a resilient supply chain capable of adapting to unforeseen circumstances is also a critical element of risk management. Furthermore, this summary underscores the significance of technological tools and data analytics in the field of risk management. Advanced technologies like the Internet of Things (IoT), blockchain, and artificial intelligence enable real-time monitoring and predictive analytics, which can improve the visibility of risks and provide early warning signals. The success of a supply chain heavily relies on a proactive approach to risk assessment and management. By identifying potential risks, evaluating their impact, and implementing robust risk management strategies, organizations can bolster the resilience of their supply chains and ensure uninterrupted operations even in challenging environments. This abstract emphasizes the significance of understanding, evaluating, and effectively managing risks in supply chain management, as it contributes to an organization's long-term sustainability and competitive advantage.

Seismic Risk Assessment of Existing Buildings of HSTU Campus in Dinajpur

Bangladesh is at high risk of earthquakes due to its geographic location. Earthquakes can cause widespread damage to buildings and infrastructure, as well as significant loss of life, due to the sudden release of energy in the form of seismic waves. Therefore, identifying and assessing the vulnerability of existing buildings is crucial for earthquake risk reduction. Hajee Mohammad Danesh Science and Technology University (HSTU) is situated just 13 kilometers from Dinajpur, which falls under zone II, a medium-risk zone according to the Bangladesh National Building Code (BNBC, 1993). This study aims to evaluate the seismic vulnerability of HSTU's existing buildings. A widely used seismic assessment technique, the Turkish two-level assessment procedure, was employed in this study to identify seismically vulnerable buildings. A total of 79 buildings were surveyed at level I, considering vulnerability parameters such as soft storey, heavy overhanging, pounding effect, topographic effect, short column, shape of building, number of storeys, apparent quality of building, and soil type. Based on these parameters, performance scores were assigned to each building, and they were classified into damage categories of safe, moderate, and unsafe at level I. Digital photographs of each building from at least two directions were taken for easy identification. All 79 buildings on the HSTU campus were found to be safe after the level I survey. Level II assessment was conducted on only one building to validate the level I findings, and it was classified as a low-risk structure. A significant outcome of this study is the identification of damage categories and the potential to reduce seismic risk at HSTU by creating a comprehensive building inventory.