cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2022.803
title: Warehousing service provider selection using AHP, TOPSIS, and hybrid approach: a case study

description: Warehouses are highly significant connections in the supply chain network, whether the market in question is local or worldwide. The position of the warehouse in the network of the supply chain influences the efficiency and speed of the supply chain. The acceleration of material movements within supply chains is helped by warehouse management activities.As of now, the company has encountered several problems regarding its current warehouse storage rate and operation. The aim of this study is to define the most appropriate third-party service providers that meet company’s the minimum warehousing criteria and at the most valuable storage rate. The three suppliers S1, S2 S3 are chosen for selection The experience decision-makers in warehouse operation are evaluated for each criterion and given a score for each model.Consequently, selecting a suitable Multi-Criteria Decision-Making (MCDM) method is a crucial step in selecting appropriate the third-party logistic service provider (3PL). In this research, the chosen selection method is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytic Hierarchy Process (AHP), and the hybrid model of the Analytic Hierarchy Process (AHP) and Techniquefor Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The result from 3 different methods indicated that S3 is the most suitable for the company.
DOI: 10.14457/tu.the.2022.804
title: Estimate optimal warehouse design for the beverage distribution center

description: This paper will develop the effectiveness of designing a warehouse by determining the travel distance for the order-picking using a heuristic algorithm. Order picking is the process of retrieving products from a warehouse to fulfill each customer's orders. The order-picking process is one of the processes that cost a lot of money and is time-consuming. Therefore, it can be a significant factor that helps to improve the efficiency of the warehouse. For the data collection, this study required quantitative data such as the demand for each product, the dimension of the warehouse, and the retrieval of products. By using this data, it can determine the product retrieval activity. According to the literature review, many papers propose the modification of the heuristic algorithm, S-shape, largest gap, and TSP method to determine the best route for the travel distance in the warehouse. This study selected a beverage distribution center in Samutprakarn, Thailand as a case study to investigate inventory management practices. Customer order and purchase order data were collected to compute the daily inventory levels of this distribution center. Subsequently, we analyzed the warehouse layout and developed a picking route based on the stock levels of inventory. These findings provide insights for practitioners seeking to optimize inventory operations. After preparing the data, we propose the warehouse simulation to run the warehouse operation in one month by using the VBA (visual basic analysis) language in Excel. There are two scenarios for this case study. the first scenario is the original position of the product while the second scenario is positioning the products based on the percentage of total sales. After simulation in one month, the result shows that the second scenario can save in more than 100,000 steps.
DOI: 10.14457/tu.the.2022.805
title: An automatic warehouse inventory monitoring system based on text extraction using computer vision

description: Nowadays, most of product companies around the world do sales forecasting and inventory management to give themselves benefits to be able to take care of all customers’ demand and avoid the shortage of supplies that might cause a lost in sales. Most companies then require at least a warehouse whether it is an in-house or outsourced warehouse to store products safely and be able to deliver them to assigned destinations. That is why warehouse management is an inevitable factor when we consider maximizing supply chain’s profit. In order to ensure that operating warehouses perform product storage correctly, companies usually request for an inventory checking report from warehouses, some for annually and some for semi-annually. This report normally presents meaningful keys that represent identity of each product such as product ID, pallet’s tag ID, location of pallet on the rack, and quantity of products in that pallet. However, this request indirectly sends non-benefit impacts to the warehouse since inventory checking has been done manually which requires several workers to unload, check, and load pallets back on the rack and also needs to stop all transporting schedule to do the checking. With the following impacts, warehouse needs to reschedule its normal activities and sometimes even postpone the delivery appointment. This study is going to find the solution for these drawbacks by replacing manual inventory counting with an automatic warehouse inventory monitoring system based on text extraction with the use of computer vision that can detect product label attached on every pallet on the rack, recognize and extract key information from label, and send digital text to store in document for comparing with the master data. Under the field of computer vision, technology of image processing will be applied in the part of detecting product label from captured images while optical character recognition will be applied to extract text from cropped image of label. With the help of technology, warehouse can reduce time used and decrease human error in the inventory checking process.
DOI: 10.14457/tu.the.2022.806
title: An intelligence system for a car-carrier loading optimization

description: The route planning and vehicle positioning process for car-carriers in the finished vehicle logistics business is currently a manual and time-consuming task, resulting in suboptimal solutions due to variations in individual planner logic. This research aims to develop and validate a standardized mathematical model for car-carrier route planning to obtain optimal solutions. By addressing the vehicle routing problem specifically for car-carriers, this study aims to derive a tailored mathematical model.Different methods exist for solving vehicle routing problems, including exact and heuristic methods. Due to the exponential increase in computational complexity with the number of cities or nodes, exact methods are time and resources intensive. Hence, a heuristic approach is adopted if it satisfies the problem's constraints and conditions.To ensure a user-friendly experience and leverage existing expertise with MS Excel, an intelligence system will be developed using Visual Basic for Applications (VBA). VBA, a programming language designed for automation within the Microsoft Office suite, enables seamless integration into Excel, facilitating a familiar and intuitive interface for users.The accuracy of the algorithm will be evaluated by comparing the results generated by the intelligent system with historical data manually planned by human planners. This assessment will quantify planning utilization based on the number of trailers used, enabling an evaluation of the algorithm's performance.By automating and optimizing the route planning process, this research aims to reduce the reliance on manual planning and enhance overall efficiency and resource utilization in the finished vehicle logistics business.
DOI: 10.14457/tu.the.2022.807
title: LP-based heuristics for multi-objective capacitated vehicle routing problem and arrival time prediction method for delivery trucks

description: A Delivery Planning and Scheduling (DPS) system is very important for logistics service providers and manufacturers that own a fleet of vehicles. This research develops a DPS system for Small- to Medium-sized Enterprise (SME) companies to plan delivery routes of vehicles and schedule arrival times at customers. The DPS system is developed based on two methods, including a Linear Programming-based Cluster-First Route-Second (LP-based CFRS) heuristic and a Delivery Route and Arrival Time Planning (DRATP) method.First, the LP-based CFRS heuristic is proposed to efficiently plan delivery routes of vehicles, for a Multi-Objective Capacitated Vehicle Routing Problem (MOCVRP). The objectives of the MOCVRP include (1) minimizing the total fleet operating costs (i.e., fuel consumption cost and driver overtime cost), (2) Assigning drivers to deliver and assist customers who have high personal relationships with them, (3) balancing delivery weights among vehicles, and (4) minimizing the amount of carbon dioxide emissions. Four LP clustering models, including three individual clustering models and one compromise-solution clustering model, are developed to group customers into clusters. One LP routing model is developed, to sequence customers in each cluster to form a delivery route that starts and ends at the depot.Then, the DRATP method is proposed to accurately schedule arrival times at customers for each vehicle, considering road restrictions, traffic congestion, travel times with traffic conditions, and drivers’ behaviors. Three techniques involved drivers’ behaviors are proposed to improve the accuracy of arrival times. The DRATP method allows interactive decisions between planners and application software.From a real case study, the results show that the proposed LP-based CFRS heuristic efficiently plan delivery routes of vehicles. It compromises the four objectives and requires a reasonable computational time. However, the proposed LP-based CFRS heuristic does not provide accurate arrival times at customers, since it uses constant travel times. From another real case study, the results show that the proposed DRATP method accurately determines arrival times at customers for each vehicle. The DRATP method that considers drivers’ behaviors improves the accuracy of arrival times about 50% over the DRATP method without considering drivers’ behaviors.
DOI: 10.14457/tu.the.2022.809
title: A collaborative knowledge framework for personalized attention deficit hyperactivity disorder (ADHD) treatments

description: Attention Deficit Hyperactivity Disorder (ADHD) is a complex mental health disorder that can affect your child's success at school and his/her relationships. The symptoms of ADHD vary and are sometimes difficult to recognize. Many of the individual symptoms of ADHD are normal for children to experience. Evaluating the child under several criteria is necessary to make a diagnosis of ADHD. Generally, the diagnostic will conduct on children by the time they are teens, the average age of diagnosis is 7. Older children exhibiting these symptoms may have ADHD, but they often have exhibited rather than elaborate symptoms early in life. Attention Deficit Hyperactivity Disorder (ADHD)is a complex mental health disorder that can affect your child's success at school and his/her relationships. The symptoms of ADHD vary and are sometimes difficult to recognize. Many of the individual symptoms of ADHD are normal for children to experience. Evaluating the child under several criteria is necessary to make a diagnosis of ADHD. Generally, the diagnostic will conduct on children by the time they are teens, the average age of diagnosis is 7. Older children exhibiting these symptoms may have ADHD, but they often have exhibited rather than elaborate symptoms early in life. To further explain the above statement, the problems are summarized as follows: 1) lack of analysis framework for ADHD children, 2) lack of tools for recording behavioral symptoms of ADHD children include to and 3) the analysis takes longer time for children under 6 years old. Most of the time they are influenced by surrounding activities. The above diagnosis is rather difficult because the disease is rising gradually over a period, the disease can occur at the age of 3-7 years. For a person who receives a diagnosis of ADHD, the symptoms of inattention and hyperactivity-impulsivity must be chronic or long-lasting, impair the person’s functioning and cause the person to fall behind normal development for his/her age. The treatment of ADHD uses behavioral therapy (BT) and medication such as methylphenidate, dextroamphetamine, and pemoline statins for reducing restless children and helping them to concentrate on their studies and work. The main purpose of this research is to design a collaborative knowledge framework for personalized ADHD treatments. There are two main objectives, which are 1)to design a framework and develop a tool for observing and recording behavioral symptoms of ADHD children that can be used by doctors, parents, and teachers, and 2) to introduce effective algorithms for classifying ADHD types with appropriate individual behavioral therapy and activities recommendation. In our framework, we introduce a combined technique for ADHD classification using a machine learning approach. The expected outcome of our proposed framework is to provide an effective way to classify types of ADHD and to recommend appropriate treatments and therapy based on individual behavior
DOI: 10.14457/tu.the.2022.810
title: Bed position classification using bed sensor panel for preventing falls and prolonged bed rest

description: The rapid growth of the elderly population promotes the need for geriatric care. Due to the shortage of care workers, elderlies have to spend most of their time alone. This leads them to face potential risks around the bed without any assistance. Falls are major problems in nursing home care. These mishaps are related to seriously injured issues such as bruises, soreness, and bone fractures. Falling from a bed often occurs immediately after getting out of bed or coming close to the edge of a bed. Moreover, a lack of repositioning of the body of a bedridden elderly person may cause bedsores.In order to avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. Therefore, bed fall has become an active area of research, particularly bed fall detection. Wearable devices and vision sensors have some constraints on the comfort and privacy of a subject. As a result, a non-constraint sensing device, which is embedded in the environment, is a suitable approach for continuously monitoring elderly activity. For fall prevention, the system needs to detect the position of lying with respect to the edge of the bed. Moreover, for bedsore prevention, the duration of a person lying in exactly the same position for a long time can be monitored.In this study, we proposed a bed position classification based on feature exploration and deep learning methods on signals from the panel. It is installed on the bed under the mattress. The panel sensor is embedded with the four sensors, composed of two piezoelectric sensors and two pressure sensors. The bed positions are classified into five classes, i.e., off-bed, sitting, lying center, lying left, and lying right. The off-bed and sitting positions are highly significant for detecting the bed exit activity because they are normally the positions just before or after the bed exit. The in-bed positions are important to prevent falls when the elderly patient moves toward the edge of the bed. The system will also alert the caregiver to turn the elderly patient’s body when staying in the same position for more or less two hours to prevent bedsores. The findings of our study contribute to bed falls and bedsore prevention in the elderly.
DOI: 10.6084/m9.figshare.24190770
title: Dataset : <b>Exploring Potential Actions of </b><b><i>Peganum harmala</i></b><b> Seeds: In Silico and In Vitro Investigations</b>

description: This dataset contains all of the data that was used to generate the figures and tables in the manuscript. The raw dataset is in a excel format and can be opened with any spreadsheet software.
DOI: 10.6084/m9.figshare.24190770.v2
title: Dataset : <b>Exploring Potential Actions of </b><b><i>Peganum harmala</i></b><b> Seeds: In Silico and In Vitro Investigations</b>

description: This dataset contains all of the data that was used to generate the figures and tables in the manuscript. The raw dataset is in a excel format and can be opened with any spreadsheet software.
DOI: 10.6084/m9.figshare.24190770.v1
title: ITEM II : Dataset

description: This dataset contains all of the data that was used to generate the figures and tables in the manuscript. The raw dataset is in a excel format and can be opened with any spreadsheet software.
DOI: 10.6084/m9.figshare.24190896
title: <b>Tabla 9: Rúbrica de evaluación</b>

description: <b>Tabla 9: Rúbrica de evaluación</b>
DOI: 10.6084/m9.figshare.24190896.v1
title: <b>Tabla 9: Rúbrica de evaluación</b>

description: <b>Tabla 9: Rúbrica de evaluación</b>
DOI: 10.17179/excli2023-6296/supplementary_data
title: Risks of obesity and diabetes development in the population of the Ivano-Frankivsk region in Ukraine

description: The epidemic of obesity that parallels diabetes mellitus and its complications are diseases of major concern to modern societies. Community-based screening is an effective strategy to identify people at high risk of developing overweight, obesity, prediabetes, diabetes, and related health problems. Here, we present the results of screening the population of four locations in the Ivano-Frankivsk region (Western Ukraine). The study group consisted of 400 adults and 252 children. The measured parameters were: (1) main vital signs – body temperature, resting heart rate, blood pressure; (2) anthropometric indicators – body mass and height, body mass index, waist circumference; and (3) metabolic parameters – fasting capillary blood glucose, total body fat, visceral fat, physical activity level and 10-year risk of developing type 2 diabetes. The study found that 23 % of the adults were overweight and 14.8 % obese. Among children, 9.9 % were overweight and 8.7 % obese. Adult body mass index correlated with visceral fat percentage, systolic/diastolic blood pressure and levels of fasting capillary blood glucose. Adults over 18 years of age had fasting capillary blood glucose ≥5.6 mmol/L (14.3 %), including those with undiagnosed pre-diabetes (13.3 %) and suspected diabetes mellitus (1.0 %). The percentage of visceral body fat in adults was positively associated with the 10-year risk of developing type 2 diabetes.
description: EXCLI Journal; 22:Doc1047; ISSN 1611-2156
DOI: 10.6084/m9.figshare.24182949
title: Data from: <b>Reduced tillage intensity does not increase arbuscular mycorrhizal fungal diversity in long-term experiments across Europe</b>All data used in the analyses of this manuscript is presented

description: All data used in the analyses of this manuscript is presented. Metadata and corresponding virtual taxon table.
DOI: 10.6084/m9.figshare.24182949.v1
title: Data from: <b>Reduced tillage intensity does not increase arbuscular mycorrhizal fungal diversity in long-term experiments across Europe</b>All data used in the analyses of this manuscript is presented

description: All data used in the analyses of this manuscript is presented. Metadata and corresponding virtual taxon table.
DOI: 10.6084/m9.figshare.24190944
title: <b>Tabla 9: Rúbrica de evaluación</b>

description: <b>Tabla 9: Rúbrica de evaluación</b>
DOI: 10.6084/m9.figshare.24190944.v1
title: <b>Tabla 9: Rúbrica de evaluación</b>

description: <b>Tabla 9: Rúbrica de evaluación</b>
DOI: 10.6084/m9.figshare.24125616.v1
title: Supplementary Material for: Association between sleep and Alzheimer’s disease: A bibliometric analysis from 2003 to 2022

description: Introduction: Alzheimer’s disease (AD) often presents with sleep disorders, which are also an important risk factor for AD, affecting cognitive function to a certain extent. This study aimed to reveal the current global status, present hotspots, and discuss emerging trends of sleep and AD using a bibliometric approach. Methods: Research and review articles related to sleep and AD from 2003 to 2022 were extracted from the Web of Science Core Collection. VOSviewer 1.6.18.0, Scimago Graphica, and CiteSpace 6.2.R2 were used to map the productive and highly cited countries, institutions, journals, authors, references, and keywords in the field. Results: Overall, 4,008 publications were included in this bibliometric analysis. The number of publications and citations showed an increasing trend over the past two decades. The USA and China had the largest and second largest, respectively, number of publications and citations and cooperated with other countries more closely. Ancoli-Israel Sonia published the most papers, and Holtzman David M was co-cited most frequently. The most productive journal was Journal of Alzheimer’s Disease, and Neurology was the most frequently cited journal. The risk factors, β-amyloid, tau, neuroinflammation, astrocytes, glymphatic system, orexin, functional connectivity, and management have been the main research directions of researchers over the past few years and may be the future trend of valuable research. Conclusion: We identified hotspots and emerging trends including risk factors, Aβ, tau, neuroinflammation, the glymphatic system, orexin, and management, which may help identify new therapeutic targets and improve clinical efficacy of sleep and AD.
DOI: 10.57745/yjv4oq
title: phenotyping_data_gnotobiotic_Acar_ et al 2023.ods

description: Phenotyping data of symbiotic and aposymbiotic D. sansibarensis under gnotobiotic conditions. Raw data used in Acar et al. 2023.
DOI: 10.57745/fm87zz
title: phenotyping_data_greenhouse_Acar et al 2023-corrected.ods

description: Legend added and headers translated to English.
DOI: 10.57745/gc7bl4
title: phenotyping_data_gnotobiotic_Acar_ et al 2023-corrected.ods

description: Spreadsheet with legend added and headers translated to English.
DOI: 10.57745/omorzc
title: phenotyping_data_greenhouse_Acar et al 2023.ods

description: Automated phenotyping data of Dioscorea sansibarensis - greenhouse conditions
DOI: 10.57745/oo1ekd
title: phenotyping_data_greenhouse_Acar et al 2023-corrected.ods

description: Legend added and headers translated to English.
DOI: 10.57745/rjmxt7
title: phenotyping_data_gnotobiotic_Acar_ et al 2023-corrected.ods

description: Spreadsheet with legend added and headers translated to English.
DOI: 10.57745/24pkvj
title: Readme.txt

description: Description of experimental design and key to variable names.
DOI: 10.6084/m9.figshare.24190413
title: PLINK files (ped and map) of wild and domestic goat species

description: We compiled the comprehensive SNP dataset of wild and domestic goats. Samples were genotyped with Illumina Goat SNP50 BeadChip. We complemented those genotypes with published data from animals that were genotyped with the same SNP chip. Complete dataset contain whole genome SNP data of 613 individuals from 10 species, 8 subspecies, and 26 breeds including the outgroup. There are 48.311 autosomal SNPs with a call rate greater than 95%, any other filtering steps, phasing or imputation were not performed on this data. In compressed folder are genotypes in Plink format and a table with samples' information.Publication: Pogorevc N., Dotsev A., Upadhyay M., Sandoval-Castellanos E., Hannemann E., Simčič M., Antoniou A., Papachristou D., Koutsouli P., Rahmatalla S., Brockmann G., Sölkner J., Burger P., Lymberakis P., Poulakakis N., Bizelis I., Zinovieva N., Horvat S. and Medugorac I. <b>Whole-genome SNP genotyping unveils ancestral and recent introgression in wild and domestic goats. </b><i>Molecular Ecology</i>