The paper presents the results of research on the influence of logistics customer service on sustainability-focused freight transport practices of enterprises. Additionally, the extended perspective of the key relation through the inclusion of the joint effect of selected organizational competencies of the companies and their competitiveness in interaction with logistics customer service was introduced. The adopted research procedure included the use of several different statistical methods with regard to data collected in 275 freight transport enterprises. First, the Kaiser-Meyer-Olkin test and the Bartlett Sphericity test were determined, then a factor analysis was carried out with the intention of performing a reliability analysis and discriminant validity assessment, and finally, correlations and hierarchical multiple regression were determined. The findings of the research suggest a primal concluding explication that sustainability-focused freight transport practices are conditioned by auxiliary logistics processes realized by the enterprise within logistics customer service, joint competencies within the organization’s management, as well as peripheral circumstances of competitiveness.

The paper aims towards understanding the factors responsible for the adoption of green products by consumers in the Indian market. A well-structured survey questionnaire was developed and circulated to 327 participants. Various factors associated with the adoption of green products were taken into account. They were analyzed by using descriptive statistics and factor analysis. The paper concludes that 61.2% of the respondents were willing to adopt green products and the 77.1% of the respondents are aware of green products and various benefits associated with them. An effort was made to understand the influence of green labelling on the adoption of green products by consumers. From the survey, it was inferred that green labelling is an important tool used by consumers for verifying and procuring green products. It was also found that factors like “concerns about the environment” and “recommendations from family and friends” significantly influence consumer's purchase decisions about green products.

When tourists choose their accommodation space, they tend to prefer areas with beautiful natural landscapes, or close to scenic spots, or those that have local styles. Based on existing research results of tourist accommodation space, some scholars proposed to design smaller spaces for tourist accommodation, however, in terms of the spatial correlation with tourism resources, relevant analysis is insufficient and needs to be supplemented. This paper studied the distribution characteristics and spatial correlation of tourist accommodation spaces based on environment information. At first, the paper analyzed the correlation between tourism resources and tourist accommodation spaces, gave the route of spatial correlation analysis, and analyzed the distribution characteristics of tourist accommodation spaces; then, this paper gave the determination process of optimal spatial distribution pattern of tourist accommodation spaces based on environment information, and adopted the kernel density estimation and the spatial attributes of tourist accommodation space points to study the spatial distribution characteristics. At last, combining with experiment, the distribution characteristics of the tourist accommodation spaces in the study area were given and analyzed.

The natural resources that exist on this small island have the potential as a tourism destination. The purpose of this research is to be able to develop the potential of community-based tourism. The method used to collect data is observation and in-depth interviews with key figures, then the data is analyzed descriptively and analysis of component 4(A) namely attraction, accessibility, amenity, and ancillary of Tourist Attractions. The results identified are land use patterns, tourist attractions include beach tourism, mangrove tourism, underwater tourism (snorkelling), special interest tours for langurs, cycling tours (around the island), climbing tours, and religious tourism. Existing accessibility includes roads, boats, and piers. The amenities include places of worship, public toilets, food stalls, lodging, and snorkeling equipment rental services. Additional or ancillary facilities that exist are an information center located at the village office and a tour guide. The need for human resource training is also very much needed to shape community-based tourism to be much better.

The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.

Digital learning is the use of telecommunication technology to deliver information for education and training. As the increased acceleration of the propagation speed of the web, a lot of data collected by automated or semi-automated way. The 4s (Volume, Velocity, Variety and Veracity) of big data increase the challenge to extract useful value via systemic framework. This study aims to construct the data model of big data digital learning. Based on the digital learning data, data-driven innovation framework was proposed to identify data form and collect data. Bayesian network was proposed to capture learning model to extract user experience of students to enhance learning efficiency. Empirical study was conducted on a university to validate the proposed approach. The results have been implemented to support the strategies to improve student learning outcomes and competitiveness.

This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

To analyses the impact of high mobility, dynamic topologies, scalability and routing due to the more dynamic changes in network. To enhance mobile Ad-hoc network (MANET) self-organization capabilities by geographical routing algorithm during mobility. In this paper, a survey has been carried out on geographic routing protocols, such as hybrid routing, Greedy Routing, face-2 Algorithm, Perimeter Routing, quasi random deployment (QRD) techniques and time of arrival (TOA). An optimized multipath routing in wireless sensor network (WSN), energy utilization, detection of anonymous routing, node mobility prediction, data packet distribution strategies in WSN is analyzed. Geographic routing offers previous data packet information such as physical locations, packet elimination dependencies, storage capacity of topology, Associate costs and also identifies the dynamic behavior of nodes with respect to packets frequencies.

There is an enormous amount of data present in many different formats, including databases (MsSql, MySQL, etc.), data repositories (.txt, html, pdf, etc.), and MongoDB (NoSQL, etc.). The processing, storing, and management of the data are complicated by the varied locations in which the data is stored. If combined, this data from several sites can yield a lot of important information. Since many researchers have suggested different methods to extract, examine, and integrate the data. To manage heterogeneous data, researchers propose data warehouse and big data as solutions. However, when it comes to handling a variety of data, each of these methods have limitations. It is necessary to comprehend and use this information, as well as to evaluate the massive quantities that are increasing day by day. We propose a solution that facilitates data extraction from a variety of sources. It involves two steps: first, it extracts the pertinent data, and second, then to identify the machine learning algorithm to analyze the data. This paper proposes a system for retrieving data from many sources, such as databases, data sources, and NoSQL. Later, the framework was put to the test on a variety of datasets to extract and integrate data from diverse sources, and it was found that the integrated dataset performed better than the individual datasets in terms of accuracy, management, storage, and other factors. Thus, our prototype scales and functions effectively as the number of heterogeneous data sources increases.

Several scientific reports indicate lower as well as higher relative yield stability in organic and conventional (chemical) agriculture systems. This study presents the results of on-farm trials conducted on leafy vegetables grown in organic and conventional management systems. Four leafy vegetables collard green (Brassica oleracea cv. acephala), kale (Brassica oleracea cv. sabellica), lettuce (Lactuca sativa) and swiss chard (Beta vulgaris L. cv. cicla) were grown in organic and conventionally managed plots in the spring 2018 and 2020. United States Department of Agriculture (USDA), National Organic Program (NOP) standards were followed for cultural and management practices in organically managed experimental field plots. Synthetic chemical fertilizer was applied in the experimental field plots managed in the conventional production system. Data on plant height, leaf number and total fresh weight of leafy vegetables were measured at the end of the experiment. There was no difference in plant height and number of leaf count between the two production systems for all four crops. Collard was the tallest in the organic system in both years, kale in 2018 and collard in 2020 were tallest in the conventional system while lettuce was the shortest in both the production systems. In terms of leaf number, organic kale had the highest leaf number; however, all other crops have the same number of leaves. In organic production, lettuce fresh weight was significantly higher than the collard and similar to the rest of the crops. In conventional production, kale fresh weight was significantly higher followed by collard, swiss chard and lettuce. Moreover, lettuce fresh weight was significantly higher in organic than conventional system, no difference was recorded for swiss chard between two systems while collard and kale fresh weight was significantly higher in conventional than organic production. Our results suggest that the organic system can be a best choice for lettuce and conventional system is best choice for collard and kale.

The logistics industry faces many challenges, such as low efficiency and transparency, and data cannot be updated in real time. Digital twin in logistics is regarded as a new technology that can lead the further development of logistics. It can realize the integration of logistics entity and virtual environment in the logistics process to improve transparency and reduce risks. Therefore, in recent years, it has aroused widespread concern, and many researchers have studied the application of digital twins in the field of logistics. However, there are still some problems in the practical application process. This study aims to analyze the current status of digital twin citation in the logistics industry, and comprehensively review the application and limitations of DT with a systematic evaluation method. After careful searching of the database, fourteen related literatures were selected for key classification and analysis. This research shows that Digital Twin has the ability to solve some challenges in the field of logistics.

The integrated scheduling of production and maintenance can make equipment maintenance in line with the production pace, so as to effectively prevent anormal interruptions of the production process due to equipment failure, and ensure the smooth implementation of the production scheduling plan. Aiming at the parallel machine job-shop environment, and considering stochastic machine failures and different degradation speeds of parallel machines, this paper introduced the minimal maintenance and preventive maintenance strategies to establish an integrated scheduling model for production and maintenance, designed a genetic algorithm based on process coding and binary hybrid coding to solve the model, and verified the correctness of the proposed model and the effectiveness of the algorithm through an instance. This study provided an effective decision-making method for parallel machine job-shop scheduling problems.

Human societies and researchers ensued that the continuation of a one-dimensional development focused on economic benefits can endanger the survival and tranquility of humanity, after experiencing a period of economic development and due to the advantages and disadvantages of this type of development. Concerns and damages of the environment and social challenges have led to the evolution of a three-dimensional concept of development based on economy, environment and society being known as sustainable development. Due to different indicators in each dimension of sustainability, finding effective ones is substantial. Supply chains are one of the most important and comprehensive domains in which sustainability led to better integration of layers and improve the total performance. On the other hand, current literatures demonstrate serious gap in representing comprehensive and integrated guidelines in order to optimize environmental and social indicators impacts in the management of supply chain. In this paper, all possible indicators for sustainability are collected, mapped into the layers of supply chain and inserted to a proposed mathematical model. The outputs are the effective indicators in three dimensions of sustainability for all layers of supply chain maximizing the sustainability of the whole supply chain. The proposed approach is implemented in a fishery supply chain.

The production process usually involves several processes that are divided into production lines. The processes in this production line affect the costs incurred by the company. From the analysis results, the costs arising from production line activities are very high. Therefore, the company strives to reduce production costs by paying attention to the aspects that result in the emergence of waste. The method used is by combining the process on the machining line. This study was conducted to find out the effect of combining process lines on production efficiency. The results of this study are expected to be an input in determining production planning in the enterprise. This study didn’t use sampling. From the results of the study, there was an increase in the daily production of gear A (0.86%) and gear B (1.12%). From this merger, the company was able to optimize manpower and cut production WIP storage areas.

To make railway systems more autonomous and energy efficient, the suction phenomenon induced by virtual coupling (VC) can be considered as a beneficial source of energy saving since trains are very closely spaced. A minimum safe distance between railway systems must be defined and maintained to ensure the safety of the whole convoy. The purpose of this paper is to study and quantify the aerodynamic gain in case of VC of two modular and autonomous trains ‘Smart Cabins’ as designated in our project. Computational fluid dynamics simulations are investigated to analyze the aerodynamic effect under several scenarios by varying the inter-cabins distance. Some design simplifications have been made for each Smart Cabin to prepare simulations and reduce computation time. Simulation results confirm the interest of VC in the sense of reducing coefficient drag of the full convoy up to 27%, which reflects a power gain of about 4% of the total traction power required for a single Smart Cabin (~200 kW).

Due to the climate crisis, extreme fluctuations in temperature are caused by the high sources of energy, and carbon consumption have a great impact on both construction and water resources management. Accordingly, the world today is paying attention to searching for cleaner energy resources. In Egypt, the extreme heat of the summer seasons causes constant air-conditioner (AC) usage in building to provide cooling, which produces outlet wastewater. The continuous flow of this outlet wastewater results in cracks and erosion among the facades that need maintenance.

As a way to search for environmentally friendly material that can reuse this outlet water and reduce the solar radiation on the façade providing more cooler spaces, algae are suggested due to their availability in water from several sources in Egypt. This paper presents the assessment of an innovative façade element photobioreactors (PBR) made from algae on a real administrative building facade in Cairo. The aim is to evaluate digitally by simulation the solar radiation reduced from the façade that acts as a double green skin and self-watering system with an appealing aesthetic form preventing any erosion on the façade surfaces.

The method of assessment is done using Ladybug plug-in simulation in Grasshopper plug-in in Rhino software based on the climatic data from EnergyPlus. Four main phases are followed: 1) the form generation of the façade using Rhinoceros software, 2) the simulation to assess the solar radiation before and after adding the PBR, 3) the evaluation phase to calculate the thermal conductivity and water temperature mathematically, and 4) fabrication of small-scale façade using the 3D-printed technique with algae filament.

The results recorded a reduction in the solar radiation from 301 to 75 kWh/m² comparing the current case of the building façade, while the thermal performance was 0.36 W/m²K, which is better than the most common materials used in arid climates such as rammed earth, fired brick, and concrete. The optimization of the algae tube length was based on the required outlet temperature that is suitable for plantation 15°C to help in reducing the water temperature.

The finding addresses the significant role of using algae that can generate biomass to explore their benefits regarding their O₂ production and CO₂ absorption through 3D printing, which is considered a cleaner technology.

As freight transportation demand increases worldwide, railway practitioners must carefully manage the capacity of existing facilities to ensure efficient and reliable operations. Railroad gravity hump classification (marshalling) yards, where individual railcars (wagons) are sorted into new trains to reach their destination, are an integral part of the freight rail network. Efficient operation of yard processes is critical to overall freight railway performance as individual carload shipments moving in manifest trains spend most of their transit time waiting for connections at intermediate yards, with more than half of this waiting time spent dwelling on classification bowl tracks. Previous research has developed optimal strategies to allocate bowl tracks to blocks for a given set of yard track lengths. However, these strategies make simple assumptions about the performance impact of over-length blocks due to a lack of basic analytical models to describe this relationship. To meet this need, this paper develops an original hump classification yard model using AnyLogic simulation software. A representative yard with accurate geometry and operating parameters reflecting real-world practice is constructed using AutoCAD and exported to AnyLogic. The AnyLogic discrete-event simulation model uses custom Java code to determine traffic flows and railcar movements in the yard, and output performance metrics. With complete flexibility to change track layout patterns, a series of simulation experiments quantify fundamental classification yard capacity relationships between performance metrics and the distribution of track lengths, as a function of the railcar throughput volume and size of outbound blocks created in the yard. The resulting relationships are expected to better inform railway yard operating strategies as traffic, train length, and block size increase but yard track lengths remain static.

Monitoring systems are a key tool to improve the safety of railway vehicles and to support maintenance activities. Their on-board application on railway vehicles is currently well established on newly built passenger vehicles, while their use on freight vehicles is not yet sufficiently widespread. This is due to the complex management of the operating procedures of the freight wagons, to the substantial impact of the cost of these systems compared to the cost of the wagon and to the common lack of electrification on freight wagons.

This work illustrates the characteristics of a monitoring system developed at Politecnico di Torino and previously installed on freight vehicles and operationally tested as regards the detection of accelerations and temperatures as diagnostic parameters. This system has been improved by adding diagnostics of the vehicle braking system, in order to detect anomalies during braking operations and to support maintenance procedures. The activity described in the present work aims to identify, beyond the specific diagnostic system that has been implemented, the basic characteristics that a modern monitoring system, intended to be installed on railway freight wagons, should feature. The new version of the monitoring system that has been developed at Politecnico di Torino has been preliminarily tested on a scaled roller-rig in order to monitor the braking system even in abnormal operating conditions, which would be difficult to reproduce safely on a real vehicle. The monitoring system is equipped with an axle generator capable of autonomously supporting its operation, and it is also provided with a diagnostic information processing system and communication protocols to send outside this information.

Computer-aided engineering (CAE) refers to software applications aimed at helping solve technological problems through numerical methods. Exploiting CAE, it is possible to evaluate determined systems through virtual models rather than physical prototypes. By doing so, useful information on the system’s performance can be gathered at the beginning of the design phase, when the modifications to the project cost less. In the field of lubrication and efficiency, computational fluid dynamics (CFD) has been applied to geared transmissions, leading to an important step forward in the understanding of multiphase physics and the optimization of the systems’ layout. Being the simulations of gears non-stationary, the topological changes of the domain require the adoption of mesh-handling strategies capable of accomplishing the boundaries’ rotation. In this analysis, the Global Remeshing Algorithm with Mesh Clustering (GRAMC), previously developed by the authors to reduce the computational time associated with the remeshing process, is applied to study dip and injection lubrication in helical and spur gearboxes. The results suggest that this methodology is an effective and efficient solution to analyse the lubrication and the efficiency even for complex kinematics. The investigation was conducted in the OpenFOAM framework.

In turning processes, cutting force is of great importance since many cutting force features are useful for predicting and detecting tool conditions. To precisely measure cutting forces, many commercial devices have been developed; however, they are costly, cumbersome, and some implementation restrictions could hinder their suitability in real applications. In this work, a simple, portable, and low-cost tool holder sensor was designed and developed to sense strain and measure cutting force applied during ultra-precision diamond turning. The device can assess cutting intensity up to 3 N with a high sensitivity of 4.592 mV/N or 0.004592 V/N, a calibration test variability of 99.6%, and a percentage error of 2.19, according to static calibration testing.

Parametric accelerated life testing (ALT) with the reliability quantitative (RQ) specifications is recommended as reliability methodology to pinpoint design flaws and correct them in transit. It covers (1) cycles of an accumulated failure rate of X% (BX) lifetime with ALT strategy, (2) fatigue design, (3) ALTs with alterations, and (4) discernment if design(s) obtains targeted BX life. The quantum/transport-based (generalized) life-stress failure prototype and sample size formulation for generating RQ specifications were suggested. The equivalent elevated damage potential in parametric ALT was applied, represented by field power spectral density. A case study was used to evaluate a refrigerator fatigued during rail. In first ALT, for RQ specifications – 40 min, refrigerator tubes made of ethylene propylene diene monomer rubber fractured because of mount designs. The failed shape in first ALT was alike to those of the field refrigerator. After mounts and tubes were redesigned, there were no difficulties during second ALT. Refrigerator was assured to fulfill a B1 lifetime for travel distance.

In many real-world applications, it is more realistic to predict a price range than to forecast a single value. When the goal is to identify a range of prices, price prediction becomes a classification problem. The House Price Index is a typical instrument for estimating house price discrepancies. This repeat sale index analyzes the mean price variation in repeat sales or refinancing of the same assets. Since it depends on all transactions, the House Price Index is poor at projecting the price of a single house. To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net regression, with the aid of machine learning with feature selection. The proposed prediction model for house prices was evaluated on a machine learning housing dataset, which covers 1,460 records and 81 features. By comparing the predicted and actual prices, it was learned that our model outputted an acceptable, expected values compared to the actual values. The error margin to actual values was very small. The comparison shows that our model is satisfactory in predicting house prices.

Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity. In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production. The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set. The training and test results of the suggested approach are presented visually on a tensor board. With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use. According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.

Osteoporosis is a common systemic bone disease with insidious onset and low treatment efficiency. Once it occurs, it will increase bone fragility and lead to fractures. Computed tomography (CT) is a non-invasive medical examination method that can identify the bone condition of patients. In this paper, we propose a novel channel attention module, which is subsequently integrated into the supervised deep convolutional neural network (DCNN) termed DSNet, which can perform feature fusion from two different scales, and use the method of quadratic weight calculation to enhance the interconnection among feature map channels and improve the detection and classification performance for the bone condition in lumbar spine CT images. To train and test the proposed framework, we retrospectively collect 4805 CT images of 133 patients, using DXA as the gold standard. According to the T-value diagnostic criteria defined by WHO, the vertebral bodies of L1 - L4 in CT images are labeled and classified into osteoporosis, osteopenia and normal bone mineral density. Meanwhile, the training set and test set are constructed in the ratio of 4:1. As a result, the DSNet achieves a prediction accuracy of 83.4% and a recall rate of 90.0% on the test set, indicating that the proposed model has the potential to assist clinicians in diagnosing individuals with abnormal BMD and may alert patients at high risk of osteoporosis for timely treatment.