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Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

The purpose of this study is to investigate the transport phenomena of smoke flow in a semi-open vertical shaft.

The large eddy simulation (LES) method was used to model the movement of fire-induced thermal flow in a full-scale vertical shaft. With this model, different fire locations and heat release rates (HRRs) were considered simultaneously.

It was determined that the burning intensity of the fire is enhanced when the fire attaches to the sidewall, resulting in a larger continuous flame region in the compartment and higher temperatures of the spill plume in the shaft compared to a center fire. In the initial stage of the fire with a small HRR, the buoyancy-driven spill plumes incline toward the side of the shaft opposite the window. Meanwhile, the thermal plumes are also directed away from the center of the shaft by the entrained airflow, but the inclination diminishes as HRR increases. This is because a greater HRR produces higher temperatures, resulting in a stronger buoyancy to drive smoke movement evenly in the shaft. In addition, a dimensionless equation was proposed to predict the rise-time of the smoke plume front in the shaft.

The results need to be verified with experiments.

The results could be applied for design and assessment of semi-open shafts.

This study shows the transport phenomena of smoke flow in a vertical shaft with one open side.

This paper aims to examine the effect of the Sarbanes–Oxley Act (SOX), which was signed by President George W. Bush and came into effect on July 30, 2002, on firm productivity.

The authors use the total factor productivity (TFP) as our measure of firm productivity.

Analyzing annual firm-level data from the Compustat database for the period of 1991-2006, the authors find that firm productivity increases at a higher rate in the post-SOX period. The results indicate that, although firms incur significant costs in complying with the requirements of the SOX, they also benefit from these requirements as evidenced by the improved productivity over time post-SOX. There is also a shift in the output elasticities from capital toward labor. The SOX has a positive effect on the output elasticity of labor but a negative impact on that of capital.

The results have the following important implications. The SOX is a value-enhancing regulation in that it not only strengthens a firm’s corporate governance but also improves its productivity. However, compliance with the SOX can impose a long-term cost on firms: the decrease in the capital investment, leading to a decline in the output elasticity of capital. If this decline in the capital investment continues, it can have an adverse effect on firm productivity in the long term.

This paper extends the literature along the line of the actual operational effects of the SOX regulation by examining its effect on the productivity of firms.

The purpose of this study is to identify the core responsible leadership (RL) practices that are most relevant to small- and medium-sized enterprises (SME) restaurants. Furthermore, the authors adapt scales to measure these practices and conduct a pilot study to evaluate their impact on business performance in such establishments.

Exploratory sequential mixed methods are used to fulfill the research aims. In the first phase, a set of definitions and practices associated with RL are derived from a systematic literature review. Second, a projective method of data collection is applied, involving a panel of 16 experts. Third, a fuzzy cognitive map is developed, which captures the responses of 40 owners or general managers of SME restaurants.

Twenty-five practices are identified from the systematic literature review. The results show the five leadership practices that match the order of importance assigned by the experts: societal orientation, ethics, stakeholder involvement, power-sharing and environmental orientation. The relevance of those five practices is validated to explain SME restaurants’ financial performance and innovation performance.

Innovation is the key to advancing business sustainability and resilience, and the results identify the specific RL practices that enable improvements to be made in innovation performance among SME restaurants.

This paper identifies the RL practices that are particularly relevant to the tourism field (specifically, the restaurant industry), offers measurement scales for those practices and provides empirical evidence of the relationship between these RL practices and business performance in SME restaurants.

Advanced medical technology has reduced the mortality rate among traumatic brain injury (TBI) patients. This, however, has led to an increasing number of surviving patients with a major disability. As a consequence, these patients need attentive care which becomes an important issue for the society, particularly family members. Thus, this paper aims to review some of the salient roles, challenges and needs of the family caregivers in caring or nursing for their family members diagnosed with TBI.

An inclusive search of the literature was undertaken to identify the family roles, challenges and needs in supporting and nursing TBI patients.

Previous studies have shown that the family needs to address two important aspects of taking care of TBI patients, which involve emotional and physical affairs. Hence, it is essential for the family members to have adequate information on healing treatment, nursing and care methods, financial support, support groups, managing self-care and, more importantly, emotional and social support.

This paper is not currently under consideration, in press or published elsewhere. In Malaysian culture, nursing disabled patients have always been a family responsibility. The role of nursing the patients has been done domestically and is considered a private affair. In order to execute the role, some put the patient needs as their priority and leave aside their needs and matters.

In response to food supply constraints resulting from coronavirus disease 2019 (COVID-19) restrictions, in the year 2020, the project developed automated household Aquaponics units to guarantee food self-sufficiency. However, the automated aquaponics solution did not fully comply with data privacy and portability best practices to protect the data of household owners. The purpose of this study is to develop a data privacy and portability layer on top of the previously developed automated Aquaponics units.

Design Science Research (DSR) is the research method implemented in this study.

General Data Protection and Privacy Regulations (GDPR)-inspired principles empowering data subjects including data minimisation, purpose limitation, storage limitation as well as integrity and confidentiality can be implemented in a federated learning (FL) architecture using Pinecone Matrix home servers and edge devices.

The literature reviewed for this study demonstrates that the GDPR right to data portability can have a positive impact on data protection by giving individuals more control over their own data. This is achieved by allowing data subjects to obtain their personal information from a data controller in a format that makes it simple to reuse it in another context and to transmit this information freely to any other data controller of their choice. Data portability is not strictly governed or enforced by data protection laws in the developing world, such as Zimbabwe's Data Protection Act of 2021.

Privacy requirements can be implemented in end-point technology such as smartphones, microcontrollers and single board computer clusters enabling data subjects to be incentivised whilst unlocking the value of their own data in the process fostering competition among data controllers and processors.

The use of end-to-end encryption with Matrix Pinecone on edge endpoints and fog servers, as well as the practical implementation of data portability, are currently not adequately covered in the literature. The study acts as a springboard for a future conversation on the topic.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This study aims to analyze information on vessel traffic between the two Koreas with a probability distribution for each route/vessel type. The study will then conduct an estimate for maritime transport patterns of inter-Korean trade in the future. To analyze the flow of inter-Korean coastal shipping, this study conducted visualization analysis of shipping status between North and South Korea by year, ship type, and port using navigation data of three years from Port Logistics Information System (Port-MIS) sources during 2006 to 2008, which saw the most active exchanges between the two governments. Also, this study analyzes shipping status between the two governments as a probability distribution for each port and provides the prospects for future maritime transport for inter-Korean trade by means of Bayesian Networks and simulation. The results of the analysis are as follows: i) when North-South routes are reopened, the import volume for sand from North Korea will be increased; ii) investment in the modernization of ports in North Korea is required so that shipping companies can generate profit through economies of scale; iii) the number of the operating vessels including container ships between the two governments is expected to increase like when the tensions and conflict on the Korean Peninsula was release, especially between Busan port in South Korea and Nampo port in North Korea; and iv) among container ships, transshipment containers imported and exported through Busan Port will be shipped to North Korea by feeder transportation.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.