Commercial Consumers Research Articles

Error Investigation on Wi-Fi RTT in Commercial Consumer Devices

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance.

Towards an automated approach for smart sterility test examination.

As new technologies emerge, deep learning applications are often integral parts of new products as features and often as differentiating benefits. This is especially notable in commercial consumer products in everyday applications, such as voice assistants or streaming content recommendation systems. Due to the power and applicability of these deep learning technologies significant efforts are being directed to the development and integration of appropriate models into science and engineering applications to supplant analogue systems that may be highly prone to human error. Here we present an innovative, low-cost approach to advance sterility assessment workflows that are required and regulated within drug release/manufacturing processes. The model system leverages off-the-shelf hardware as well as deep learning models to detect and classify different microbial contaminations in test containers. The paired hardware and software tools were evaluated in experiments using common model organisms (C. sporogenes, P. aeruginosa, S. aureus). With this approach we were able to detect all three test organisms across 40 experiments, furthermore we were capable of classifying the present organisms with an average classification accuracy of over 87%.

Lateral Flow Microimmunoassay (LFµIA) for the Reliable Quantification of Allergen Traces in Food Consumables.

Quality assurance and food safety are of great concern within the food industry because of unknown quantities of allergens often present in food. Therefore, there is an ongoing need to develop rapid, sensitive, and easy to use methods that serve as an alternative to mass spectrometry and enzyme-linked immunosorbent assay (ELISA) for monitoring food safety. Lateral flow immunoassay is one of the most used point-of-need devices for clinical, environmental, and food safety applications. Compared to traditional methods, it appears to be a simple and fast alternative for detecting food allergens. However, its reliability is frequently questioned due to the lack of quantitative information. In this study, a lateral flow microimmunoassay (LFµIA) is presented that integrates up to 36 spots in microarray format in a single strip, providing semi-quantitative information about the level of allergens, positive and negative controls, internal calibration, and hook effect. The LFµIA has been evaluated for the on-site simultaneous and reliable quantification of almond and peanut allergens as a proof of concept, demonstrating high sensitivity (185 and 229 µg/kg, respectively), selectivity (77%), and accuracy (RSD 5-25%) when analyzing commercial allergen-suspicious food consumables.

Optimum energy management strategy with enhanced time of use tariff for campus building using particle swarm optimization

<span>Enhanced time of use (ETOU) tariff was introduced by Tenaga Nasional Berhad (TNB) in 2016 to promote demand response for commercial and industrial consumers. The ETOU tariff scheme offers different tariff rates at different times of the day. However, increment of electricity expenses might occur if consumers fail to optimally shift their consumption to lower rate hours and causing higher usage during peak hours. Furthermore, the time-based pricing tariff is still new to Malaysian consumers, thus consumers have lack of knowledge to perform demand response. Therefore, this research proposes an optimum load management strategy under the ETOU tariff for a commercial building using particle swarm optimization (PSO). The model was applied for Universiti Teknologi MARA (UiTM) Complex Engineering Shah Alam. Load profile of five different buildings in the complex were used as inputs for the study. The analyses were carried out at different controlled loads weightage factors ranged from 10-40% for load shifting strategy to determine the optimal solution. Results show the electricity cost decreases in all the controlled load <span>weightage factors tested on the buildings after applying the load management strategy. The weightage factor of 40% provides the best solution for all buildings, saving 1-4% on the monthly bills.</span></span>

Optimal operation of a hybrid multisource energy system considering grid load shedding

On-site grid-connected hybrid renewable energy systems are extensively used in the mitigation of scheduled grid load shedding in commercial buildings. This paper proposes an optimal operation of grid-connected hybrid PV-battery-Diesel generator (DG) energy system under scheduled grid load shedding for a commercial building. A mathematical model for an optimal operation of a grid-connected PV-Battery-DG energy system that has a potential of mitigating the scheduled load shedding is developed and implemented. The impact assessment of scheduled load shedding on commercial consumers is also assessed on the cost saving as compared to the baseline without load shedding. Results showcase the potential of the proposed grid-connected hybrid PV-Battery-DG to mitigate scheduled grid load shedding in commercial buildings. Scheduled grid load shedding impacts differently on commercial consumers depending on the scheduled time of a day.

A Demand Side Management Control Strategy Using RUNge Kutta Optimizer (RUN)

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid’s stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid’s peak load demand.

Commercial consumers pay attention to marginal prices or average prices? Implications for energy conservation policies

Any energy conservation policy intervention associated with energy price variation needs to consider whether consumers respond to marginal or average pricing. To the best of our knowledge, there is no such evidence for commercial electricity consumers (such as office buildings and malls), although commercial consumers are responsible for nearly 18% of electricity consumption in the United States. This study examines a four-tiered decreasing-block pricing schedule for commercial consumers. Based on individual-consumer-level data, we analyze the daily electricity consumption of 597 commercial accounts in Phoenix metropolitan, Arizona, from May 1, 2013, to December 31, 2016. We run 2SLS models with policy-induced price variation as instrumental variables to estimate the effects of marginal and average prices on commercial electricity consumption at each cutoff point. We also study the heterogenous response across industry sectors. Our analysis shows that commercial consumers respond to both marginal and average prices, but have different responses with respect to how much electricity they consume. Higher-usage consumers tend to respond more to average prices, whereas lower-usage consumers are more sensitive to marginal prices. Our findings indicate that conservation policies should be tailored differently for commercial electricity consumers. Nonlinear electricity pricing structures can reduce energy consumption, particularly for commercial consumers who have lower energy demand if the pricing structure shifts from decreasing to increasing blocks. In contrast, a flat rate with a higher price level can limit the electricity consumption of high-use consumers. As for an industry-wise policy design, nonlinear pricing can be effectively used to reduce aggregate consumption in the construction, manufacturing, real estate, and rental and leasing industries, professional, scientific, and technical services, other services (except public administration), and public administration industries.

Role of ethics in global business

Man wants success, since the success of a person in personal, professional, and social life depends on his patterns of communication behavior, the correction of these patterns also plays an essential role in achieving success.Success is achieved in the light of man's right and easy connection with himself and others; Because people are constantly interacting with themselves and others. Man is a social being and today, with the amazing development of societies, globalization,Individuals interact with a multitude of different organizations, and this has significantly complicated the relationship of individuals with each other and with organizations and the interrelationship of organizations with each other.Despite these complexities, everyone wants easy and correct communication with each other; Because they find comfort and tranquility in its light. This article seeks to examine the role of professional ethics in business, the history of business ethics in the world, the principles and foundations of business ethics and the views of thinkers, fair and unfair business practices, ethical guidelines on commercial advertising, consumer rights and ethics In production, globalization and business ethics, social marketing and its relationship with ethics, business ethics, and intellectual property rights, definitions of moral values, fair or just trade, ethical charters, and their codification circumstance business ethics terminology, international organizations active in the field of professional ethics.

Optimization of DC, AC, and Hybrid AC/DC Microgrid-Based IoT Systems: A Review

Smart microgrids, as the foundations of the future smart grid, combine distinct Internet of Things (IoT) designs and technologies for applications that are designed to create, regulate, monitor, and protect the microgrid (MG), particularly as the IoT develops and evolves on a daily basis. A smart MG is a small grid that may operate individually or in tandem with the electric grid, and it is ideal for institutional, commercial, and industrial consumers, as well as urban and rural societies. A MG can operate in two methods (stand-alone and grid-connected), with the ability to transition between modes due to local grid faults, planned maintenance, expansions, deficits and failures in the host system, and other factors. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optimal energy management, security protocols, control methods, and applications in the MG, with numerous distributed energy resources (DER) and interconnected loads. The use of IoT architecture for MG operations and controls is discussed in this research. With the use of power grid equipment and IoT-enabled technology, MGs are enabling local networks to give additional services on top of the essential supply of electricity to local networks that operate simultaneously or independently from the regional grid. Additionally, this review shows how hybrid AC/DC MGs are advantageous compared to AC and DC MGs. The state-of-the-art optimization techniques and trends in hybrid MG research are included in this work.

Sub-Chronic Effects of Sodium Metabisulfite on the Intestinal Microarchitecture of Mice

Background: Sodium metabisulfite (SM) is commonly used with varying concentrations as a preservative, sewage treatment chemical, and in other commercial consumer products etc. It is reported to pose series of health hazards affecting various vital organs. The current study was aimed to find histological alterations in the intestine with sub-chronic exposure of SM in albino mice. Materials and Methods: Ten mice were orally given SM (1.2 mg/kg mixed in 0.1 ml corn oil) for 21 days while ten mice were kept in the control group (no SM). All the mice were dissected, and small intestine pieces were excised and processed for histological sectioning and staining. Results: SM disrupts the outer wall of villi and microvilli of the small intestine. Moreover, a narrow lumen and few mitotic figures were seen in the intestinal glands. Conclusion: The results concludes that SM is a potential source of major organ’s defects and hence its usage should be lessened to improve the health status.

Non-destructive evaluation of the edible rate for pomelo using X-ray imaging method

Flesh content of pomelos sometimes does not match the price due to thick peel, which reduces their commercial value and consumer satisfaction. This study demonstrated the potential use of X-ray imaging method for non-destructive evaluation of the edible rate and the flesh content of ‘Hongrou’ pomelos and ‘Guanxi’ pomelos. An adaptive threshold segmentation method was used to segment the X-ray image into background, flesh region and peel region. Then, 2D edible rate and 2D flesh content were defined based on region area ratio and gray level logarithmic sum, respectively, and the multiple linear regression (MLR) models of edible rate and flesh content were established for quantitative analysis. The results showed that the residual predictive deviation (RPD) value of edible rate of ‘Hongrou’ and ‘Guanxi’ pomelos in prediction set were up to 2.78 and 2.82, respectively. The hybrid model based on both two cultivars pomelos also achieved good prediction accuracy (RPD = 2.83). In terms of flesh content prediction, the model prediction performance of ‘Hongrou’ pomelo (RPD = 2.92) was obviously better than that of ‘Guanxi’ pomelo (RPD = 2.05), and the RPD value of hybrid model in prediction set was 2.62. Further, both two-grade and three-grade linear discriminant analysis (LDA) classifiers were trained to explore the feasibility of using X-ray images to classify the edible rate of pomelos, and the classification accuracy of hybrid samples were 96.7% and 90.0%, respectively. Overall, the features extracted from X-ray image of pomelo could allow the non-destructive evaluation of the edible rate for pomelo.

3D printing sacrificial templates for manufacturing hydrogel constructs with channel networks

In hydrogel-based tissue engineering, channel network is an efficient structure for transporting nutrients/oxygen to support cell survival and construct living tissues in vitro. 3D printing can create complex hydrogel-based tissue constructs, however, due to the weak mechanical properties of hydrogel bioinks, cell-laden hydrogel constructs with effective channel networks are difficult to be directly printed. Here, an easy sacrificial 3D printing method based on commercial desktop 3D printer and water-soluble polyvinyl alcohol (PVA) to construct effective hydrogel channel networks is introduced. Specifically, i) commercial PVA consumables are printed to be sacrificial templates; ii) gelatin methacryloyl (GelMA) solution is cast to encapsulate the sacrificial template; iii) the sacrificial template is dissolved to form channel networks. PVA is a water-soluble sacrificial material with sufficient dissolution time and high mechanical strength, which can avoid the disadvantages of classic sacrificial materials (sugar and Pluronic F127). High roundness of printed PVA filaments results in high roundness of the channel networks. The interconnected channel networks accelerate the supply of oxygen/nutrients and promote cell growth. And with a period of culture, the higher the channel networks density, the better the cell growth. Additionally, PVA sacrificial templates have high stability and long-term preservation, facilitating transportation, circulation and use, which is conducive to the manufacturing and promotion of hydrogel-based constructs with channel networks. Taken together, our easy strategy of manufacturing hydrogel constructs with channel networks has broad application prospects, such as 3D cell culture, construction of functional tissue in vitro or tissue repair in vivo etc.

Job demands-resources, job crafting and work engagement of tobacco retailers.

In recent years, the development of tobacco control actions in China and the changes in people's health concepts have slowed the development of the tobacco industry. As an important strategic partner of tobacco sales companies, tobacco retailers are the key link between tobacco commercial enterprises and consumers. How to improve the work engagement level of tobacco retailers is an urgent issue for tobacco business enterprises. On the basis of job demands–resources (JD–R) theory, the mechanisms of the effects of job resources and demands on tobacco retailers' work engagement were explored. Results showed that (1) The negative path of job demands influencing tobacco retailers' work engagement was supported, and job crafting played a mediating role in it. (2) The positive path of job resources influencing tobacco retailers' work engagement was supported, and job crafting played a mediating role in it. (3) Servant leadership moderated the influence of job resources and demands on job crafting. Higher level of servant leadership brings the stronger effect of job resources on job crafting and the weaker effect of job demands on job crafting. (4) The mediating effect of job crafting between JD–R and work engagement was moderated by servant leadership. The higher level of servant leadership strengthened the mediating role of job crafting between job demands and work engagement, whereas it weakened the mediating role of job crafting between job demands and work engagement. This study enriches the research on application fields and boundary conditions of JD–R theory and provides practical guidance for improving the work engagement level of tobacco retailers.

Repeatable enhancement of healthcare data with social determinants of health.

BackgroundSocial and behavioral aspects of our lives significantly impact our health, yet minimal social determinants of health (SDOH) data elements are collected in the healthcare system.MethodsIn this proof-of-concept study we developed a repeatable SDOH enrichment and integration process to incorporate dynamically evolving SDOH domain concepts from consumers into clinical data. This process included SDOH mapping, linking compiled consumer data to patient records in Electronic Health Records, data quality analysis and preprocessing, and storage.ResultsConsumer compilers data coverage ranged from ~90 to ~54% and the percentage match rate between compilers was between ~21 and 64%. Our preliminary analysis showed that apart from demographic factors, several SDOH factors like home-ownership, marital-status, presence of children, number of members per household, economic stability and education were significantly different between the COVID-19 positive and negative patient groups while estimated family-income and home market-value were not.ConclusionOur preliminary analysis shows commercial consumer data can be a viable source of SDOH factor at an individual-level for clinical data thus providing a path for clinicians to improve patient treatment and care.

Is the electricity cross-subsidization policy in India caught between a rock and a hard place? An empirical investigation

Functioning of the Indian electricity sector is often found to be characterized by policy-level inconsistencies and meddled with political interference at all levels of governance. The objective of this study is to analyse the effect of reform-based policies and regulations and political environment on the cross-subsidization process in India. A panel data econometric technique is used as an empirical strategy to establish such a linkage, covering 18 Indian states over 10 years. Findings reveal that a favourable open access environment has become a game-changer for industrial and commercial consumers to migrate from the state grid to other sources - preferably to renewables - leading to greater renewable energy penetration. This has implications for cross-subsidies. In addition, it emerges from the analysis that it is crucial for distribution utilities and state regulatory commissions to be independent in their functioning. Greater reliance on the state exchequer by the distribution utilities could endanger their autonomy; whereas the lack of financial independence of SERCs could negatively impact their abilities to operate independently. Finally, it is found that the process of cross-subsidization is not always based on the hard logic of expanding welfare, rather it is motivated by the entrenched political goals and ambitions. This is in tune with findings from earlier studies.

Student-centred theory building: pedagogical collaboration after Mark Fisher

ABSTRACT This study presents the findings of a pedagogical case study examining a framework for developing innovative theory in postgraduate marketing education. The article describes a theory-building framework introduced to a postgraduate marketing course in order to enable students to examine, collaboratively and critically, a number of apparent paradoxes within contemporary commercial promotion and consumer behaviour. The article examines the pedagogical context for the framework, invoking the recently published account of Mark Fisher’s ‘exit’ pedagogy. The article focuses on the example of a case study of student-led theory development, tracing a series of stages to examine and explain promotional practices. Features such as critical reasoning, deliberation, synthesis and consensus building are exhibited through the stages of theory building. The approach ultimately contributes to developing an innovative approach to teaching theory through a framework that empowers theory development.

Modeling the Next Decade of Energy Sustainability: A Case of a Developing Country

The development of any country is closely related to its ability to provide access to electricity for productive labor. Many countries in sub-Saharan Africa have low electrification rates for commercial, industrial and residential consumers. This study focuses on Nigeria, which has one of the largest populations and economies in sub-Saharan Africa. Although Nigeria possesses abundant renewable energy resources that can increase electricity generation, it has suffered a significant setback in electricity generation. However, for Nigeria to become one of the leading industrialized countries by 2030, access to clean, reliable, and sustainable energy sources is vital (Vision 20: 2030). This study assesses the possibility of Nigeria developing and transitioning to the use of various energy sources. Additionally, this study evaluates greenhouse gas (GHG) mitigation plans and future trends in energy sustainability through multi-criteria decision analysis (MCDA), considering the technical, social, economic, and environmental dimensions of the sustainability structure. A total of twelve (12) sustainability indexes were taken into consideration; these consist of two (2) technical, three (3) social, three (3) environmental, and four (4) economic indicators. A scenario-based software called Long-range Energy Alternative Plan (LEAP) was used to integrate the analysis criteria and forecast a sustainable energy generation mix for the future. It considered three scenarios, namely: the business as usual scenario (BAU); renewables, natural gas and biomass scenario (RNB); and renewables and coal scenario (REC). It was concluded that the renewables, natural gas, and biomass scenario (RNB) is the best scenario to solve Nigeria’s energy problem based on the aim of the study.

The Energy Storage System Integration Into Photovoltaic Systems: A Case Study of Energy Management at UTFPR

Energy storage system integration can reduce electricity costs and provide desirable flexibility and reliability for photovoltaic (PV) systems, decreasing renewable energy fluctuations and technical constraints. In this sense, this study aimed to propose energy management strategies through this integration, aiming to improve the demand profile of a university commercial consumer for compensation during peak hours. To achieve the objectives of this research, a pilot project was installed which included a 10 kWp grid-tie PV system (GTPVS), bidirectional inverters, and battery bank, as part of a research and development (R&D) project implemented in the Federal University of Technology–Paraná (UTFPR) Curitiba campus at Neoville headquarters. The methodology consisted of the analysis of variables that interfere with the operation of this system, such as a detailed university profile of demand and consumption, local energy pricing, PV generation profile as well as the interaction between PV generator and the energy storage system, and the variation in power fluctuations throughout the year. Additionally, a technical feasibility study was carried out aimed to determine this consumer’s optimal size through the Homer Grid software. The detailed demand profile allowed us to know in depth the peak values were above 80 kW, for university working days. Furthermore, the daily analysis of the PV generation profile confirmed that although the PV source is quite variable, the scheduled dispatch operation is not interfered by the variability and intermittency of the solar source, as the system was designed and programmed to operate in parallel with the electrical network. Regarding the energy consumed at the university, very significant values were obtained, in which the month of July presented the highest consumption for the base year 2019, with a total monthly value of 48,670 kWh. The established operating strategy for the battery bank with a 10% discharge depth resulted in a life cycle of 2,500 cycles which can extend its useful life by approximately 9.5 years. The results obtained by the simulation show different levels of energy contribution with percentages of 60.0, 33.6, and 2.7%, respectively.

Short Term Load Forecasting of Residential and Commercial Consumers of Karnataka Electricity Board using CFNN

Electricity use and its access are correlated in the economic development of any country. Economically, electricity cannot be stored, and for stability of an electrical network a balance between generation and consumption is necessary. Electricity demand depends on various factors like temperature, everyday activities, time of day, days of the week days/Holidays. These parameters have led to price volatility and huge spikes in electricity prices. The research work proposes a short term Load prediction Model for LT2 (residential consumers), LT3 (Commercial Consumers) of Karnataka State Electricity Board using Cascaded Feed Forward Neural Network (CFNN). MATLAB software is utilized to design and test the forecasting model for predicting the power consumption. Furthermore, a shallow feed forward neural network-based prediction model is constructed and evaluated for performance comparison. The Performance metrics include Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The suggested STLF CFNN prediction model outperformed shallow feed forward networks on both performance metrics with prediction errors of less than 1%.

Social norm compliance and involvement with Covid-19

Consumer decisions are influenced by various variables, including compliance with society’s social norms and by involvement by consumers with the issue under consideration. Both variables have influenced consumers’ actions during previous pandemics. Therefore, this study investigated involvement with the Covid-19 pandemic, together with social norm compliance (SNC), their mutual influence, and how demographic characteristics and country of residence influenced these three issues. The methodology involved a quantitative descriptive cross-sectional survey, with a quota sample of 1096 responses, based on age, gender, education, habitation, and income. The research was conducted with populations representing the consumers who patronize major retailers in a developed country (Germany) and a developing one (South Africa). Data was collected via questionnaires e-mailed to commercial consumer panels covering both countries. The findings showed that involvement is influenced by gender, age, education, habitation, and country of residence, but that SNC is not influenced by demographics but does differ according to country. Further findings were that involvement with Covid-19 strongly influences SNC and, similarly, SNC also strongly influences involvement with Covid-19. Furthermore demographics, excluding habitation, also influence these variables. However, the relationship between the involvement and SNC variables does not differ between the two countries. This study has provided a better understanding of consumers’ involvement with Covid-19 and SNC as components of consumer behavior, which is important since, during the Covid-19 pandemic, retailers have been instrumental in implementing health protocols. Therefore, understanding the relationship between involvement with Covid-19 and SNC, and how they are influenced by consumer demographics, is important.