Regular Days Research Articles

Analyzing Urban Freight System, Supply Chain Characteristics and Temporal Travel Pattern in Indian context

State of the art in understanding the urban freight system is quite challenging in developing countries, and India is one of them. Consequently, there is a paucity of knowledge in comprehending the urban freight system's supply chain characteristics and its impact on operation, infrastructure, and the environment. Therefore, this study aims to analyze the supply chain characteristics, estimate the freight trips and freight temporal travel patterns for different industrial sectors in the various establishments at a microscopic level. Using an establishment-based freight survey (EBFS) form, a total of 647 establishment data was collected from Tiruchirappalli, India. This study initially investigated the dynamic and robustness of the supply chain characteristics such as establishment, commodity, vehicular movements, and delivery patterns involved in the urban freight system at the microscopic level. Then, correlation tests were performed to understand the relationship between supply chain characteristics and freight trips at different confidence intervals. Business size variables (employment and gross floor area) were used to estimate the freight trip movement and generate a freight trip rate table for intermediate and pure receiver establishment. Freight trips rates for different industrial sectors were estimated using multiple classification analysis method and compared to various goodness of fit measures. Eventually, the urban freight temporal travel pattern was assessed and visualized by hourly travel distribution of the freight movements during regular working days in the various industrial sector for the Indian context.

The impact of somatic cell count on milk yield and composition

The impact of somatic cell count on milk yield and composition

Can’t drive today? The impact of driving restrictions on bikeshare ridership in Mexico City

The Mexico City government has introduced policies to reduce pollution from cars, including a license-plate based driving restriction and a bikeshare. When restrictions are in place, people need to find transportation alternatives. This research leverages the random nature of driving restrictions to explore whether people use bikeshare when driving is restricted due to poor air quality. Negative binomial models were used to study these effects using data from 2016 to 2019. Results indicate that restricted days exhibit approximately 17% lower bikeshare use (10–24% decrease, 95% CI) for non-peak traffic hours compared to regular days. However, ridership increases during peak traffic hours. Morning ridership increases up to 12.5% (3–22% increase, 95% C.I) and evening ridership increases up to 16.2% (5–27.6% increase, 95% C.I). The analysis suggests that the poor environmental conditions may buffer bikeshare system use increases and that bikeshare can be a critical partner in local transportation infrastructure.

Tracking triadic cardinality distributions for burst detection in high-speed graph streams

In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people’s sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these phenomena are associated with the sudden surges of node interactions in networks, which we call “bursts” in this work. We uncover that, in many scenarios, the emergence of a burst is accompanied with the formation of triangles in networks. This finding motivates us to propose a new and robust method for burst detection on an OSN. We first introduce a new measure, i.e., “triadic cardinality distribution,” corresponding to the fractions of nodes with different numbers of triangles, i.e., triadic cardinalities, in a network. We show that this distribution not only changes when a burst occurs, but it also has a robustness property that it is immunized against common spamming social-bot attacks. Hence, by tracking triadic cardinality distributions, we can more reliably detect bursts than simply counting node interactions on an OSN. To avoid handling massive activity data generated by OSN users during the triadic tracking, we design an efficient “sample-estimate” framework to provide maximum likelihood estimate of the triadic cardinality distribution. We propose several sampling methods and provide insights into their performance difference through both theoretical analysis and empirical experiments on real-world networks.

COVID-19 lockdowns reduce the Black carbon and polycyclic aromatic hydrocarbons of the Asian atmosphere: source apportionment and health hazard evaluation.

The entire world is affected by Coronavirus disease (COVID-19), which is spreading worldwide in a short time. India is one of the countries which is affected most, therefore, the Government of India has implemented several lockdowns in the entire country from April 25, 2020. We studied air pollutants (i.e., PM2.5, Black Carbon (BC), and Polycyclic Aromatic Hydrocarbons (PAHs) level, and observed significantly sudden reduced. In India, most of the anthropogenic activities completely stopped. Therefore, we studied the levels of BC, PAHs and PM2.5 concentrations, their sources apportion, and health risk assessment during normal days, lockdown (from lockdown 1.0 to lockdown 4.0) and unlock down 1.0 situation at Sakchi, Jamshedpur city. It was observed that lockdowns and unlock down situations BC, PAHs and PM2.5 concentrations were significantly lower than regular days. We applied the advanced air mass back trajectory (AMBT) model to locate airborne particulate matter dispersal from different directions to strengthen the new result. The diagnostic ratio analyses of BC shows that wood burning contribution was too high during the lockdown situations. However, during normal days, the PAHs source profile was dedicated toward biomass, coal burning, and vehicle emission as primary sources of PAHs. During the lockdown period, emission from biomass and coal burning was a significant contributor to PAHs. The summaries of health risk assessment of BC quantified an equal number of passively smoked cigarettes (PSC) for an individual situation was studied. This study focuses on the overall climate impact of pandemic situations.Graphic abstract

Study of Socio-demographic and Clinical Profile of Individuals with Deliberate Self-harm Attempt during COVID-19 Lockdown

Introduction: Self-harm attempt is an important aspect in consultation liaison psychiatry. During lockdown due to the Coronavirus Disease-2019 (COVID-19) pandemic, many people underwent various stressors. Many individuals were referred to Psychiatry Out Patient Department (OPD) with a history of self- harm attempt. They were also aware of the travel restrictions and difficulty in reaching hospitals. So, it was necessary to understand the reasons for the self-harm attempt, especially during the lockdown. Aim: To study the socio-demographic and clinical profile of individuals with self-harm attempt during the lockdown. Materials and Methods: The cross-sectional study was done during the COVID-19 lockdown period (last week of March 2020 till end of May 2020) in the Kodagu Institute of Medical Sciences Teaching Hospital, Madikeri, Karnataka, India. A total of 52 subjects of self-harm attempt who were referred to the Department of Psychiatry for counseling and needed psychological help, on regular OPD days were recruited for the study after obtaining informed consent. Socio-demographic and clinical details like nature of self-harm attempt, method of attempt, number of attempts, reason for attempt, time of attempt, consumed alcohol prior to the attempt, how did they reach hospital, clinical depression, difficulty in reaching hospital due to lockdown, regret/remorse about the attempt were documented using the semi-structured proforma. The data was expressed as percentages and Chi-square test was applied. A p-value less than 0.001 were considered as significant. Results: Out of 52 subjects, 65.4% belonged to 21-40 years of age, 51.9% were females, 65.4% were married, 30.8% belonged to lower socio-economic status, 61.6% residing in a rural area, education below class 10 (73.07%), the predominant occupation was labourers and home maker. Poisoning was the common method and 25% had depression and needed medications. The interpersonal conflict was the common reason and had difficulties due to lockdown (5.7%). However, the findings were statistically not significant among males and females. Conclusion: Self-harm attempt was common in young married females in a rural area by poisoning, who reported depression, interpersonal conflict and economic difficulties during the lockdown.

Pandemic (COVID-19) Lockdown and Human Resource Management Emergency Policy Intervention

This study assesses the emergency policy intervention of the Human Resource Department (HRD) at the employee's housing during the Curfew and Pandemic Lockdown in Kuwait. In this case, the study applies interviews using a structured questionnaire and observation. This study is a qualitative study that primarily uses interviews and observations. Interview and observations are used to gather some feedback from the respondents while comparing the regular or normal-day protocol and emergency protocols in the employees’ housing. The researcher makes use of the total population sampling as a type of purposive sampling. The respondents’ demographics show that majority are mature employees who have reached their level of expertise in doing the right things for the right reasons in policy execution that contributed to the success of their policy execution. The Disaster Risk and Reduction Team (DRRT) regarded the implementation to be responsive since the majority of the employees abide by the policies and that, the housing is not infiltrated by the virus. The role of HRD is vital in Risk management. HRD plans according to considerations, primarily in the promotion of the welfare of its people. It serves as the backbone of policy planning, while the DRRT serves as the frontlines of the housing. They are the implementor of the plans serve by HRD. Yes, there is a significant relationship between the emergency policies and the safety of employees. It is the policies that limit the actions of the employee actions that are distinctive during the regular free-pandemic days. Without the emergency policies, the safety of the employees may be at risk that could probably infect employees in the organization and cause the rise of COVID-19 patients in the country.

Dental Treatments During the COVID-19 Pandemic in Three Hospitals in Jordan: Retrospective Study.

BackgroundCases of COVID-19 first emerged in December 2019. Since then, the virus has spread rapidly worldwide, with daily increases in the numbers of infections and deaths. COVID-19 spreads via airborne transmission, which renders dental treatment a potential source of virus transmission. Dental treatments require the use of handpieces, ultrasonic devices, or air–water syringes, which generate considerable amounts of aerosols. Jordan, being one of the affected countries, instituted preventive lockdown measures on March 17, 2020. Emergency dental treatments were only allowed in dental clinics of the Royal Medical Services of Jordan Armed Forces and Ministry of Health, and were prohibited in other sectors such as private clinics and universities.ObjectiveThe aim of this study is to investigate the dental treatments performed in three military hospitals during the 44-day lockdown period in Jordan. The investigation explores the impact of COVID-19 on the number of patients and types of performed dental treatments.MethodsData such as number of patients, patients’ age and gender, and performed dental treatments were collected retrospectively from the hospital records and were analyzed.ResultsOur results showed a 90% (17,591 to 1689) decrease in patient visits during the lockdown period compared to regular days. The total number of treatments (n=1689) during the lockdown period varied between endodontic cases (n=877, 51.9%), extraction and other surgical cases (n=374, 22.1%), restorative cases (n=142, 8.4%), orthodontic treatments (n=4, 0.2%), and other procedures (n=292, 17.3%). The differences in gender and age group among all clinics were statistically significant (P<.001 and P=.02, respectively).ConclusionsThe COVID-19 pandemic had a significant effect on the number of patients seeking dental treatments. It also affected the types of treatments performed. Endodontic treatment accounted for almost 50% of patient load during the lockdown compared to approximately 20% during regular days.

The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic

Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations.

Study of clinical outcome following arthroscopic anterior Crucitate ligament reconstruction using autologous quadruple hamstrings graft

Study of clinical outcome following arthroscopic anterior Crucitate ligament reconstruction using autologous quadruple hamstrings graft - IJOS- Print ISSN No: - 2395-1354 Online ISSN No:- 2395-1362 Article DOI No:- 10.18231/2395-1362.2018.0066, Indian Journal of Orthopaedics Surgery-Indian J Orthop Surg

The Effects of a Day off from Retail Price Competition: Evidence on Consumer Behavior and Firm Performance in Gasoline Retailing

First, we analyze how regular days off from competition and a time-dependent price pattern affect firm performance. Second, we examine the effects on firms' profitability from consumers’ changing search- and timing behavior. We use microdata from gasoline retailing in Norway. From 2004 to 2017, firms practiced an industry-wide day off from competition, starting on Mondays at noon, by increasing prices to a common level given by the recommended prices (decided and published in advance). In turn, a foreseeable low-price window is open before every restoration. During the data period, we observe an additional weekly restoration on Thursdays at noon. The additional day off from competition increases firm performance. As expected, a conventional price search of where to buy reduces firms’ profitability. In contrast, consumers who are aware of the cycle and spend effort on when to buy have a positive impact on firms’ profitability. If consumers spend effort on when to buy, they attempt to tank during low price windows. By its very nature, this shrink consumers’ ability to compare prices at several outlets. Consequently, more attention to when to buy may soften price competition.

English

The aim of the survey was to assess artificial insemination (AI) service delivery system and identify the challenges and opportunities of AI service in cattle production in Western Zone of Tigray Region, Ethiopia. Twenty Kebeles (lowest administrative units) and 353 respondents were purposively involved in face-to-face interview. Data analysis was performed using SPSS and mainly summarized by frequency and percentages. Chi-square test was the statistical method used to test proportions of variables. There was statistically significant (P<0.05) differences in AI service interruptions in both regular working hours and weekends and holiday hours. AI service interruption was a critical challenge in regular working days and in weekends and holidays. This is because 73% of the respondents reported that AI service was interrupted in Monday through Friday and 75% of the respondents also reported that Artificial Insemination Technicians (AITs) were not available to deliver AI service in weekends and holidays. Above half (63%) of the small scale farmers reported that AITs were very poor. Most of the very poor AITs were in midland (48%) and lowland (36%) agro-ecologies compared to the AITs in highland (16%) agro-ecology. Mobile AI service delivery system through motor bikes was the most common AI service delivery system used. The major challenges of AI service in order of their importance were lack of community awareness, poor animal management, unskilled AITs and inadequacy of AITs. The government and NGOs should solve the current challenges of AI service. The current approach and system of AI delivery should be restructured. Adequate numbers of skilled manpower should be assigned based on the standard numbers of breedable cows per AI center or per AIT. Moreover, agro-ecology based human management system and infrastructure development should be adequately addressed. Key words: Communication means, AI service interruption, distance between homestead and AI service center, AI service delivery system, agro-ecology.

Hybrid meat

Hybrid meat

The Study on Work Life Balance as mediating variable of Workload and Job Satisfaction on Jember University’s Educational Contract Staff Employees

University of Jember is a large State University with many students registered at Jember University, with the large number of students, it needs teaching staff (lecturers) and education staff in their daily education activities. Teaching and learning activities at the University of Jember starts from early morning hours until the evening so that impact on the workload of educational staff. At work, most people begin to feel there is something else that must be considered in addition to work, namely daily life. Lack of work-life balance is one of the triggers for stress. When an individual does not maintain balance and work too much in an organizational setting, this can lead to psychological and behavioral consequences, as well as low job satisfaction as a result of his/her productivity will also be low. This study aims to analyze mediating effect of Work Life Balance on the relationship between Workload and Job Satisfaction of Employees in Education Contract Staff at the University of Jember. The data used in this study are primary data using questionnaires on 106 respondents determined using purposive sampling technique. Researcher determines the sample taken is the University of Jember education contract staff who work more than 1 year and have shifts work schedules outside regular working hours several days a week. The analytical method used is Structural Equation Modeling (SEM) analysis. The results showed that workload and Work life balance had a significant effect on job satisfaction of Education Contract Employees at Jember University.

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System.

BackgroundType 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system.ObjectiveThe study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information.MethodsWe retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly).ResultsOur analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively.ConclusionsWe presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

BackgroundSemisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model.ObjectiveThis study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio.MethodsThree groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required.ResultsThe one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time.ConclusionsWe demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.

The Comprehensive Primary Care Initiative and Emergency Department and Urgent Care Center Use

Improved access to quality primary care is considered a key element in reducing unnecessary visits to emergency departments (EDs) and urgent care clinics (UCCs) and in reducing expenditures. The Comprehensive Primary Care (CPC) initiative tested large‐scale payment and delivery reform in 497 primary care practices in seven regions. Practices received enhanced payment, data feedback, and learning support to improve outcomes. In this study, we analyze the effect of CPC on ED and UCC visits October 2012‐December 2016. We expect the greatest changes to occur with those types of visits most amenable to improved primary care‐primary care substitutable visits (for conditions treatable in a primary care setting), and potentially primary care preventable ED visits (for conditions whose symptoms and exacerbations can be mitigated with effective primary care). We disaggregated visits by day of week to study whether changes were concentrated during regular business days (via increased same‐day/next‐day appointments and more responsiveness during weekdays by the care team) versus weekends and holidays. We expect access improvements to have greater effects on weekdays.We estimated CPC’s four‐year effects on Medicare fee‐for‐service (FFS) beneficiaries’ all‐cause and primary care substitutable (PCS) outpatient ED and UCC use and on potentially primary care preventable (PPCP) ED visits. To categorize visits, we adapted the commonly used claims‐based New York University Emergency Department Algorithm. We estimated difference‐in‐differences regressions comparing changes in outcomes between before CPC began (baseline) and the four years of CPC for attributed Medicare FFS beneficiaries in CPC practices, with changes over the same period for beneficiaries attributed to propensity‐score‐matched comparison practices. Regressions controlled for baseline practice, patient characteristics, and clustering.565,674 Medicare FFS beneficiaries attributed to 497 CPC practices, and 1,165,284 beneficiaries attributed to 908 similar comparison practices.CPC practices had 2 percent decreased growth in all‐cause ED visits, and 3 percent lower growth in both PCS (P = .02) and PPCP ED visits (P = .04), relative to comparison practices. Lower growth in weekday visits (4 percent, P = .002) drove the PCS ED results. UCC visits had 9 percent lower growth for both all‐cause (P = .08) and PCS (P = .07). As expected, there was no difference in ED visits for injuries—which is an excellent falsification test.CPC was associated with lower growth in outpatient ED and UCC visits, driven by reductions in weekday PCS visits (likely reflecting improved access to the practice), and PPCP ED visits (suggesting the importance of improved primary care more generally).Large‐scale initiatives to improve access to quality primary care, such as CPC, hold promise for reducing visits to EDs and UCCs. More research in understanding the contributions of specific care delivery changes, from same‐day appointments to improved health status of patients, primary care practice transformation, and the related response of attributed beneficiaries could help explain the differential impacts on visits by weekday versus nonweekday.Centers for Medicare and Medicaid Services.

Statistical analysis and characteristics of hospital medical waste under novel Coronavirus outbreak

One of thesources of infection as a result of coronavirus disease treatment is the medical waste generated during the health care activities. Since the registration of the first infected case of coronavirus in Jordan the daily number of patients fluctuated from as low as zero to as high as 40 with a recovery ratio and case fatality risk of 39% and 1.7%, respectively. The main objective of the present study is to carry out statistical analysis and assess the generation rates and the composition of the medical waste generated during the treatment of coronavirus pandemic with reference to a major tertiary care hospital in Jordan. Data onthe daily generated waste, number of the admitted patients and on the amounts of consumables like various personal protective equipment, testing kits, and disinfectant used during the treatment of coronavirus disease was obtained. Data was subjected to descriptive statistical analysis to find the average generation rates, 3 days moving average, as well as the frequency distribution of the generated amounts. During 25 days' period, King Abdullah University Hospital has admitted 95 infected patients by coronavirus. The amount of the average rate of the medical waste generated as a result of coronavirus treatment was found to be 14.16 kg/patient/day and 3.95 kg/bed/day, which are more than tenfold higher than the average generation rate during the regular operational days of the hospital. Frequency analysis of the data revealed that the medical waste generation follows log normal distribution with correlation coefficient of 0.89. The distribution is distorted to the right and flatter than the normal distribution curve as judged by the skewness and kurtosis coefficients, respectively, which indicates deviation from normality.

The Effects of Air Pollution on Sales Promotions and Channel Use Across Online and Offline Retail

When the air quality is expected to be bad, it is common for people to wear anti air pollution masks to protect themselves or systematically restrict their outdoor activities. While this avoidance behavior is beneficial for the customers, it is unclear how deteriorating air quality may affect their other economic activities. In this paper, we focus on the retail sector that is prone to such disruptions. We study how diminished air quality affects the substitutive relationship between offline and online sales associated with a cosmetics retailer located in South Korea. This retailer offers regular and systematic promotion days monthly – during these days, consumers get significant discounts on all their purchases at both the offline stores as well as through the PC and mobile channels. While the point of these promotions is to boost demand on these days, we specifically test how air pollution may affect the actual demand that manifests during these promotion days across the offline and online channels. Interestingly, air pollution significantly affects consumer behavior. In general, polluted air boosts online sales and online promotion effectiveness. Unexpectedly, air pollution is unlikely to hurt the offline sales, and even increase offline sales and offline promotion effectiveness. We also find a notion of the inverted-U shaped reaction to the seriousness of polluted air consistently in offline sales. These show the combined effect of channel substitution and recreational shopping. Considering the significance and size of the effect, this paper points out that climate change should be added in the function of the business.

Distributionally robust optimization for power trading of waste-to-energy plants under uncertainty

Waste-to-energy (WTE) plants are operated worldwide to address the management of municipal solid waste. Against this background, an increasing number of WTE plants serve as combined heat and power (CHP) producers that supply heat to the heating systems in local districts and trade electricity in the regional power markets. This paper studies a short-term operation planning problem of determining effective power trading strategies for WTE CHP plants that participate in day-ahead markets. A two-stage distributionally robust optimization (DRO) model is developed with the consideration of uncertain electricity prices, waste supply, and district heating demand. These different kinds of uncertainty are captured by an ambiguity set that contains a collection of possible probability distributions of the uncertain parameters. The two-stage DRO model seeks to ascertain a power trading strategy that maximizes the expected profit of a WTE CHP plant on a regular operating day under the worst-case distribution in the ambiguity set. As the DRO model is intractable, a solution method based on linear decision rule techniques is designed to reformulate the model as a tractable robust linear program. To test the applicability of the DRO model, a case study with real-world data is conducted. The computational results show that the two-stage DRO model can facilitate a WTE CHP plant in obtaining economical and robust power trading strategies for regular operating days in a day-ahead market. Furthermore, the impacts of the parameters in the ambiguity set on deriving robust power trading strategies for WTE CHP plants are investigated.