How manufacturing firms respond to energy subsidy reforms?

Energy prices increased several folds due to the 2010 Iranian Energy Subsidy Reform. This study assesses the impact of the reform on the performance of manufacturing firms using a detailed micro-panel dataset at the 4-digit ISIC level for the period 2009 to 2013. Since the reform universally affected all firms, the analysis relies on a quasi-experimental framework implementing first an explorative before-after design with structural fixed-effects and second a difference-in-difference analysis exploiting energy-sensitivity. The subsidy removal caused a shrinkage in output and manufacturing value-added of at least 3 and 7%, respectively. This results in a deterioration of profits by nearly 9%. Manufacturing firms have been affected through three channels: increasing costs of direct energy inputs, pass-through costs for inputs from upstream firms and an energy-price-induced demand contraction. To successfully implement an energy subsidy reform while maintaining growth in the manufacturing sector, not only the direct but also the indirect, pass-through effects have to be considered since capital or technology-led responses to mitigate negative repercussions in the short-run are unlikely at large scale. The results can inform price reforms that aim to mitigate climate change

Victims’ Fundamental Need for Safety and Privacy and the Role of Legislation and Empirical Evidence

Various laws, guidelines and other types of regulation have been created that introduced new rights worldwide for victims of crime. Many of these rights focus on active victims who wish to step into the open and to orally express their views and experiences in court. Rights and wishes to remain in the background and to preserve one’s privacy received less attention. This article focuses primarily on the wishes of victims that reveal their intention to not play an active role in the criminal process, and on victims who fear an invasion of their safety and privacy. According to the literature, such wishes and needs can be considered to be fundamental. The article questions the empirical basis for the present victim legislation: are the new laws that have been created over the decades founded on empirically established victim needs, or on presumed victim needs? The article concludes with a plea for a more extensive use of empirical findings that shed light on victim wishes in the legislation and the criminal process

Antimicrobial Use and Antimicrobial Resistance in Community-Acquired Urinary Tract Infections

This thesis describes the prescribing of antimicrobial drugs by GP's to treat urinary tract infections, possible risk factors for antimicrobial resistance in urinary tract infections and the possible effects of antimicrobial drug use on the composition of the microbiota

Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management

Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure a reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristic

Victim-Offender Contact in Forensic Mental Health

Crime victims have gained a stronger position in all phases of the criminal procedure, including the post-sentencing phase. It is in this phase specifically that victims’ needs and interests relating to acknowledgement interplay with the offenders’ needs and interests relating to resocialisation. In the Netherlands, offenders who suffer from a mental disorder at the time of the offence limiting their criminal accountability and pose a significant safety threat, can be given a TBS order. This means that they are placed in a forensic psychiatric hospital to prevent further crimes and receive treatment aimed at resocialisation. As resocialisation requires the offender to return to society, contact with the victim might be a necessary step. This article focuses on victim-offender contact during the execution of this TBS order, and looks at risks and opportunities of victim-offender contact in this context, given the particular offender population. Offenders are divided into three groups: those with primarily psychotic disorders, those suffering from personality disorders and those with comorbidity, especially substance abuse disorders. The TBS population is atypical compared to offenders without a mental disorder. Their disorders can heighten the risks of unsuccessful or even counterproductive victim-offender contact. Yet, carefully executed victim-offender contact which includes thorough preparation, managing expectations and choosing the right type of contact can contribute to both successful resocialisation as well as victim acknowledgement

Board Structure Variety in Cooperatives

This paper investigates why agricultural cooperatives exhibit different principles for the allocation of decision rights between the Board of Directors and the Management. A mass-action interpretation of the Nash equilibrium in an investment proposal game shows that, on the one hand, board structure variety is an equilibrium outcome while, on the other, the Traditional model (the board has full control) and the Management model (the professional management makes up the Board of the cooperative society) perform better than the Corporation model (the Management is in full control of the cooperative firm)

Demand Management for Attended Home Delivery – A Literature Review

Given the continuing e-commerce boom, the design of efficient and effective home delivery services is increasingly relevant. From a logistics perspective, attended home delivery, which requires the customer to be present when the purchased goods are delivered, is particularly challenging. To facilitate the delivery, the service provider and the customer typically agree on a specific time window for service. In designing the service offering, service providers face complex trade-offs between customer preferences and profitable service execution. In this paper, we map these trade-offs to different planning levels and demand management levers, and structure and synthesize corresponding literature according to different demand management decisions. Finally, we highlight research gaps and future research directions and discuss the linkage of the different planning levels

A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout

The facility layout problem (FLP) is the problem of determining non-overlapping positions of departments on the shop floor to minimize material handling costs. Traditional methods for solving FLPs consider pairwise (from-to) flows to optimize layouts. This paper shows that these traditional methods underestimate the total travel distance of a layout, when departments have more than a single input/output point and some flows consist of visits to more than two de- partments. To accurately calculate the traveled distances, the actual routes of the workers and transporters (so-called connected movements) in the system need to be determined. The con- nected movements of the workers in a facility can now be captured using the Internet of Things network and stored in the cloud server for analysis. We propose a mixed-integer non-linear programming model for the FLP that minimizes the total travel distance using these connected movements as the input data. Because of the complexity of the problem, a biased random key genetic algorithm is used to find the layout. To ensure the validity of the method, a case study is carried out at a fertilizer production company that implemented an Internet of Things network to capture worker movement data to minimize worker productivity loss via an improved layout. By using these connected movements, the best layout for the case company is found. The results of the proposed data-driven optimization method indicate that leveraging connected movements can reduce the total travel distance by 10.6% compared to the best possible layout generated by the traditional pairwise method in the case study

How Do Victims With the Need for Protection Judge Their Experiences With the Police in the Netherlands?

This article presents a preliminary analysis of how victims who report to the police for protection in the Netherlands judge their experiences with the police, in comparison with victims reporting crimes for other reasons. An existing dataset was used: the data was originally collected for a comprehensive survey among crime victims of 12 years and older in 2016. Female victims of violent (sexual and non-sexual) crimes constitute the major part of the victims for whom protection is the most important reporting reason. Victim perceptions of police contribution to safety as well as police information were investigated. The analyses show that overall, victim perceptions of the police’s contribution to safety are rather negative. Contribution to safety is judged somewhat better by victims for whom protection is their most important reporting reason; however, the respondents who are positive still form a minority. Police information is judged positively by more victims than contribution to safety. Of the respondents for whom protection is a reporting reason, victims of sexual crimes appear to judge police information positively more often than victims of other crime types

Streamlined Quantitative Imaging Biomarker Development: Generalization of radiomics through automated machine learning

Radiomics uses quantitative medical imaging features and AI to create predictive models which can be used as biomarkers. In this thesis, we have developped an adaptive radiomics framework to automatically optimize the radiomics workflow per application and demonstrate its use to create biomarkers in eight different clinical applications