On-demand System Research Articles

On-demand system to support patients in home-parenteral nutrition

On-demand system to support patients in home-parenteral nutrition

Enhancing quality assurance in radiotherapy for gynaecological cancers: implementation of an on-demand peer review process.

Ensuring high-quality radiotherapy requires peer-reviewing target volumes. The Royal College of Radiologists recommends peer review specifically for individual target volumes in cases of gynaecological cancers. This study presents the outcomes of implementing an on-demand peer review system for gynaecological cancers within our institute. The peer review process was planned for gynaecological cancer cases intended for curative radiotherapy. After junior clinical oncologists (COs) completed the segmentation, two senior COs specializing in gynaecological cancers conducted the peer review. All peer review outcomes were recorded prospectively. The audit process compliance, the proportion of patients requiring major and minor modifications in target volumes, the direction of changes, and the factors influencing these changes were reported. A total of 230 patients were eligible, and out of these, 204 (88.3%) patients underwent at least one peer review. Among the patients, 108 required major modifications in their target volumes. P-charts revealed a stabilization in the need for major modifications at the end of three months, indicating that 38.2% and 28% of patients still required major modifications for the nodal and primary CTV, respectively. Multivariable analysis demonstrated that major modifications were associated with the use of extended field radiotherapy and radical radiation in non-cervical primary cases. An on-demand peer review system was feasible and resulted in clinically meaningful, major modifications in the target volumes for 53% of patients. Gynaecological cancers require ongoing peer review to ensure quality of care in radiotherapy. A flexible on-demand system not only ensures that patient treatment start is not delayed but also has an important educational role for junior trainees.

The 63rd Annual Meeting of the Japanese Respiratory Society.

The 63rd Annual Meeting of the Japanese Respiratory Society (JRS) was held in Tokyo on 28–30 April 2023. To reduce the risk of coronavirus disease 2019 (COVID-19) infection, the meeting was held in a hybrid format, with approximately 1200 registered abstracts and more than 9700 participants attending the meeting. Many more local participants attended compared with the previous year, encouraged by the Japanese government's decision to downgrade the status of COVID-19 infection from category three to category five in May 2023. Participants were able to attend lectures regarding the latest findings from various fields, including basic research and clinical research, in accord with the conference theme, ‘Future of pulmonology pioneered by the fusion of knowledge’. Participants actively engaged in a face-to-face question-and-answer session after the lecture, and the venue was full of enthusiasm. I presented in two oral sessions and one poster presentation. It was a stimulating experience, and I exchanged ideas with many researchers and received helpful tips for my research. As editor-in-chief of Respirology Case Reports, the official case reports journal of the Asian Pacific Society of Respirology (APSR), I also took part in promotional activities at the APSR booth, informing participants that JRS members received 50% off the article publication charge for Respirology Case Reports and encouraging them to submit articles. After the conference, young respiratory physicians involved in allergy research across Japan gathered for dinner together. The group is named Kinkira-kai, meaning shining and sparkling. Around 30 researchers participated, talking about their daily practice and research over beer and wine. Some researchers discussed potential collaborations and decided to undertake joint research. The COVID-19 outbreak is now largely behind us, and we were able to hold a large gathering for the first time in 3 years. We have pledged to meet again at the 72nd Annual Meeting of the Japanese Society of Allergology in October 2023. It will be exciting to see how the membership of this group grows in the next 5 or 10 years. Unfortunately, the number of foreign participants at the Annual Meeting of JRS was still limited. We expect that the influence of COVID-19 on the conference will disappear over time, and that next year's Annual Meeting in Yokohama will attract many participants from Japan and abroad. I am writing this column while watching the on-demand streaming of the meeting. I hope that this excellent on-demand system will continue after the COVID-19 pandemic has subsided. None declared.

Balancing convenience and sustainability in public transport through dynamic transit bus networks

The rapid transformation of urban mobility fueled by the evolution of digital technologies has enabled smarter and more convenient modes of transportation. However, these new modes (e.g., ride-hailing and ridesharing) are not always more sustainable than traditional modes, such as public transport networks. In this work, we propose a dynamic transit bus system that aims at combining the higher convenience provided to passengers in on-demand systems with the sustainability of public transport. The goal is to attract more passengers to the transit service by offering reduced walk-to-station distance and total travel time compared to the fixed lines. Our proposed system plans the routes and intermediate stops of the transit lines based on the actual observed demand. Due to the complexity of the resulting optimization problem under investigation, we propose a solution method which splits the problem into four stages: clustering, initialization, optimization, and merging. We compare our proposed system in two settings: flexible (i.e., all intermediate stations are dynamic), and semi-flexible (i.e., original fixed lines intermediate stations are kept at the route and more dynamic stations are added) to the fixed static line. Our results show that our proposed system offers a more sustainable option and can reduce the system-wide travel distance by attracting more on-demand passengers to the transit service by offering them reduced walking distance and travel duration compared to the static lines. We also quantify the positive impact of passengers’ willingness to act more sustainably by accepting to walk more or arrive later to their destinations on the system-wide sustainability.

Mixed fleets of automated and human-driven vehicles in public transport systems: An evaluation of feeder line services

This study focuses on the transitioning period of operating mixed fleets of both automated and human-driven vehicles for public transit services. The type of service investigated here is flexible, including elements of both fixed route and on-demand systems. The operation of the mixed fleet is optimized with analytical methods leading to models for optimal service headway and stop spacing for the two types of vehicles. Analytical models for optimal passenger capacity per vehicle and required fleet size for each type of vehicles are also derived. Four operational strategies are considered, referring to whether the two types of vehicles operate jointly or independently in terms of optimal service headway and stop spacing within the mixed fleet. Numerical analyses indicate that automated vehicles operate optimally with less frequent vehicle dispatches and more fixed stop locations compared to human-driven vehicles. They also require greater fleet size and similar passenger capacity per vehicle. The four operational strategies perform similarly in terms of total generalized costs for the input values considered here. However, sensitivity analyses showed that the operational characteristics of the two types of vehicles in a mixed fleet and the performance of the four operational strategies depend significantly on the percentage of total demand that each type of vehicle serves, as well as on the automated vehicles’ speed and in-vehicle travel time cost for users. The mixed fleets represent the transitioning period towards transit fleets of automated vehicles only and it is shown to be the costliest period for both users and operators.

On the Value of Dynamism in Transit Networks

The rise of on-demand mobility technologies over the past decade has sparked interest in the integration of traditional transit and on-demand systems. One of the main reasons behind this is the potential to address a fundamental trade-off in transit: the ridership versus coverage dilemma. However, unlike purely fixed systems or purely on-demand systems, integrated systems are not well understood; their planning and operational problems are significantly more challenging, and their broader implications are the source of a heated debate. Motivated by this debate, we introduce the dynamicity gap, a general concept that quantifies the attainable benefit of allowing (but not requiring) dynamic components in the response strategy to a multistage optimization problem. Although computing the dynamicity gap exactly may be intractable, we develop an analytical framework with which to approximate it as a function of problem input parameters. The framework allows us to certify the value of dynamism (i.e., a dynamicity gap greater than one) for certain combinations of problem input parameters. We showcase our approach with two sets of computational experiments, from which we gain both qualitative and quantitative insights about the settings in which the integration of transit and on-demand systems may certifiably be a worthwhile investment.Funding: This work was partially supported by the National Science Foundation [Grants DMS-1839346 and CNS-1952011]. Part of this research was performed while the authors were visiting the Institute for Pure and Applied Mathematics, which is supported by the National Science Foundation [Grant DMS-1925919].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1193 .

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.

Rubik's Cube as Reconfigurable Microfluidic Platform for Rapid Setup and Switching of Analytical Devices.

Microfluidics technology plays an important role in modern analytical instruments, while the modular design of microfluidics facilitates the reconfiguration of analytical instrument functions, making it possible to deploy on-demand systems in the field. However, modular design also faces the challenges such as connection reliability and reconfiguration convenience. Inspired by the self-locking structure of the Rubik's cube, a modular, reconfigurable microfluidic instrument architecture is proposed in this paper. The system has a self-locking structure of Rubik's cube components and an O-ring-based alignment and sealing mechanism, which enables reliable interconnection and rapid rearrangement of microfluidic modules by simply rotating the faces of the microfluidic cube. In addition, the system is capable of integrating a variety of customized modules to perform analysis tasks. A proof-of-concept application of detecting multiple pollutants in water is demonstrated to show the reconfigurable characteristics of the system. The findings of this paper provide a new idea for the design of microfluidic analytical instrument architectures.

An agent-based model for assessing the financial viability of autonomous mobility on-demand systems used as first and last-mile of public transport trips: A case-study in Rotterdam, the Netherlands

The continuing urbanization and corresponding increase in transport demand are putting pressure on the accessibility, safety, sustainability, livability, and efficiency of urbanized regions. Public transport is regarded as a sustainable mode of transport for these regions and therefore transport policies aim to increase its attractiveness. However, public transport is facing last-mile connectivity problems. The application of Autonomous Mobility on-Demand (AMoD) as a feeder service for public transport hubs can potentially improve the first and last-mile trip leg which increases the attractivity of public transport. However, will such a system be financially viable when applied in an urban area? and what kind of operation will lead to the highest system performance? In this research, this question is addressed by proposing a method that connects macro transport modeling and agent-based modeling (ABM). An existing gravity-based travel demand estimation model built in a macro simulation tool is used to predict passenger demand across all the OD pairs of a city. For those OD pairs that can use the AMoD as first /last mile this is modeled using an agent-based rationale to be able to simulate the behavior of passengers and vehicles within that specific area of the city. The simulation model is applied to the case study area of the south of Rotterdam, in The Netherlands, where metro Station Zuidplein and the rail Station Lombardijen function as two AMoD hubs. Using the case study, the impact of relocation, ridesharing, and charging strategy is assessed in regards to financial viability. Among other insights, results show that the AMoD service leads to a profit on a typical business day for the operating companies despite the high-quality level of the service (very low average waiting time for a vehicle). If this particular system would not consist of automated vehicles and one would have to pay a salary to drivers, it would not be possible to make a profit on a typical business day. Moreover, results show that activating dynamic ridesharing and using wireless fast chargers at the stations results in the most financially viable operation. Activating automatic relocations results in the most costly operation.

Streamlined plug-in aerosol prototype for reconfigurable manufacture of nano-drug delivery systems

The significant advances in nano-drug delivery systems (NDDS) for anticancer agents have led to the development of computational techniques, such as machine learning and neural networks to identify the optimal architectural and compositional design in a wide variety of therapeutic nanoformulations. On the other hand, few studies have examined downsized plug-in reaction-ware embodied in an autonomous platform for the instant reconfigurable production of engineered nanomaterials to guide optimal NDDS designs and delivery strategies. This paper describes an on-demand system for an electrically operable, continuously processible material produced by sequential spray pyrolysis and vibrating spray for single-pass NDDS assembly. In particular, a mild chemotherapeutic NDDS consisting of amorphous boron nitride (a-BN; a stable base material for loading), doxorubicin (DOX; an anticancer drug), and folic acid-chitosan conjugate (FACHI; a targeting and antiopsonic agent), called a-BN-DOX@FACHI, was fabricated using the developed system. a-BN-DOX@FACHI was assessed for the pH-responsive release of DOX, targeting of the folate receptor, and its resistance to opsonization and macrophage phagocytosis. a-BN-DOX@FACHI was found to be a mild cancer chemotherapeutic with reasonable biosafety. Integrating a metal ablation device with the developed on-demand system enabled the reconfiguration of NDDS from a-BN-DOX@FACHI to a-BN-Au-DOX@FACHI or a-BN-Pt-cisplatin@bovine serum albumin to add a photothermal effect with a range of architectures and compositions.

Smart stimuli-responsive implantable drug delivery systems for programmed and on-demand cancer treatment: An overview on the emerging materials

• Smart materials develop programmed and on-demand cancer treatment. • Tumor recurrence is prevented via multi-responsive implantable delivery systems. • Redox, enzyme and pH-responsive systems are described in programmable systems. • On-demand systems are regulated by NIR, ultrasound, electric and magnetic stimuli. • Applications of the smart systems are described for treatment of different tumors. Despite noteworthy developments in tumor therapy, cancer is still one of the main reasons for death worldwide. While present cures supply hopeful marks, they trigger harsh cytotoxicity, with limited success in preventing and recurrence of the disease. To overcome the mentioned issues, implantable biomaterials have been developed as promising systems for the local delivery of therapeutic agents to cancerous tissues. Due to the unique characteristics of biocompatible polymers, they have been used in different approaches of localized drug delivery to ensure accurate control over the release of chemotherapy drugs, genes, proteins, and peptides. In this review article, the recent progress in stimuli-responsive implantable drug delivery systems for preventing tumor recurrence and suppressing tumors are reported. In particular, we reviewed redox-, enzyme-, and pH-responsive systems in programmable drug delivery and the near-infrared-, ultrasound-, electric-, and magnetic-regulated release in on-demand local drug delivery systems. The preparation process, physicochemical characteristics of implantable materials, and kinetic models of the drug release from these platforms were also discussed in detail.

Conversion and storage of solar energy for cooling

A passive no electricity and sustainable cooling on-demand (NESCOD) system can convert and store solar energy for cooling.

Hypoxia driven opioid targeted automated device for overdose rescue

Opioid use disorder has been designated a worsening epidemic with over 100,000 deaths due to opioid overdoses recorded in 2021 alone. Unintentional deaths due to opioid overdoses have continued to rise inexorably. While opioid overdose antidotes such as naloxone, and nalmefene are available, these must be administered within a critical time window to be effective. Unfortunately, opioid-overdoses may occur in the absence of antidote, or may be unwitnessed, and the rapid onset of cognitive impairment and unconsciousness, which frequently accompany an overdose may render self-administration of an antidote impossible. Thus, many lives are lost because: (1) an opioid overdose is not anticipated (i.e., monitored/detected), and (2) antidote is either not present, and/or not administered within the critical frame of effectiveness. Currently lacking is a non-invasive means of automatically detecting, reporting, and treating such overdoses. To address this problem, we have designed a wearable, on-demand system that comprises a safe, compact, non-invasive device which can monitor, and effectively deliver an antidote without human intervention, and report the opioid overdose event. A novel feature of our device is a needle-stow chamber that stores needles in a sterile state and inserts needles into tissue only when drug delivery is needed. The system uses a microcontroller which continuously monitors respiratory status as assessed by reflex pulse oximetry. When the oximeter detects the wearer’s percentage of hemoglobin saturated with oxygen to be less than or equal to 90%, which is an indication of impending respiratory failure in otherwise healthy individuals, the microcontroller initiates a sequence of events that simultaneously results in the subcutaneous administration of opioid antidote, nalmefene, and transmission of a GPS-trackable 911 alert. The device is compact (4 × 3 × 3 cm), adhesively attaches to the skin, and can be conveniently worn on the arm. Furthermore, this device permits a centralized remotely accessible system for effective institutional, large-scale intervention. Most importantly, this device has the potential for saving lives that are currently being lost to an alarmingly increasing epidemic.

Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

Effects of acoustic comfort and advertisement recallability on digital signage with on-demand pinpoint audio system

Digital signage in public spaces is generally silent. However, the emission of sound with the aim of generating recallability produces noise, which can be uncomfortable for passersby in the surrounding space, resulting in a loss of acoustic comfort. In this paper, we implement an On-Demand pinpoint audio system in which sound is delivered only to one person, and only the sound of the advertisement that attracts the person’s attention is played. By applying this system to passersby, this paper examines the system’s influence on advertisement recallability and acoustic comfort. Additionally, we detect the categories of advertisements affecting the advertisement recallability and acoustic comfort when digital signs emit sound.Through experiments with 30 participants, acoustic comfort is maintained and On-Demand System can reduce noise level compared to loudspeaker. In addition, we collected participants’ self-reported recallabilities and analyzed by advertising category. These results show that music-dominating advertisements presented by the On-Demand System were more recallable than silent and other advertisement categories. These results indicate the possibility of personalization of the advertising information and the perceptual spaces, even if the individuals are in public spaces.

Toward securing the control plane of 5G mobile networks against DoS threats: Attack scenarios and promising solutions

With the advent of the fifth generation (5G) technology, a plethora of revolutionary applications can now be supported. This tremendous growth will be certainly accompanied by a wider and fast-evolving security threat landscape, especially when the potential of massive devices connectivity will be fully unleashed. In this paper, we outline the security challenges faced by the 5G radio access network (5G-RAN) control plane as a result of the functional and architectural enhancements made at the radio resource control (RRC) protocol layer. We correspondingly analyze the dynamics of the new 5G RRC three-states model for a machine-type traffic pattern under an attack-free situation. Afterwards, we introduce and describe two RRC-based denial of service (DoS) attacks threatening the 5G-RAN resources availability. In the first threat scenario, the attacker maliciously manipulates the timing of state transitions in an attempt to overload the control plane. The second attack can be carried out through faking huge on-demand system information requests to prevent legitimate consumers from access to the cell and cause failed and interrupted 5G mobile services. Through numerical simulations, we measure the potential impact of these attacks on both 5G devices using the collision probability along with the access delay metrics, and on the critical gNB-centralized unit using the signaling overhead and the resource occupancy time metrics. Our observations reveal that both attacks can have disastrous effects on network stability and resiliency. Finally, some promising solutions are listed, with a special emphasis on injecting randomness into system parameters to complicate the task of designing such DoS attacks.

A Real-Time Low-Power Coding Bit-Rate Control Scheme for High-Efficiency Video Coding in a Multiprocessor System-on-Chip

A real-time high-performance transmission bandwidth-aware (TB-aware) coding bit-rate (CBR) controller design with low power consumption and low hardware complexity is presented in this article for H.265/high-efficiency video coding (HEVC) in a multiprocessor system-on-chip (MPSoC). Previous TB-aware motion estimation designs with CBR-control capability in video coding have focused on algorithm development with precise CBR models, which require a complicated algorithmic derivation according to the system on-demand CBR and are difficult to realize in very large scale integration (VLSI) due to their lack of consideration for hardware implementation and modeling. Consequently, we present a hardware-oriented CBR-control algorithm that uses simple CBR control functions instead of requiring root and exponential operations to realize the real-time low-power design objective for HEVC applications within a mobile MPSoC. Then, an adequate hardware architecture with low hardware complexity is exploited to accomplish a low-power and high-speed VLSI design of a CBR controller for our proposed algorithm. Using diverse video-sized sequences under on-demand system coding-bit-rate constraints, the experimental outcomes demonstrate that the introduced design is capable of low power consumption and high speeds and can utilize low-complexity hardware.

On-demand ridesharing with optimized pick-up and drop-off walking locations

On-demand systems in which passengers with similar routes can share a vehicle are expected to become a relevant part of future mobility, thanks to their flexibility and their potential impact on reducing congestion. Nevertheless, due to the long detours required by a door-to-door scheme, they induce extra costs to the users in terms of delay. In this paper, we face the design of such a system in which users might be requested online to walk towards/from nearby pick-up/drop-off points if this improves overall efficiency. We show theoretically that the general problem becomes more complex (as it contains two sub-problems that extend set-cover), analyze the trade-offs that emerge, and provide a general formulation and specific heuristics that are able to solve it over large instances. We test this formulation over a real dataset of Manhattan taxi trips (9970 requests during one hour), finding that (a) average walks of about one minute can reduce the number of rejections in more than 80% and Vehicles-Hour-Traveled in more than 10%, (b) users who depart or arrive at the most demanded areas are more likely to be required to walk, and (c) the performance improvement of the service is larger when the system receives more trip requests.

Motivation for Carsharing Services: the Role of Sustainability Values

Sharing, collaborative consumption, and on-demand systems, provide various models of peer-to-peer exchanges of tangible and intangible assets. Sharing is believed to demonstrate the basic values of the sustainable development with focus on environmental, economic, and social issues. Carsharing services demonstrate high growth rate with personal safety requirements changing focus with COVID-19 pandemic. The behaviour of carsharing users is determined by several main motives: getting pleasure from and enjoying new technologies, realizing users’ views on reasonable consumption, strengthening one’s reputation among members of the community, and obtaining economic benefits. The article presents the results of carsharing motivation research. Online survey and deliberative research sessions revealed the importance of sustainable development values for the young active consumers.

Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms

Problem definition : We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. Although these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers’ relocation decisions and ultimately the system performance. Academic/practical relevance : Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers’ relocation decisions from a behavioral perspective. A deeper understanding of workers’ behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. Methodology : We use a combination of behavioral modeling and controlled laboratory experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled laboratory experiments. Results : We find that regret aversion plays an important role in workers’ relocation decisions. Regret-averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system. Managerial implications : Our research emphasizes the importance and necessity of incorporating workers’ behavioral biases such as regret aversion into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers’ decision may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.