Set Of Customers Research Articles

STUDY OF MULTI-STAGE SOFTWARE DEPLOYMENT STRATEGYWITH THE EXCLUSION OF SIDE EFFECTS

The article addresses a problem and its solution that arose during onboarding of new subscribers to the existing data import and distribution service. The analysis of all available solutions demonstrates that none fully meet the requirements. The process of searching for the values of the required indicators and the process of creating reports necessarily uses indexes. Messages of different types are placed in their respective MongoDB collections. Over the long period of operation of the repository, up to 64 indexes per collection have accumulated. According to the policies of connecting to the storage (Connect timeout) and waiting for a response (Socket timeout), the need to set such long-time expectations for consumers is unacceptable and usually, by default, is equal to 30 seconds. A multi-step strategy for delivering the solution that allows the service to meet availability requirements was proposed. The novelty of the article is in the description and investigation of a new method of delivering solutions to the software deployment environment—production. The implementation of the described strategy opens a new approach for solving similar classes of problems related to the type of interaction with users, when custom settings are a mandatory part of the service and are guaranteed to carry the threat of service unavailability. This approach opens up new opportunities for achieving high availability of systems, reducing risks and increasing user satisfaction. Based on the analysis of the proposed approaches, the latest strategy of multi-step delivery of the service to users was developed, the result of which was the acceptable response waiting time of up to 5 seconds. The implementation of this strategy contributed to the achievement of the availability of software products - 99.999%, ensuring the reliability and stability of services for end users.

Overcoming the semantic gap in the customer-to-manufacturer (C2M) platform: A soft prompts-based approach with pretrained language models

Customer-to-Manufacturer (C2M) is a strategy in smart manufacturing where customers collaborate with manufacturers for customized product development on an online platform. The platform enables the shift from the traditional manufacturing process, which is driven by research and marketing, toward a customer-centric product development process. However, a challenge arises as customers lack technical knowledge to communicate their product specifications effectively, creating a semantic gap. This paper proposes a soft prompt-based network structure that utilizes pretrained language models to bridge the semantic gap on the C2M platform. To address limited customer needs data and imbalanced classes, a large corpus of product review texts is used to establish a mapping between reviews and product specifications. A smaller set of customer needs text is then employed to adapt this mapping to the target customer needs-product specifications relationship, thereby closing the semantic gap. The experimental results demonstrate the effectiveness of the proposed model adaptation operation and the prompting structure. Additionally, the experiments highlight the robustness of the proposed method against variations in training data size, thereby mitigating the challenges posed by imbalanced classes. The proposed method could potentially bring innovation to product customization and C2M platform development. By bridging the semantic gap, companies can better integrate customers in the co-design process and effectively translate customer needs into actionable product specifications.

Sensing hotel customers distribution and their sentiment variations using online travel agent data: a case of Shanghai star-rated hotels

ABSTRACT Customer numbers and comments have been extensively studied to increase hotel arrivals and loyalty, whereas the specific distribution and sentiment variations have received less attention. This study constructed a data set of hotel customers and the deep learning model was applied to quantify unstructured text, which can mine the distribution and sentiment variation patterns of different star hotel customers. The findings show that customers of different star-rated hotels have different variation patterns of sentiment and distribution. There is a negative correlation between the visit heat and sentiment of some high-star hotels in various administrative regions: regions with lower visit heat have higher sentiment scores. In addition, season and location are important factors that affect customers’ sentiments. This study is the first to use a quantitative approach to examine the distribution and sentiment variation patterns of hotel customers. It provides insights into appropriate marketing strategies for the hotel industry and offers practical suggestions to hotel customers.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

Effect of Resistance Training Programme on Selected Physical and Physiological Variables among under 18-19 Basketball Women Players

A sport is an activity that is governed by a set of rules or customs and often engaged competitively. Sports commonly refers to activities where, the physical capability of the competitor are the sole or primary determinant of the outcome (winning or losing), but the term is also used to include activities such as mind sports (a common name for some card games and board games with little to no element of chance) and motor sports where mental acuity or equipment quality are major factors. Sports are commonly defined as an organized, competitive and skillfull physical activity requiring commitment fair play. Some view sports as differing from games based on the fact that there are usually higher levels of organization and profit (not always monetary) involved in sports. Accurate records are kept and update for most sports at the highest levels, while failures and accomplishments are widely announced in sport news. The term sports are sometimes extended to encompass all competitive activities in which offense and defence are played, regardless of the level of physical activity. Both games of skill and motor sport exhibit many of the characteristics of physical sports, such as skill, sportsmanship, and at the highest levels, even professional sponsorship associated with physical sports.

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon.

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

An Exact Approach to the Multi-Compartment Vehicle Routing Problem: The Case of a Fuel Distribution Company

Over the years, the vehicle routing problem has been studied by several authors, creating several extensions, such as the multi-compartment vehicle routing problem. Several studies in the literature have addressed this problem, but few have solved it through exact approaches owing to model convolution. In this way, a mathematical model is proposed for the multi-compartment vehicle routing problem with time windows, in which three types of fuel products are distributed to a set of customers using a limited homogeneous fleet. The model explicitly considers time windows, as well as regulatory rest times for the drivers and time limits for each trip and for working schedules, addressing a real company’s decision support requirements, which is scarce in the literature. The optimal solution determines, for each vehicle, the distribution route and time to carry out the deliveries with the corresponding loading of products to compartments, complemented by the calculation of carbon emissions. The main objective is to minimize the total distance traveled, which corresponds to the sum of the distances traveled by each one of the allocated vehicles. The results allow the assessment of the solution optimization applied to a set of instances for a Portuguese company to evaluate the performance and compare decision support improvements with current baseline company procedures.

Diferencia del Derecho y la Moral en Huanta. 2022

The purpose of this study was focused on recognizing the differences that exist between the right and the moral that people of the city of Huanta have. The present investigation corresponds to quantitative research, both a theoretical, documentary and/or bibliographic review, as well as a survey of the population of Huanta in order to obtain more precise information and data. It is known that law is a set of principles and norms, and that it generally seeks order and justice, which regulate human relations in society and whose observance can be coercively imposed by a public power. Similarly, morality is a set of customs and norms. Therefore, it will be known which are those customs and norms that are considered good to guide or reproach the behavior of the Huantina community. The main difference, is that the moral norms that Ethics studies and establishes, regulate, only the individual acting; on the other hand, the juridical norms, which comprise the object of Jurisprudence or Law, are regulators of the interindividual acting and refer to the external jurisdiction of man establishing relationships in a regime of coexistence (Monge,2018). On the other hand, the general conditions of duty is the objective of morality, in other words, the general principles that determine the rectitude of human behavior

An improved and extended dual-index multiplexed 16S rRNA sequencing for the Illumina HiSeq and MiSeq platform.

Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms. We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run. The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.

Intolerance among Different Islamic Sects in Pakistan: Causes and Solution

Intolerance has been a serious problem as an inter-communal problem and this problem exists in the form of inter-communal intolerance in Pakistan which leads to violence and bloodshed. Many factors contribute to this intolerance, including sectarian differences in belief and practice, political exploitation of sectarian differences, lack of education and understanding, and the influence of extremism. Due to which Pakistani people are facing many problems. There are many different sects of Muslims in Pakistan, each with its own set of beliefs and customs. These differences can sometimes lead to stresses and even violence between sects. For instance, the Sunni and Shia organizations of Islam have several views about the succession of the Holy Prophet (PBUH), and this has been a wellspring of contention between the two factions for a really long time. Misunderstandings and prejudices between sects can result from the fact that many Pakistanis lack Islamic knowledge. For instance, some individuals may be of the opinion that their particular sect is the only genuine Islamic sect and that all others are heretics. Intolerance and even violence can result from it. The aim of this research work is to explain a comparative analysis of intolerance among different Islamic sects in Pakistan and as well as to find out its causes and solution under the Islamic context. In this study used a qualitative approach to its investigation. The information was gathered from a variety of sources, including websites, books, magazines, news pieces, and research papers. The primary and secondary both sources were used in references. Any society that wants to sustain stability, moral development, and peace has to have a giving and tolerant relationship among its members of all religions and sects. To promote regional security, stability, peace, and order, it is equally important to behave with charity and tolerance. In Pakistan, where people of diverse countries, beliefs, and sects live, tolerance is especially important. There must be religious tolerance for people to coexist peacefully in society. Then the environment of society would be peaceful and calm.

Detailed Cost Analysis of Hydrogen Refueling Costs for Fleets

AbstractThe costs for hydrogen refueling of fleets were calculated by a two‐step approach. Firstly, different hydrogen refueling station (HRS) system configurations were simulated and optimized for various HRS sizes and 350 bar pressure, with the Fraunhofer ISE's toolkit H2ProSim, showing that large‐scale and pipeline‐supplied HRS can reduce the hydrogen refueling costs. Secondly, as customer travel costs to the HRS play an important role, especially in the hydrogen market activation phase, the placement and dimensioning of the HRS network was optimized in a case study for the Upper Rhine region. The analysis shows possible network structure and cost distributions for a set of specific customers. Hydrogen supply costs are highly dependent on the hydrogen demand and spatial distribution of the customers and can drop to values of around € 0.60 kg−1 for depot refueling stations for high‐volume customers. However, some customers cannot be supplied at a reasonable cost.

Interpretation and importance of culture

Cultural value and relevance are acknowledged by people not just in India but also in other parts of the world. Cultural differences have an impact on people’s general living conditions. People learn about conventions, values, standards, and principles through cultures, and these things are thought to be extremely important for improving people’s lives. People learn about other cultures from their teachers and family members starting in their early years. The implementation of cultures is necessary for individuals to enhance their life as they mature. Different communities, such as rural, urban, and tribal ones, have distinct cultures. Every one of these communities lives according to its own set of customs, values, beliefs, and tenets. People from different backgrounds, age groups, and communities will be able to accomplish their professional and personal goals as well as become productive members of society when they understand the value of culture and incorporate it into their daily lives. This study work has focused on three primary areas: the meaning and relevance of culture, the characteristics of culture, and the role of culture in the lives of humans.

Topology reconstruction using time series data in telecommunication networks

AbstractWe consider Hybrid fiber‐coaxial (HFC) networks in which data is transmitted from a root node to a set of customers using a series of splitters and coaxial cable lines that make up a tree. The physical locations of the components in a HFC network are always known but frequently the cabling is not. This makes cable faults difficult to locate and resolve. In this study we consider time series data received by customer modems to reconstruct the topology of HFC networks. We assume that the data can be translated into a series of events, and that two customers sharing many connections in the network will observe many similar events. This approach allows us to use maximum parsimony to minimize the total number of character‐state changes in a tree based on observations in the leaf nodes. Furthermore, we assume that nodes located physically close to each other have a larger probability of being closely connected. Hence, our objective is a weighted sum of data distance and physical distance. A variable‐neighborhood search heuristic is presented for minimizing the combined distance. Furthermore, three greedy heuristics are proposed for finding an initial solution. Computational results are reported for both real‐life and synthetic network topologies using simulated customer data with various degrees of random background noise. We are able to reconstruct large topologies with a very high precision.

‘Breaking the chains’: reflections on the making of an ethnographic documentary on human rights violations against people with mental illness in Indonesia

Breaking the Chains is an ethnographic documentary about the use of physical restraint and confinement of people with mental illness in Indonesia, a practice known as pasung. The film contributes to an understanding of the socio-cultural meanings of pasung, the sets of beliefs and customs that support such practices (including beliefs about mental illness and healing), and the challenges that must be overcome to eradicate this human rights abuse while highlighting the efforts to eradicate this practice at several levels. This article provides a reflection on the author's experiences and motivations for making Breaking the Chains. It also presents an opportunity to reflect upon the use of film as an applied, public and activist research methodology and the limits and possibilities presented by relying on film-documentary as a ‘generator’ of ethnographic knowledge.

Multi-trucks-and-drones cooperative pickup and delivery problem

This study aims to propose a decision methodology on scheduling trucks and drones for truck-and-drone cooperative delivery and pickup system. A fleet contains multiple truck groups; each truck group is a truck with carrying multiple drones. The fleet serves a set of dispersed customers who have the requirements of pickup and delivery services as well as their due time for service. A mixed-integer linear programming (MILP) model is formulated in this study for routing the trucks and drones in the fleet so that each customer’s pickup or delivery requirements could be served by either a truck or a drone before their required due time. For solving the MILP model efficiently, this study designs a novel hybrid algorithm by combining the column generation and the logic-based Benders decomposition. Based on the main frame of column generation algorithm, the hybrid algorithm uses logic-based Benders decomposition to solve the pricing problem, and dynamic programming to solve subproblems of logic-based Benders decomposition for the purpose of accelerating the whole algorithm’s solving process. Numerical experiments are also conducted on the context of the Hangzhou city so as to validate the efficiency of the proposed hybrid algorithm. Some managerial implications are also derived on the basis of some sensitivity analysis. The proposed methodology, i.e., the MILP model and the novel hybrid algorithm, is potentially useful for platform operators who run the truck-and-drone based urban delivery systems.

A data-driven game theoretic multi-objective hybrid algorithm for the Dial-A-Ride Problem with multiple time windows

The Dial-A-Ride Problem (DARP) designs pick-up and delivery routes for a set of customers. It arises in door-to-door transport services tailored to elderly and impaired people. It minimizes operational costs while accommodating as many drivers’ and customers’ constraints as possible; e.g., constraints on transit time and time windows on pick up and drop off.This paper develops a two-layer and a three-layer artificial neural network (ANN) to predict DARP parameters. The ANN is trained with real data provided by a transit agency from the Canadian city/region of Vancouver. Experimental results show that a three-layer ANN with rectified linear activation functions in conjunction with a stochastic gradient descent optimizer provides the most accurate output forecasts.Utilizing the predictions as parameter estimates for a deterministic mean DARP with multiple time windows (DARPMTW), this paper models the problem as a Company-Drivers-Customers’ theoretic game that minimizes the total route duration and maximizes the minimal satisfaction of the drivers and the customers. It shows that non-dominated Pareto feasible solutions satisfy the Nash equilibrium conditions of the game-theoretic model. It then creates a multi-objective hybrid adaptive large neighborhood search (DD-HALNS) to estimate the Pareto front. The multi-objective DD-HALNS algorithm confines its search to feasible solutions that satisfy the Nash equilibrium conditions and employs local search operators based on their success rates. The operators are controlled by a learning mechanism that utilizes previous successful moves, cost savings, and utility maximization. Four hybridization operators are applied, namely Simulated Annealing, Tabu Lists, Genetic Crossover, and Restarts. The results of experiments conducted on real-world DARPMTW instances demonstrate the ability of the multi-objective DD-HALNS to enhance the best-known routing solutions, as well as its implementability.

Supply chain design and planning for the carpet industry considering recycling processes

This paper introduces a mixed-integer linear programming model to tackle the design and planning problem of Closed Loop Supply Chains in the carpet industry. A distinctive feature of the approach is handling different recovery processes involving dissimilar activities and flows of recovered materials. Moreover, the reverse network facilities and logistics are completely integrated with the forward network structure, in order to obtain a high level of customer supply while considering economic goals. In addition, the economies of scale for the recycling facilities are also explicitly handled. The model allows to determine the products and quantities to be manufactured, transported, stored, and recycled to cover the demand of a set of customers. The objective function is to maximize the profit of the whole life-cycle of products. An example based on an industrial case that involves the recovery of raw materials is presented to show the relevance of the method.

A route-based algorithm for the electric vehicle routing problem with multiple technologies

We consider a variant of the electric vehicle routing problem: a fleet of identical vehicles of limited capacity needs to visit a set of customers with given demands. An upper limit is imposed on the duration of the routes. Vehicles have limited autonomy: they may need to stop en-route at recharge stations. Recharges can be partial and multiple recharge technologies are available at stations, providing energy at different costs and different recharge rates.We present a new a branch-and-price algorithm, that relies on an extended formulation having one variable for each possible depot-to-depot route of each vehicle, implicitly encoding also recharge plans. We design ad-hoc pricing algorithms, which exploit a novel encoding of recharge plans, allowing for efficient bi-directional dynamic programming techniques.Extensive computational results show our approach to clearly outperform previous ones from the literature, being able to solve instances with up to 30 customers, 5 stations, 7 vehicles and 3 technologies to proven optimality within some minutes on a standard PC.

The multi-visit drone-assisted pickup and delivery problem with time windows

We consider a new combined truck–drone routing problem with time windows in the context of last-mile logistics. A fleet of trucks, each equipped with an identical drone, is scheduled to provide both pickup and delivery services to a set of customers with minimum cost. Some customers are paired, in that the goods picked up from one must be delivered to the other on the same route. Drones are launched from and retrieved by trucks at a pool of designated stations, which can be used multiple times. Each drone can serve multiple customers in one flight. We formulate this problem as a large-scale mixed-integer bilinear program, with the bilinear terms used to calculate the load-time-dependent energy consumption of drones. To accelerate the solution process, multiple valid inequalities are proposed. For large-size problems, we develop a customised adaptive large neighbourhood search (ALNS) algorithm, which includes several preprocessing procedures to quickly identify infeasible solutions and accelerate the search process. Moreover, two feasibility test methods are developed for trucks and drones, along with an efficient algorithm to determine vehicles’ optimal waiting time at launch stations, which is important to consider due to the time windows. Extensive numerical experiments demonstrate the effectiveness of the valid inequalities and the strong performance of the proposed ALNS algorithm over two benchmarks in the literature, and highlight the cost-savings of the combined mode over the truck-only mode and the benefits of allowing multiple drone visits.