Tight Resource Research Articles

Impact of China’s outward foreign direct investment on environmental pollution in the home country

In the context of economic globalization and increasingly tight resource and environmental constraints, the environmental effect of open economy has been highly concerned by the academic community. With the implementation of the “Going Global” campaign and the “Belt and Road Initiative”, China’s OFDI has been expanding. What is the impact of the rapid growth of OFDI on the home environment? How can China improve the quality and level of OFDI in order to promote domestic green development under the dual context of constructing a new dual circulation development pattern and advocating green development? These questions deserve further discussion. Most of the existing literature focuses on the empirical interpretation of OFDI’s home country environmental effect by using linear models, while few involve nonlinear models. The literature on the important role of financial development in home country and the threshold effect of OFDI on home country environment is even less common. In view of this, based on the mechanism of argumentation and propositional inference, this paper calculated the environmental pollution index of 30 provinces in China from 2003 to 2017 by using China’s provincial panel data of these years and selecting industrial waste water, industrial waste gas, industrial SO 2 , and industrial soot emissions and industrial solid waste production amount index. Then, this paper empirically studied China's OFDI home environment of financial development threshold effect by constructing financial development as the threshold variables panel threshold model. The results showed that: ① China’s OFDI had a significant impact on the environment of the home country, based on the level of financial development of the home country. When China’s financial development level was lower than the first threshold value, China’s OFDI had a significant restraining effect on environmental pollution in the home country. When China’s financial development level crossed the first threshold value but did not exceed the second threshold value, China’s OFDI significantly enhanced its inhibiting effect on environmental pollution in its home country. When China’s financial development level was higher than the second threshold value, the inhibiting effect of China’s OFDI on environmental pollution in the home country was further enhanced. ② In the sample period, the financial development level of most provinces in China was not high. Only a few provinces with high financial development level had a strong inhibiting effect on environmental pollution in their home country, and the provinces with a high degree of coupling and coordination between OFDI and financial development tended to have a low level of environmental pollution. Accordingly, this paper proposes to optimize the financial service environment of OFDI, improve the green technology content of OFDI, and strengthen the original innovation and re-innovation ability of green technology.

Why Nursing Home Residents Use Social Network Systems: An Attachment Perspective.

Most long-term care facilities can offer residents’ with sufficiently material and physical care, but psychological support may not be always provided due to the tight financial budget or labor resources. Residents’ isolation and loneliness then become a big issue, especially for the residents. Social network systems (SNS) have been proved to be a more effective information transmission channel for thoughts, perspectives, and information sharing than traditional channels such as microblogging, e-mails, or telephones. This study conducted a quasi-experiment to identify factors that influence residents’ intention of using SNS and the impacts of SNS on them in a long-term care facility. The results showed that residents’ attached motivation of personal interacting is a significant factor that influences their intention to use the social network platform. Meanwhile, both the loneliness and depression scales of the participants were decreased significantly.

Advances and challenges in hydraulic fracturing of tight reservoirs: A critical review

The hydraulic fracturing technology has been widely utilized to extract tight resources. Hydraulic fracturing involves rock failures, complex fracture generation, proppant transport and fracture closure. All these behaviors affect the productivity of fractured wells. In this work, the advances and challenges in hydraulic fracturing development of tight reservoirs are summarized from following aspects: the hydraulic fracture propagation, the proppant transport and distribution in hydraulic fractures, the calculation of hydraulic fracture conductivity, and productivity and/or pressure analysis model of multi-stages fractured horizontal wells. Current fracture propagation simulation methods generate only limited propagation paths and cannot truly reflect the complexity of the propagation. The current proppant migration and distribution research is mainly focused on indoor experimental studies of proppant migration in a single fracture or branched fracture, and simulation studies on proppant migration and distribution in a small-scale single slab fracture. Whereas fractures formed after hydraulic fracturing in tight reservoirs are generally complicated. There is a lack of models for calculating complex fracture conductivity that take into consideration the effect of proppant placement and proppant distribution in fractures, fracture surface roughness and dissolution, diffusion, deposition, elastic embedding, and creep caused by stress. The productivity models of fractured horizontal wells are mostly conducted based on the original reservoir fluid saturation and pressure distribution. Most of the studies are focused only on one aspect of the fracturing process. Predications of well performance after fracturing based on these studies are often inconsistent with actual field data. The paper also discusses the future research directions of fracturing in tight reservoirs and the results may be used to promote the development of tight reservoirs.

Tight Gas Potential Status, Obstacles and the Way Forward in Meeting Future Energy Needs of Pakistan

Production from unconventional tight gas resources plays significant role in energy market. It is commonly held that the tight gas has gained considerable importance in meeting energy needs and is widely developed across the world. It is also worth noting that several countries are engaged in pursuing the tight gas resources development to meet energy challenges. However, the copious concerns have emerged to technical and economic development of tight gas potential. Hence, it is essential to accurately assess the resources and to address the key technical issues and economic feasibilities that will support in mitigating the risk and to increase the investment opportunities. This paper provides a systematic review about current status, field development best strategies; in addition, the paper also addresses the obstacles and issues for development of tight gas potential in Pakistan. Although, the efforts made by the Exploration and Production (E&P) firms operating in Pakistan are not very encouraging for tight gas resources development. Hence; this requires further improvements and accurate assessments to develop tight gas resources effectively. Thus, for accurate assessment of tight gas potential, various imperative approaches, ideas, considerations, several challenges and the best strategies for field development are highlighted that offers significant improvement in materializing the future trends in tight gas potential development. The paper will be a kind of helpful tool for the policy makers, potential investors and industry professionals working in Pakistan’s E&P industry.

Winning Lottery Tickets in Deep Generative Models

The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers proposing effective ways of finding winning tickets in classification problems. In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. We show that the popular iterative magnitude pruning approach (with late resetting) can be used with generative losses to find the winning tickets. This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models. Furthermore, we show the practical benefits of lottery tickets in generative models by detecting tickets at very early stages in training called early-bird tickets. Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints. These results out-perform existing early pruning methods like SNIP (Lee, Ajanthan, and Torr 2019) and GraSP(Wang, Zhang, and Grosse 2020). Our findings shed light towards existence of proper network initializations that could improve convergence and stability of generative models.

Allocation of health care under pay for performance: Winners and losers

Many physicians receive a payment for their performance (P4P). This performance is often linked to a health target that triggers a bonus when met. For some patients the target is easily met, while others require a significant amount of care to reach the target (if ever). This study contributes to the literature by providing evidence of how P4P affects allocation of care across patients with low and high responsiveness to treatment compared to a fixed payment, such as capitation and salary, under different degrees of resource constraint. Our evidence is based on a controlled laboratory experiment involving 143 medical students in Denmark in 2019. We find that patients who have the potential to reach the health target, gain care under P4P, whereas patients with no potential to reach it, may receive less care. Redistribution of care between patients under P4P arises when physicians are resource constrained. As many physicians are currently operating under tight resource constraints, policymakers should be careful to avoid unintended inequalities in patients’ access to health care when introducing P4P. Risk-adjusting the performance target may potentially solve this issue.

Media motion-based resource distribution for mobile video networking

Wireless video communication is challenging due to vulnerability of media bitstreams to channel distortions. Investigation has been carried out on wireless video channel under tight networking resource budget. One of the challenges is the impact of channel errors on the quality of media streams with high motion activity. Motion activity in this context defines the magnitude of activity displacement in video sequence. Based on the analysis, Media Motion-based Resource Distribution (MRD) is proposed to maximize the average received video quality over wireless system, by regulating the resource distribution of the media streams based on their motion activity characteristics. Experimental results demonstrate that the proposed scheme can improve the average received video quality performance under tight resource constraints budget.
 Keywords: Wireless video communication, resource constraints, received video performance, media motion

Strengthening the digital rock physics, using downsampling for sub-resolved pores in tight sandstones

Employing digital rock physics (DRP) to study the petrophysical characteristics of tight sandstones is a challenging and problematic issue. The non-resolved individual pores (sub-resolved pores), which cannot be determined in micro x-ray Computed Tomography (μxCT) images, is the main reason for incorrect calculations of the petrophysical properties with DRP. We present a workflow to resolve the matter. We compute the porosity and permeability for the μxCT images of samples from tight sandstone. After that, we apply downsampling. For each downsampled model, the porosity has been calculated. Then, by approximation of the trendline into the porosity data, the porosity for an effective resolution of 0 μm per voxel (μm/vox), is estimated. Next, we calculate the bias to correct the computed permeability. The match between calculated parameters and lab measurements proves that the proposed workflow is a reliable approach for practical DRP applications. Further analysis shows that taking advantage of image processing for studies of tight porous media leads to more accurate results than using the conventional petrophysical models of tight resources to map the porosity to permeability.

Production Scheduling of Open Pit Mine Using Sequential Branch-and-Cut and Longest Path Algorithm: An Application from an African Copper Mine

Open pit mine production scheduling assigns mining blocks in different production periods for maximising profits after satisfying geotechnical and operational constraints. In this paper, two Open pit mine production scheduling models were applied in an African copper deposit. The first model is a traditional model with more tight resource constraints; the second model is a more robust model where resource constraints are relaxed by penalizing the objective function. Both the models were solved using two step algorithms: (a) year wise production scheduling using a sequential branch-and-cut algorithm; and (b) an iterative longest path algorithm to improve the solution generated from branch-and-cut. Results demonstrated that due to the tight constraints in Model 1, the optimizer was unable to generate a feasible solution after the first period, therefore the lower limit metal production constraint was eliminated to generate a feasible solution; however, Model 2 was able to generate a feasible solution for all periods. Results show that both the models generated nearly the same amount of ore, waste, metal content, and mine life. Model 2 generates relatively more net present value as compared to Model 1, whereas, the computational time required for solving the scheduling problem is relatively less for Model 1 than for Model 2.

Teacher Fellow accreditation – a madrigal of voices! But is it the right tune? Tensions of standardisation, engagement and neoliberalism

PurposeThe purpose of this paper is to explore the impact competency frameworks as standardisation can have on the employee engagement of academic staff within higher education (HE) through their employment as managerial tools.Design/methodology/approachA literature review is conducted from which the conditions for effective competency frameworks are evaluated and the influence of changes in the HE environment in the form of political agendas and tight resources are explored.FindingsThis paper provides insights into the dynamics of public service modernisation and the tensions between the dominant discourse of managerialism and the degree of agency afforded to professional academics. It highlights the relevance of informal peer relationships in setting the climate to generate collegial bonding and professional engagement that underpin successful teacher fellowship accreditations. It further highlights the key role managers play in this process and provides a conceptual framework highlighting the dynamics and combined effect of employee engagement and competency frameworks set within complex HE environment.Practical implicationsThis paper brings together the prerequisites for effective implementation of competency frameworks to implement successful employee engagement strategies set within the complexities of the HE context, which has not been studied to date. Armed with such insights, Human Resource Development (HRD) departments and universities can implement competency assessments that generate greater staff engagement.Originality/valueThe paper provides a critical approach in reviewing the impact of Continued Professional Development and its link to professional status and thus helps British Universities and others to understand how the mechanisms at work affect engagement levels of academic staff. Armed with this depth of understanding of how the change initiative works, with whom and under what circumstances, universities will be better able to meet target UK Professional Standards Framework membership levels required by the higher education academy (HEA) and, subsequently, the HEA to meet their targets for the government.

Design Patterns for Resource-Constrained Automated Deep-Learning Methods

We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.

TZmCFI: RTOS-Aware Control-Flow Integrity Using TrustZone for Armv8-M

Control-Flow Integrity (CFI) is a class of defensive techniques against control-flow attacks such as Return-Oriented Programming. We propose a light-weight CFI scheme for RTOS-based applications, TZmCFI, which utilizes TrustZone for Armv8-M, a hardware-assisted security feature for embedded systems with tight resource constraints. TZmCFI embodies several existing CFI techniques to provide a comprehensive protection. The traditional shadow stack technique is used to ensure stack integrity and validate function returns. To protect exception handlers, TZmCFI extends shadow exception stacks, which are a variant of the traditional shadow stack technique we proposed in our previous work, for RTOS integration and performance improvement. We conducted an experiment on Arm Versatile Express Cortex-M Prototyping System (V2M-MPS2+) to evaluate the run-time overhead of the proposed system.

Factors affecting the willingness of agricultural green production from the perspective of farmers' perceptions

In the context of increasingly tight resource and environmental constraints, understanding why and how farmers were willing to engage in agricultural green production (AGP) had become a major practical issue that needed to be answered to promote sustainable development of agricultural economy. This study collected first-hand data of 645 grain growers and used structural equation model (SEM) to explore the impacts of farmers' perceptions on AGP willingness focusing on perceived value and its antecedent factors, namely perceived benefits and perceived risks. Results showed that perceived value and perceived benefits had significantly positive impacts on AGP willingness, while the impacts of perceived risks was significantly negative. Furthermore, it was found that although the direct effect from perceived value (0.364) on farmers' AGP willingness was greater than perceived benefits and perceived risks, the decisive factor that ultimately played a key role in AGP willingness was farmers' perceived benefits (0.501). This was because perceived value played an actively mediating role of 23.1% in the path from perceived benefits to green production willingness. Besides, the multi-group analysis (MGA) found that the variable ‘whether to join a cooperative or not’ had a positively moderating effect on the relationship between farmers' AGP perceived value and willingness. Meanwhile, the variable ‘joining cooperative group’ (0.552) had a greater impact on the perceived value-green production willingness path of farmers' AGP than the variable ‘non-joining cooperative group’ (0.251), which indicated that farmers' AGP willingness could be enhanced by joining a cooperative. This study provides some enlightenment and reference for policy makers and practitioners to design or adjust programs related to reducing rural environmental pollution and implementing AGP.

Process integration for emerging challenges: optimal allocation of antivirals under resource constraints.

The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.Graphic abstract

Districting for parcel delivery services – A two-Stage solution approach and a real-World case study

This paper studies a real-world problem arising in the context of parcel delivery. Given a heterogeneous set of resources, i.e., different drivers and different vehicles, the problem for each day consists of assigning a driver and a vehicle to each customer requiring service. Two conflicting aspects must be taken into account. On the one hand, service consistency is desirable, meaning that a customer should always be served by the same driver. On the other hand, daily demand fluctuations and tight resource constraints prohibit fixed resource assignments. With the aim of finding a reasonable compromise between these aspects, we propose a novel two-stage districting approach, which establishes delivery districts in the first stage and adapts them to the daily demand realizations in the second stage. For the first stage problem we propose three models that differ in the level of detail of their input data, their expected compliance with service consistency and the driver’s contractual working times, and their computational effort. Our two-stage approach merges the two dominant approaches in the literature, which either determine a priori routes and then adapt them on a daily basis, or derive fixed service regions for drivers. We present a case study based on a real-world data set. The results highlight the differences between the three first stage models and show that only few adaptations of the districts are necessary in the second stage to achieve feasible daily delivery tours along with a very good workload balance for drivers. We also analyze the effects of a homogeneous vs a heterogeneous fleet, of full time drivers vs full and part time drivers, and of the location of the depot and the length of the planning horizon.

Proactive Management of Regulatory Policy Ripple Effects via a Computational Hierarchical Change Management Structure

The paper proposes a novel computational impact analysis framework to proactively manage dynamic constraints and optimally promote the inception of central banks’ regulatory policies. Currently, central banks are encountering contradictory challenges in developing and implementing regulatory policy. These constraints mainly comprise of incomplete or anomalous information (information asymmetry), and very tight temporal and resources limitations (bounded rationality) when the efficiency of a policy is determined at a system-level. The complex relationships of the policy attributes and their interactions generate very dynamic emergent behaviours due to the complex causal relationships. This paper adopted and tailored the hierarchical change management structure framework to design a first step framework called ‘computational regulatory policy change governance’. The methodology uses interviews, focus-group workshop and the application of empirical data. The results of the evaluation and case study validate its applicability in computing policy parameters and the impacts of their interactions. The evaluation of the framework gained a remarkable score, averaging a 130 per cent improvement compared to the existing methods. However, the research paper used a single case study, and its outcomes require further evaluation and testing. Accordingly, we invite regulators, banks, scholars and practitioners to explore the uniqueness and features of the proposed framework.

The primary controlling parameters of porosity, permeability, and seepage capability of tight gas reservoirs: a case study on Upper Paleozoic Formation in the eastern Ordos Basin, Northern China

Tight sandstone gas (hereafter “tight gas”) has become a subject of unconventional gas exploration globally. The large-scale development and use of tight gas resources in the USA, in particular, facilitated the rapid rebound of natural gas production in the USA, in addition to driving the rapid development of tight gas worldwide. In the eastern Ordos Basin, the Upper Paleozoic feature includes multiple layers of gas, a shallow depth, and notable potential for exploration and development. However, the reservoirs in the area are relatively tight, exhibit strong heterogeneity, and possess a complex micropore structure, thus restricting the effective economic development of oil and gas. Thus, research on the primary parameters controlling pore throat structure and the seepage capability of low-permeability reservoirs will be beneficial for the efficient exploration and development of natural gas in the eastern Ordos Basin. The parameters of reservoir porosity and percolation ability, as well as permeability, were analyzed using systematic sampling of the of the Upper Paleozoic Benxi, Taiyuan, and Shanxi Formations in the eastern Ordos Basin, constant-rate mercury injection experiments, nuclear magnetic resonance analysis, and gas–water-phase experimental studies. The results indicate that reservoir porosity is controlled by the effective pore volume and number, whereas permeability is controlled by the largest throat radius, rather than the average. The effective pore volume controls the movable fluid saturation, while reservoir percolation capability is controlled by the effective pore volume, irreducible water saturation, and size of the gas–water two-phase seepage zone.

Ultra-Reliable Energy-Efficient Cooperative Scheme in Asynchronous NOMA With Correlated Sources

Massive Internet-of-Things is proposed in fifth generation networks to serve the sporadic traffic generated by devices operating under tight resource constraints. In these applications the overhead for synchronization and control functions is comparable to the data size, hence the control/data ratio is very unfavorable. The proposed techniques in this paper take advantage of the fact that transmitters are privy to whole data in order to boost the spectral and power efficiency, while increasing the reliability. We propose identical content transmission over identical content transmission over NOMA (ICToNOMA) for transmitters with correlated sources, who cooperatively combine and transmit identical messages over consecutive data packets. Our proposed redundant transmission of data is not as straightforward as it seems, considering the asynchronous reception of the data streams at the receiver. Traditionally, it is believed that the substantial spectral efficiency (SE) achievements in nonorthogonal multiple access (NOMA) could be jeopardized in the asynchronous channels. We investigate the potency of successive interference cancellation (SIC), as the main block in current NOMA receivers, in asynchronous channels. By applying water-filling and geometric power allocation, we show that the SE degradation is caused by the nature of SIC. Moreover, we demonstrate that the SE is improved in asynchronous NOMA and ICToNOMA, by managing the channel’s memory and correlation instead of canceling it. In addition, we propose our iterative joint detection and decoding (IJDD) receiver to outperform SIC in asynchronous NOMA receivers. Our extensive simulations show that ICToNOMA can outperform NOMA by providing a considerable boost in the channel reliability while increasing the spectral and power efficiency.

Creating room for evidence-based practice: Leader behavior in hospital wards.

The integration (routinizing and sustaining) of evidence-based practice (EBP) into hospital management is a key element for improving patient safety and ensuring better patient outcomes. Hospital managers and clinical leaders play crucial roles in this integration. Interactions between leaders and integration context influence the improvement's quality, but leader-based actions that are effective for improving nursing practice remain unclear. The relationship between leaders could also either hinder or enable this implementation process. The aim of this study was to generate a theory about patterns of leader behavior that leaders are engaged in when attempting to integrate EBP in a clinical setting. We used a classic grounded theory methodology to generate a substantive EBP theory. In this study, through participant observation, we observed 63 nurses (15 specialist, 39 registered, and 9 assistant nurses). From these, five ward leaders (two head nurses, one assistant head nurse, and two teaching nurses) participated in individual interviews, and 18 clinical nurses participated in four focus groups. "Creating room for EBP" emerged as a theory for explaining the way in which the leaders attempted to resolve their main concern: How to achieve EBP treatment and care with tight resources and without overextending the nurses. Creating room for EBP encompasses a process of interactions, including positioning for, executing, and interpreting responses to EBP.

Local trend discovery on real-time microblogs with uncertain locations in tight memory environments

This paper presents GeoTrend+; a system approach to support scalable local trend discovery on recent microblogs, e.g., tweets, comments, online reviews, and check-ins, that come in real time. GeoTrend+ discovers top-k trending keywords in arbitrary spatial regions from recent microblogs that continuously arrive with high rates and a significant portion has uncertain geolocations. GeoTrend+ distinguishes itself from existing techniques in different aspects: (1) Discovering trends in arbitrary spatial regions, e.g., city blocks. (2) Considering both exact geolocations, e.g., accurate latitude/longitude coordinates, and uncertain geolocations, e.g., district-level or city-level, that represents a significant portion of past years microblogs. (3) Promoting recent microblogs as first-class citizens and optimizes different components to digest a continuous flow of fast data in main-memory while removing old data efficiently. (4) Providing various main-memory optimization techniques that are able to distinguish useful from useless data to effectively utilize tight memory resources while maintaining accurate query results on relatively large amounts of data. (5) Supporting various trending measures that effectively capture trending items under a variety of definitions that suit different applications. GeoTrend+ limits its scope to real-time data that is posted during the last T time units. To support its queries efficiently, GeoTrend+ employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In peak times, the main-memory optimization techniques are employed to shed less important data to sustain high query accuracy with limited memory resources. Experimental results based on real data and queries show the scalability of GeoTrend+ to support high arrival rates and low query response time, and at least 90+% query accuracy even under limited memory resources.