High Penalty Research Articles

Research on Vehicle Scheduling Optimization Model of Precast Concrete Components Considering Carbon Emission Cost

The distribution of precast concrete (PC) components has the characteristics of high time penalty costs, transportation costs, long-term accumulated carbon emission, and long unloading waiting time. These characteristics result in increased distribution costs. Aiming at this problem, the vehicle scheduling (VS) optimization model was proposed to minimize total distribution costs, including transportation costs, unloading waiting costs, carbon emissions costs, and so forth. The proposed model was solved using the genetic algorithm. Finally, the proposed model is verified by a simulation study. The main conclusions are: 1) A PC-component VS model aiming at minimum cost is established, which can provide a better scheme for PC-component VS on the basis of minimizing fixed cost, transportation cost, unloading waiting cost, penalty cost, and carbon cost. 2) This paper constructs a VS model that reflects the loading characteristics of PC components. The type of PC component is added as a judgment variable in the model, and the actual loading weight is calculated according to the type of PC component and the loading requirements. It improves the accuracy of PC component VS results and expands the PC component VS model. 3) Based on this model, some valuable parameters have been sensitively analyzed and some valuable conclusions have been drawn to provide some references for similar cases. For example, carbon emission increases with the increase of freight volume and so on. The result of this study is to provide a cost-minimization VS model with PC components.

Rimessione della censura dell’aborto nei codici di diritto canonico della Chiesa cattolica

The value of the great gift of life has always been cherished and defended by the Church. Throughout the centuries, basing herself on Holy Scripture, she has condemned every form of violence directed against life, punishing such crimes with sanctions and penalties. Among these, abortion is considered one of the most serious offences, and the Church punishes it with the highest penalty: excommunication, a punishment included in the 1917 Code of Canon Law, the 1983 Code of Canon Law and the Code of Canons of the Eastern Churches. But being both mother and teacher, the Church not only punishes but, following Christ’s example, forgives sins and censures. She does so even in the case of abortion, and this article presents the absolution of abortion censure in the three law codes of the Catholic Church.

Constitutional Examination of Imposing High Administrative Penalties to Deter House Speculation

Constitutional Examination of Imposing High Administrative Penalties to Deter House Speculation

The causal effects of working time on mental health: The effectiveness of the law reform raising the overtime wage penalty

AbstractThe present paper reexamines the causal effect of working hours on workers’ mental health. We utilize Japan's 2010 reform of the Labor Standards Act as a social experiment to examine how the increased wage penalty for long overtime work affects working hours and workers’ mental health. Utilizing a unique panel dataset containing health behaviours as well as individual, household and workplace characteristics of male workers, we find that the wage penalty reform indeed succeeded in reducing overtime hours and total working hours and that the reductions contributed to better mental health of workers. Further empirical investigation suggests that the reduction effect of the reform on working time is homogeneous among age groups; however, the harmful effect of working time on mental health is large and statistically significant among young workers. Our results suggest that setting a high wage penalty for long overtime work effectively reduces overtime work and improves workers’ health outcomes, particularly for young people.

Chemical looping CO2 capture and in-situ conversion as a promising platform for green and low-carbon industry transition: Review and perspective

Industrial flue gas and solid waste with the characteristics of large emissions and complicated distributions are two issues for industry, due to that sequential CO2 capture and utilization for flue gas shows high energy-cost penalties and solid waste faces with low reutilization ratio. As a powerful process intensification strategy, chemical looping CO2 capture and in-situ conversion (CL-ICCC) provides an effective solution to handle those two issues. However, current researches on CL-ICCC aim to develop process configurations, design bifunctional materials, and reveal reaction mechanisms, which ignore how to apply CL-ICCC for realizing flue gas purification and solid waste reutilization simultaneously in industry. Herein, after reviewing the current status of CL-ICCC for flue gas upgrade, industrial solid waste reutilization and industrial system integration, a perspective is proposed to highlight CL-ICCC as a promising platform for green and low-carbon industry transition. In the proposed system, industrial flue gas enters the capture reactor and reacts with industrial solid waste derived bifunctional materials to separate CO2 from other impurities. In the conversion reactor, reduction agents are introduced to spill CO2 over bifunctional materials and convert it into value-added chemicals. This review and perspective provide a new pathway to establish a low-carbon and self-digestion industrial system.

Design and exergy analysis of highly efficient chemical looping reforming processes of coking dry gas in double and triple reactors

Coking dry gas is a kind of refinery dry gas, which can be used as a feedstock for hydrogen production, but its steam reforming has the problems of high energy penalty and CO2 emissions. Therefore, novel processes of coking dry gas chemical looping reforming to produce CO and H2 using Fe-Cu as oxygen carrier were proposed in this study. Firstly, two processes of two-reactor for syngas production and three-reactor for syngas-high purity hydrogen production using external-heating were designed and the effects of the temperature difference between fuel and air reactor and the heat carrier addition were studied. When the temperature difference is increased to 150 °C, the exergy efficiencies of the two processes are 70.87 % and 77.13 % and the fuel reactors both produce 2.21 kmol syngas per kmol dry gas. While the inclusion of a steam reactor in the three-reactor system generates an additional 0.61 kmol high-purity hydrogen per kmol dry gas, thereby achieving higher efficiency compared to the two-reactor system. Heat carrier was used to fully transfer the heat from the air reactor to fuel reactor to obtain two auto-thermal systems. Their exergy efficiencies are as high as 84.39 % and 84.58 % and the fuel reactors both produce 3.02 kmol syngas/kmol dry gas, while the high-purity hydrogen production of three-reactor auto-thermal system is only 0.05 kmol/kmol dry gas. Obviously, the external-heating three-reactor process has notable advantages compared to the two-reactor. For auto-thermal systems, this work suggests developing high-temperature resistant Fe-Cu composite oxygen carriers and heat carriers with larger heat capacity to decrease the usage of heat carriers.

Factors Contributing to Tax Compliance among Small and Medium Sized Enterprises: A Case of Selected SMEs in Iringa Municipality

The purpose of the study was to determine factors contributing to tax compliances among small and medium sized enterprises (SMEs) in Tanzania, with reference to selected SMEs in Iringa Municipality. The study objectives were; to evaluate the contribution of taxpayer’s education, to determine the contribution of tax rate and to assess the contribution of tax penalties on tax compliance among SMEs in Iringa Municipality. The targeted population were 172 SMEs where a sample size of 63 SMEs was drawn using descriptive research design. The study used stratified sampling technique and data were analysed using linear regression through ordinary least squares estimator showing relationship between tax compliance among SMEs as dependent variable to taxpayer education, tax rate and tax penalties as independent variables. Taxpayer education and tax penalties had a positive relationship and tax rate showed the negative relationship with tax compliance among SMEs. The results from the regression and t- test showed that taxpayer education, tax rate and tax penalties have a significant impact on tax compliance since the p-values were less than 0.05. In the case of the normal regression output the adjusted R2 equals 0.534 implying that the model explains about 53.4% variations in tax compliance among SMEs. The study concludes that tax payers’ education has positive effects on tax compliance while high tax rates discourage the SMEs on tax compliance. Nonetheless, high tax penalties positively influence tax compliance.. The study recommends fair and at moderate level tax levels and there should be more emphasis on tax payers’ education and awareness to increase tax compliance

Decision Making in a Closed-Loop Supply Chain with a Waste Management Program: Manufacturers’ Take-Back Activity and Governmental Subsidies for Remanufacturing

As awareness of climate change increases, diverse initiatives such as subsidies for remanufactured products and take-back programs for end-of-life products have been taken to conserve energies and materials. This paper explores how the subsidy program affects manufacturer’s take-back activity in a closed-loop supply chain and also analyzes how a coalition between a retailer and a remanufacturer affects the equilibrium decisions. Major findings of this paper reveal that (i) when a take-back program is implemented, the government imposes a high penalty on products that are not collected, thereby encouraging manufacturers to collect more used products, (ii) implementing a take-back program in conjunction with a subsidy program results in a greater reduction in environmentally negative impacts and an enhanced social welfare compared to implementing them separately, and (iii) a coalition between a retailer and a remanufacturer results in lowering the penalty imposed to a manufacturer, which leads to lowering the quantity of the collected and remanufactured products.

Coding Intrinsic Disorder into DNA Hybridization Probes Enables Discrimination of Single Nucleotide Variants over Wide and Tunable Temperature Ranges

AbstractDNA hybridization probes are commonly used tools to discriminate clinically important single nucleotide variants (SNVs) but often work at elevated temperatures with very narrow temperature intervals (ΔT). Herein, we investigated the thermodynamic basis of the narrow ΔT both in silico and experimentally. Our study revealed that the high entropy penalty of classic hybridization probe designs was the key attributor for the narrow ΔT. Guided by this finding, we further introduced an entropy‐compensate probe (Sprobe) design by coding intrinsic disorder into a stem‐loop hybridization probe. Sprobe expanded ΔT from less than 10 °C to over 30 °C. Moreover, both ΔT and the optimal reaction temperature can be fine‐tuned by simply altering the length of the loop domain. Sprobe was clinically validated by analyzing EGFR L858R mutation in 36 pairs of clinical tumor tissue samples collected from lung cancer patients, which revealed 100 % clinical sensitivity and specificity. We anticipate that our study will serve as a general guide for designing thermal robust hybridization probes for clinical diagnostics.

Coding Intrinsic Disorder into DNA Hybridization Probes Enables Discrimination of Single Nucleotide Variants over Wide and Tunable Temperature Ranges.

DNA hybridization probes are commonly used tools to discriminate clinically important single nucleotide variants (SNVs) but often work at elevated temperatures with very narrow temperature intervals (ΔT). Herein, we investigated the thermodynamic basis of the narrow ΔT both in silico and experimentally. Our study revealed that the high entropy penalty of classic hybridization probe designs was the key attributor for the narrow ΔT. Guided by this finding, we further introduced an entropy-compensate probe (Sprobe) design by coding intrinsic disorder into a stem-loop hybridization probe. Sprobe expanded ΔT from less than 10 °C to over 30 °C. Moreover, both ΔT and the optimal reaction temperature can be fine-tuned by simply altering the length of the loop domain. Sprobe was clinically validated by analyzing EGFR L858R mutation in 36 pairs of clinical tumor tissue samples collected from lung cancer patients, which revealed 100 % clinical sensitivity and specificity. We anticipate that our study will serve as a general guide for designing thermal robust hybridization probes for clinical diagnostics.

Flexible-reliable linear AC security constrained unit commitment considering uncertainties and renewable energy sources

In this paper, AC security-constrained unit commitment (AC-SCUC) is presented in the transmission network considering flexibility and reliability limits in the presence of renewable resources, energy storage and demand response plan. This minimization scheme considers the sum of expected operating costs and network reliability minus the benefit of network flexibility as the objective function. This problem is related to AC power flow equations, the operation model of storage, demand response, renewable sources, and non-renewable generation units, technical limits of the network, including indices of operation, reliability, flexibility, security. and the stability of the network. In this plan, load, renewable power, energy price, and availability of network equipment are uncertainties. In this paper, in order to accurately evaluate reliability and flexibility indices, stochastic planning is adopted to model the aforementioned uncertainties. The design has non-linear and non-convex constraints; therefore, to reach a unique solution in low computing time, the linear approximation model is incorporated. AC-SCUC linear modeling along with simultaneous modeling of operation, economic, flexibility, reliability, security, and stability indices along with stochastic modeling of uncertainties to accurately evaluate flexibility and reliability indices are considered as the contributions of this scheme. The obtained numerical results indicate the ability of the proposed scheme in improving the economic and technical condition of the transmission network with regard to the optimal management of energy sources, storage devices, and demand response. So, the scheme can obtain high reliability conditions for the transmission network by considering the high penalty price. Also, by considering a high incentive price for flexibility, non-renewable resources, storage devices, and demand response have been able to achieve high flexibility for the network. Optimal energy management of resources, storage devices, and demand response has significantly enhanced the network's operation status.

Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.

Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes.

Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.

1-Norm twin random vector functional link networks based on Universum data for leaf disease detection

Due to rapid climate change and man-made activities, the types of leaf diseases are gradually increasing. As a result, taking the essential measures to recognize and diagnose the problem becomes a top priority. Though manual techniques for identifying leaf disease have been used in the past, research-based studies may produce a better result and overcome the disadvantages of the manual process. Nowadays, the random vector functional link (RVFL) network gaining popularity day by day due to the better generalization ability for various applications. In the spirit of the popular twin support vector machine (TSVM) and the twin variant of the random vector functional link (TRVFL) network, a novel twin RVFL (TRVFL1norm) is suggested in this work. TRVFL1norm overcomes the inherent limitation of TRVFL, which loses the sparsity due to the use of 2 norm and also gives a high penalty to outlier points which leads to degrade the generalization performance. Moreover, it is well known that Universum data can be used as prior knowledge in the training model. Therefore, to make the model more robust and generalized, we propose another model, TRVFL1norm based on the Universum data (UTRVFL1norm). UTRVFL1norm possesses the advantages of both 1-norm as well as Universum data. For both TRVFL1norm and UTRVFL1norm, few of the components in the output matrix will be zero due to sparsity hence, the resultant classifier must depend on the lesser output nodes compared to TRVFL. To verify model’s performance, we have compared the classification performance of proposed TRVFL1norm and UTRVFL1norm models with support vector machine (SVM), Universum SVM (USVM), twin support vector machine (TSVM), Universum TSVM (UTSVM), random vector functional link (RVFL), multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN) on few interesting benchmark datasets. Further, we have also checked the performance of TRVFL1norm and UTRVFL1norm for leaf disease detection as one of the possible applications. The experimental results indicate the efficiency and applicability of the proposed models.

Truncated Balancing Policy for Perishable Inventory Management: Combating High Shortage Penalties

Problem definition: Motivated by a platelet inventory management problem, we study a fixed lifetime perishable inventory management problem under a general demand process. Determining an optimal ordering policy for perishable inventory systems is particularly challenging because of the well-known “curse of dimensionality.” Approximation policies with worst-case performance bounds have been developed in the literature for perishable inventory systems. However, using real data, we observe that the existing policies tend to underorder when the unit shortage penalty is high, which is an important concern for critical perishable products, such as lifesaving blood products. We seek to address this problem in this paper. Methodology/results: We present a new approximation policy for perishable inventory systems, which we call a truncated balancing (TB) policy. In particular, we first define a new balancing ordering quantity and prove a novel lower bound on the optimal ordering quantity. We then define our TB policy such that the maximum between the balancing ordering quantity and the lower bound is ordered at each period. We prove that when first in, first out is an optimal issuing policy, (1) our proposed TB policy admits a worst-case performance bound of two, and (2) it is asymptotically optimal when the unit shortage penalty goes to infinity. Finally, we present a calibrated numerical study based on real data from our partner hospital and show that our proposed policy performs significantly better than the existing policies in practical scenarios with reasonably high shortage penalties. Managerial implications: Our analysis offers managerial insights for perishable inventory management, especially for systems with an imbalance in underage and overage cost parameters. When the unit shortage penalty is high, simply balancing the underage and overage costs can lead to underordering, whereas our proposed policy effectively addresses this drawback. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0644 .

Constraints in credit accessibility from primary agricultural cooperative societies in Haryana state, India

Abstract Subject and purpose of work: The objective of the study was to identify the constraints in credit accessibility from Primary Agricultural Cooperative Societies in Haryana State, India. Materials and methods: The primary data were used for achieving the objective of the study. The primary data were collected in the second quarter of 2022 by employing the schedule. The multistage purposive cum proportionate random sampling techniques were adopted to select a sample of 540 respondents. The percentage was used for data analysis. Results: The constraints in accessing credit are identified faced by beneficiary farmers and nonbeneficiary farmers. The major constraints faced by the beneficiary farmers in accessing credit from PACS are inadequate credit limit (87.40%), short time to repayment of loan (66.29%), high penalty rate (55.92%), and unavailability of medium-term loans (50.00%). While inadequate credit limit (80.00%), no new accounts opened (72.22%) and fear of being a defaulter (47.78%) were the major constraints faced by the non-beneficiary farmers in accessing credit from PACS. Conclusions: The various constraints were identified in accessing credit from PACS. Credit limits should be extended on the basis of the scale of the finance. Medium-term loans need to be increased.

Blind skew compensation and digital combining with widely linear equalizer for multi-aperture coherent FSO communication.

Coherent digital combining technology using multiple small apertures has a lot of advantages over doing so with a single large aperture, including the effective mitigation of deep fading under strong turbulence, ease of scalability, and potential higher collected optical power. However, the in-phase/quadrature (I/Q) imbalance and I/Q skew induced by manufacturing imperfections of the coherent receiver front end, and the time mismatch caused by the unequal length of multi-aperture branches will induce a high OSNR penalty and reduce the digital combining efficiency, especially when the system scales to a larger number of apertures, such as massive aperture system. In this work, a complex-valued multiple-input multiple-output (MIMO) 4N×2 widely linear (WL) equalizer is designed to combine multi-aperture signals. Using WL complex analysis, a general analytical model is derived and it is indicated that multi-aperture channel equalization and combining operations can be achieved simultaneously using a MIMO equalizer as long as appropriate tap coefficients are selected. Moreover, the feasibility of the proposed WL equalizer is verified by a 10-Gbps PM-QPSK modulation and a 20-Gbps PM-16QAM modulation four-aperture offline simulated turbulence experiment. The four-aperture combining efficiency of PM-QPSK exceeds 96% even at a single-aperture extremely low OSNR of -6 dB, and 80% for PM-16QAM at a single-aperture OSNR of 0 dB.

The Concept ff Handling Overcapacity in Prison Through A Restorative Justice Approach to Drug Abusers

The implementation of the coaching carried out by Prison for the inmates often becomes not optimal due to the complexity of the problems that occur within Prison. One of the root causes of issues at Prison is overcapacity. This paper then tries to see by analyzing the nature of narcotics crime as a particular crime and Indonesia's role in overcoming narcotics crime. Then also visit and explore the implications of overcapacity in Prison and concepts that can be applied to prevent overcapacity in Prison. The research method used in this paper is a normative juridical research method, with the data source used being secondary data obtained through library research. The study results indicate that narcotics crime as a particular crime is transnational and involves international crime organizations. Indonesia's role in overcoming narcotics crime can be seen from the regulation in Law Number 35 of 2009. In this regulation, sellers and dealers are given a high penalty, even death. Sentences should not be given to those who are called victims or addicts. The implications of overcapacity in Prison can result in non-optimal coaching due to the complexity of the problems that occur in Prison. Overcapacity is one of the root causes of issues at Prison. For this reason, it is necessary to apply a concept to prevent overcapacity from occurring in Prison by providing rehabilitation as a form of using restorative justice.

DatAFLow : Toward a Data-Flow-Guided Fuzzer

Coverage-guided greybox fuzzers rely on control-flow coverage feedback to explore a target program and uncover bugs. Compared to control-flow coverage, data-flow coverage offers a more fine-grained approximation of program behavior. Data-flow coverage captures behaviors not visible as control flow and should intuitively discover more (or different) bugs. Despite this advantage, fuzzers guided by data-flow coverage have received relatively little attention, appearing mainly in combination with heavyweight program analyses (e.g., taint analysis, symbolic execution). Unfortunately, these more accurate analyses incur a high run-time penalty, impeding fuzzer throughput. Lightweight data-flow alternatives to control-flow fuzzing remain unexplored. We present datAFLow , a greybox fuzzer guided by lightweight data-flow profiling. We also establish a framework for reasoning about data-flow coverage, allowing the computational cost of exploration to be balanced with precision. Using this framework, we extensively evaluate datAFLow across different precisions, comparing it against state-of-the-art fuzzers guided by control flow, taint analysis, and data flow. Our results suggest that the ubiquity of control-flow-guided fuzzers is well-founded. The high run-time costs of data-flow-guided fuzzing (~10 × higher than control-flow-guided fuzzing) significantly reduces fuzzer iteration rates, adversely affecting bug discovery and coverage expansion. Despite this, datAFLow uncovered bugs that state-of-the-art control-flow-guided fuzzers (notably, AFL++) failed to find. This was because data-flow coverage revealed states in the target not visible under control-flow coverage. Thus, we encourage the community to continue exploring lightweight data-flow profiling; specifically, to lower run-time costs and to combine this profiling with control-flow coverage to maximize bug-finding potential.

Using Local Cache Coherence for Disaggregated Memory Systems

Disaggregated memory provides many cost savings and resource provisioning benefits for current datacenters, but software systems enabling disaggregated memory access result in high performance penalties. These systems require intrusive code changes to port applications for disaggregated memory or employ slow virtual memory mechanisms to avoid code changes. Such mechanisms result in high overhead page faults to access remote data and high dirty data amplification when tracking changes to cached data at page-granularity. In this paper, we propose a fundamentally new approach for disaggregated memory systems, based on the observation that we can use local cache coherence to track applications' memory accesses transparently, without code changes, at cache-line granularity. This simple idea (1) eliminates page faults from the application critical path when accessing remote data, and (2) decouples the application memory access tracking from the virtual memory page size, enabling cache-line granularity dirty data tracking and eviction. Using this observation, we implemented a new software runtime for disaggregated memory that improves average memory access time and reduces dirty data amplification1.