Local Strategies Research Articles

Refugees’ employment: Adapting a structural inequality framework for multinational corporations

Multinational corporations (MNCs), through action or inaction, directly and indirectly shape vertical, horizontal, or internal structural inequalities in labour markets. The refugee employment literature has given scant attention to the role of MNCs, and how these corporations can or do contribute to achieving equality in employment outcomes for diverse social groups such as refugees. In this paper, we explore how MNCs seek to address challenges associated with structural inequalities to achieve greater equality in employment outcomes for refugees. To do so, we draw on the refugee employment literature, industry evidence, and business reports. Our findings reveal that MNCs require global and national-level responses to address structural inequalities associated with vertical, horizontal, or internal inequalities. We synthesise these inequalities into a conceptual framework and illustrate the need for MNCs to balance standardisation vs localisation strategies in human resource and diversity management, sourcing and supply chain management, corporate social responsibility, marketing and branding, and external engagement. Such MNC strategies contribute to refugees’ ability to navigate structural factors that create employment inequalities.

Cancers bronchiques non à petites cellules de stade III : quel abord local optimal ?

The stage III grouping, in the international TNM classification, represents a heterogeneous group of diseases for which treatment strategy is multimodal generally including, whenever possible a local treatment, surgery or radiotherapy. Numerous trials comparing both local strategies have been published, not allowing a formal cut decision on the benefit – or absence of benefit – of including a surgical approach in the multimodal therapy. A major problem when integrating data issued from these randomized trials comes from the absence of consensual definition on what “resectable stage III disease” is, left at best at the discretion of the thoracic surgeon and the multidisciplinary team in charge of the patient.The aim of this article is to set up data relative to resectability status, integrating the EORTC Lung Cancer Group proposal and in the light of these results, to present a critical review of local treatments in stage III non-small cell lung cancer. 1877-1203/© 2024 SPLF. Published by Elsevier Masson SAS. All rights reserved.

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Bilingual phrase induction with local hard negative sampling

AbstractBilingual lexicon induction focuses on learning word translation pairs, also known as bitexts, from monolingual corpora by establishing a mapping between the source and target embedding spaces. Despite recent advancements, bilingual lexicon induction is limited to inducing bitexts consisting of individual words, lacking the ability to handle semantics‐rich phrases. To bridge this gap and support downstream cross‐lingual tasks, it is practical to develop a method for bilingual phrase induction that extracts bilingual phrase pairs from monolingual corpora without relying on cross‐lingual knowledge. In this paper, the authors propose a novel phrase embedding training method based on the skip‐gram structure. Specifically, a local hard negative sampling strategy that utilises negative samples of central tokens in sliding windows to enhance phrase embedding learning is introduced. The proposed method achieves competitive or superior performance compared to baseline approaches, with exceptional results recorded for distant languages. Additionally, we develop a phrase representation learning method that leverages multilingual pre‐trained language models. These mPLMs‐based representations can be combined with the above‐mentioned static phrase embeddings to further improve the accuracy of the bilingual phrase induction task. We manually construct a dataset of bilingual phrase pairs and integrate it with MUSE to facilitate the bilingual phrase induction task.

3D Imaging Reconstruction and Laparoscopic Robotic Surgery Approach to Disseminated Peritoneal Leiomyomatosis

ObjectivesThis video article explores the synergistic approach of 3D imaging reconstruction and laparoscopic robotic surgery for the management of a complex case of disseminated peritoneal leiomyomatosis (1). The primary focus lies in the capability of the reconstruction model to provide diagnostic support to identify fibroids during surgical procedures, potentially enhancing surgical precision, reducing operating times, minimizing uterine incisions, and limiting blood loss. 3D imaging reconstruction techniques were used to facilitate the identification of multiple parasitic and non-serosal myomas, which is particularly challenging when operating with a robotic surgical platform that lacks haptic feedback. SettingTertiary referral center. ParticipantsA case report design was employed, focusing on a 43-year-old nulliparous infertile woman with multiple symptomatic uterine myomas. Our institution has made a further diagnosis of disseminated peritoneal leiomyomatosis(4-5). InterventionsDue to the widespread nature of peritoneal leiomyomatosis and numerous uterine fibroids, robotic surgery was considered a preferable option based on our experience to operate within confined anatomical spaces. 3D imaging reconstruction technology was utilized for preoperative and intraoperative planning, enabling precise determination of the fibroids' location, size, and volume obtained through MRI imaging. Real-time 3D imaging guided rapid myoma localization and surgical strategy adjustment (2-3). The procedure resulted in the removal of 15 fibroids, with minimal blood loss (250 mL) and a total operative time of 120 minutes. Multilayer running hysterorraphy was performed using a barbed monofilament suture to ensure effective hemostasis, incorporating serosal introflection to reduce the risk of post-operative adhesion development. ConclusionsThe combined approach of 3D imaging reconstruction and laparoscopic robotic surgery holds significant potential for the management of disseminated peritoneal leiomyomatosis. This approach can overcome some robotic surgery limitations, particularly the absence of haptic feedback, providing accurate preoperative planning and real-time intraoperative guidance, facilitating efficient fibroid localization, minimizing uterine incisions, and reducing blood loss. Further research is needed to fully evaluate the clinical impact of this promising technology.

Adaptive compressed learning boosts both efficiency and utility of differentially private federated learning

In the federated learning (FL) research field, current research is confronted with several pivotal challenges, e.g., data privacy, model utility and communication efficiency. Furthermore, these challenges are further amplified by statistical data heterogeneous in the FL system. Thus, a novel Communication-efficient and Utility-assured Gaussian differential privacy-based Personalized Federated Adaptive Compressed Learning method, called CUG-PFACL, is proposed. Specifically, an end-to-end local adaptive compressed learning strategy is designed, including three crucial modules, namely the measurement matrix, the personalized compressed data transformation and the local model. Especially, jointly training the measurement matrix module and the personalized compressed data transformation module can mitigate the inherent statistical heterogeneity while preserving all important characteristics of the compressed private data of each local client, and alleviate the additional heterogeneity induced by Gaussian differential privacy in each global communication round. Numerous experimental simulation and comparisons demonstrate that CUG-PFACL has three notable advantages: data privacy guarantee, enhanced personalized model utility and high-efficient communication.

Posterior Hip Pericapsular Block (PHPB) with pericapsular nerve group (PENG) block for hip fracture: a case series

BackgroundPain after total hip arthroplasty (THA) for femoral neck fracture (FNF) can be severe, potentially leading to serious complications. PENG block has become an optional local analgesic strategy in hip fracture surgery, but it cannot provide effective pain relief for the posterior capsule of the hip joint. Therefore, we modified the traditional sacral plexus nerve block and named it Posterior Hip Pericapsule Block (PHPB) to complement the blockade of the relevant nerves innervating the posterior hip capsule region. Thereby, we detail the analgesic effect of PHPB combined with PENG block on five hip fracture patients and the effect on their hip motor function.MethodsThis case series was conducted from December 2023 to February 2024. We performed ultrasound-guided PHPB combined with PENG block on five patients with hip fractures. Numerical Rating Scale (NRS) pain scores at rest and maximum NRS pain scores during limb movement of the five patients were recorded within 48 h after surgery. Their hip flexion, abduction, adduction, keen flexion and quadriceps muscle strength were also recorded. Serious postoperative complications, including wound infection, hematoma formation, or nerve injury, were recorded.ResultsThey experienced effective pain control within 48 h postoperatively, with NRS pain scores at rest decreasing from 3.0 (3.0, 4.5) to 0.0 (0.0, 1.0) and maximum NRS pain scores during limb movement from 8.0 (7.5, 8.5) to 1.0 (0.5, 2.0). They can autonomously perform hip flexion, abduction, adduction, and knee flexion within 48 h postoperatively without any signs of movement disorders or quadriceps muscle weakness. No severe postoperative complications, such as wound infections, hematoma formation or nerve damage, were observed in any of the patients.ConclusionsUltrasound-guided PENG block combined with PHPB provided effective analgesia for hip fracture patients in the perioperative period. It maintained hip joint motor function and quadriceps muscle strength within 24 h after THA.

Control parameter optimisation using the evidence framework for the ant colony optimisation algorithm

The ant colony optimization (ACO) algorithm is a metaheuristic initially designed to solve the travelling salesman problem (TSP). The design of experiments, finding the suitable ACO algorithm configuration, and calibrating the adaptive control parameters are exhaustive and time-consuming exercises, especially for TSPs where the number of cities can exceed 1000. This paper presents an evidence framework driven control parameter optimisation (EFCPO) algorithm for an ACO algorithm solving TSPs. EFCPO performs auto-tuning of the adaptive control parameters and makes recommendations about the ACO algorithms that are best suited for the TSPs in question using the log evidence. In addition, with this ability, the algorithm can take a solution provided by an ACO algorithm and improve the results. The EFCPO accomplishes this over a number of cycles through auto-tuning of the control parameters and re-running the ACO until the process is completed. The capabilities of EFCPO are compared to another configuration tool, irace, using benchmark ACO algorithms to test the efficiency of the framework. The benchmark algorithms make use of a local search strategy to solve TSPs. The results show that ACO algorithms are able to find new and improved solution tours within reasonable times. The improvements are also significant. In addition, ACO algorithms that are best suited for the TSP in question are preferred, making the EFCPO an effective tool for real-time configuration of ACO algorithms for solving TSPs .

Using Pregnancy and Parenting Apps and Social Media During COVID-19: Absence and Sociality, Agency and Cultural Negotiations for South Asian–Origin Women in Australia

This article reports on and analyses data from a situated and in-depth project on the experiences of six cisgender South Asian-Australian women/people who gave birth during the COVID-19 pandemic. Prior to the pandemic, negatively racialized women experienced barriers to health care and a lack of social support, which were further exacerbated during the COVID-19 pandemic. International border closures in Australia combined with local mitigation strategies inhibited social and cultural support from families, impacting many migrant mothers/birthing people who gave birth for the first time in Australia. Many hospitals in the states of New South Wales and Victoria instituted restrictions to birthing services as a way of reducing exposure to the coronavirus during the pandemic. Our research suggests that pre-existing limitations of health care providers, digital platforms, and apps with regard to culturally and linguistically diverse (CALD) women in Australia have been amplified during the pandemic. Online Facebook groups from the mothers’ countries of origin or cultural backgrounds, or for mothers who had babies due in the same month, represented a significant source of information and support for the participants. This was particularly important at a time when women’s capacities to engage in traditional cultural practices, which provide practical, emotional, and informational support, were compromised by the inability to garner familial support. We situate these findings in the literature on “performing good motherhood” in neoliberal times and via reliance on digital devices and platforms and what it means for CALD women’s sociality, sense of agency, and negotiations with cultural practices.

A new lattice approach for risk-minimization hedging under generalized autoregressive conditional heteroskedasticity models

This paper explores the calculation of risk-minimization hedging strategies, specifically local and global risk minimization strategies for contingent claims under affine and non-affine GARCH models with the known closed forms of its first four moments across times under the physical measure. A unified and efficient willow tree method is introduced for various GARCH models. Unlike methods that provide option values and hedging ratios solely at the inception time, the proposed willow tree method generates a comprehensive table of option values and hedging ratios at each discrete time step across possible asset prices. Additionally, the method showcases robust performance in hedging at lower frequencies than the underlying asset’s modeling frequency (e.g., weekly or monthly hedging using a daily GARCH model). Lastly, the willow tree method outperforms the Monte Carlo method, offering greater efficiency, accuracy, and flexibility in solving risk-minimization hedging problems.

A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms.

Minimally Invasive Injectable Gel for Local Immunotherapy of Liver and Gastric Cancer.

Local immunotherapy represents a promising solution for preventing tumor recurrence and metastasis post tumor surgical resection by eliminating residue tumor cells as well as eliciting tumor-specific immune responses. Minimally invasive surgery has become a mainstream surgical method worldwide due to its advantages of aesthetics and rapid postoperative recovery. Unfortunately, the currently reported local immunotherapy strategies are mostly designed to be used after open laparotomy, which go against the current surgical philosophy of minimally invasive therapy and is not suitable for clinical translation. Aiming at this problem, a minimally invasive injectable gel (MIGel) is herein reported loaded with immunotherapeutic agents for gastric and liver cancer postoperative treatment. The MIGel is formed by crosslinking between oxidized dextran (ODEX) and 4-arm polyethylene glycol hydroxylamine (4-arm PEG-ONH2) through oxime bonds, which can be injected through a clinic-used minimally invasive drainage tube and adhered tightly to the tissue. The loaded oxaliplatin (OxP) and resiquimod (R848) can be released constantly over two weeks and resulted in over 75% cure rate in orthotopic mouse gastric and liver cancer model. Collectively, a concept of minimally invasive local immunotherapy is proposed and MIGel is designed for local intraperitoneal cancer immunotherapy through minimally invasive surgery, with good clinical translation potential.

Global and local semantic enhancement of samples for cross-modal hashing

Hashing becomes popular in cross-modal retrieval due to its exceptional performance in both search and storage. However, existing cross-modal hashing (CMH) methods may (a) neglect to learn sufficient modal-specific information, and (b) fail to fully exploit sample semantics. To address these issues, we propose a method called Semantic Enhancement of Sample Hashing (SESH). First, SESH employs a global modal-specific learning strategy to draw overall shared information and global modal-specific information by factoring the mapping matrix. Second, SESH introduces manifold learning and a local modal-specific learning strategy to extract additional local modal-specific and modal-shared data under label guidance. Meanwhile, local modal-specific information is integrated with global modal-specific details to add rich modal-specific information. Third, SESH uses discrete maximum similarity and orthogonal constraint transformation to enhance both global and local semantic information, embedding more discriminative information into the Hamming space. Finally, an efficient discrete optimization algorithm is proposed to generate the hash codes directly. Experiments on three datasets demonstrate the superior performance of SESH. The source code will be available at https://github.com/kokorording/SESH.

Nature-inspired design principles promote supply network resilience

The dynamic, interconnected nature of modern supply chains makes it important to understand how firm-level decision-making will impact the robustness of supply chains to disruption. The behavior of naturally evolved distribution systems offers a useful starting point to identify potential design features that can promote robustness without compromising the viability of individual firms. Drawing inspiration from how ant food-sharing networks respond to supply shortages, we developed an agent-based model of a generalized supply network and evaluated how different local strategies influenced the ability of firms to acquire sufficient resources to meet their demand. Our simulations reveal that differences among firms in strategic behavior can reduce variation in outcomes across firms while maintaining mean performance, thereby buffering system-level robustness. In addition, the ability to expand one’s supplier network bolstered performance when firms experienced difficulty in meeting their demand. Conversely, under the assumptions of our model, overly relying on distributors to gain access to additional suppliers or to gain competitive advantages was ineffective in helping firms to meet their consumptive demand. Our nature-inspired modeling framework provides a potentially useful approach for evaluating how different participant decision-making strategies may impact the robustness and resilience of global supply chains that are increasingly likely to face frequent and unpredictable disruptions.

Social and policy characteristics associated with injurious shootings by police in US counties: A multilevel analysis, 2015-2020.

From 2015 to 2020, shootings by police injured or killed 1769 people annually in the United States, disproportionately harming members of minoritized groups. Prior studies of the structural determinants of these inequities have largely examined state-level aggregations and fatal outcomes. This study aimed to: 1) describe state and county variation in fatal and nonfatal injurious shootings by police, and 2) analyze the relationship between state and county context and differences in county rates of injurious shootings by police. Injury data were developed from manual review of incidents listed in the Gun Violence Archive, then aggregated by county-year. Covariate selection was informed by theories of police use of force and the Social Basis of Disparities in Health conceptual framework. Fixed effects negative binomial regression models were estimated, nesting years within counties and states. Analyses controlled for county population, local reporting presence, and multiple measures of social conflict and community violence. From 2015 to 2020, 56% of counties experienced injurious shootings by police. Higher county rates of victimization were associated with greater state spending on police relative to health, county income inequality, prevalence of unmet substance use disorder needs, higher county firearm availability, and permitless concealed carry statutes. Firearm purchaser licensing polices were associated with lower incidence of injurious shootings by police. To prevent patterns of injurious shootings by police, policymakers should consider addressing undermanaged substance use disorder through crisis fund allocation and use, adopting stronger firearm licensing systems, and evaluating local strategies to combat inequities and strengthen non-policing responses to social needs.

Shame Buffers and Spatial Risk-Mitigation: Exploring the effects of social stigma on the locational strategies of illicit massage businesses and their customers

Traditional locational analysis in urban retail/business geography begins with the assumption that customers will visit retail locations that are most convenient, and that businesses that locate close to their customers’ residences or workplaces would be preferred to locations farther from those places. This assumption is problematic when applied to economic activities for which social stigma exists. In this article, we examine the influence of social stigma on the locational choices made by customers of illicit massage businesses in the Dallas-Fort Worth, Texas metropolitan area. We introduce the concept of shame buffers – zones around the places to which each customer is most deeply socially embedded, their home, workplace, and other community spaces, where the customer would not engage in stigmatized behaviors for fear of being observed and facing social shaming. We then examine the validity of the shame-buffer concept by analyzing customers' mobility patterns for both legal and illicit massage businesses in the Dallas-Fort Worth Metropolitan Area (DFW metro).

The Influence of Brand Image, Price Perception, and Local Wisdom-Based Promotional on Buyer's Decision

This study analyzes the influence of brand image, price perception, and local wisdom-based promotional strategies on the purchasing decisions for Tebo Riski Danang batik in Tebo Regency, Jambi Province. Tebo batik is unique, and Rumah Batik Riski Danang stands out by producing and marketing it, highlighting the region's history and distinctiveness. The research employed a quantitative method with questionnaires distributed to 255 respondents in Tebo Tengah District. The analysis included validity, reliability, correlation, and multiple linear regression. The findings revealed that brand image negatively impacts purchasing decisions, while price perception has a positive influence. However, the promotional strategy does not significantly affect purchasing decisions. Simultaneously, the three variables together significantly influence purchasing decisions. Brand image, price perception, and promotional strategy explain 41.4% of the variation in purchasing decisions, with other factors accounting for the remaining 58.6%. In conclusion, brand image and price perception are crucial in shaping purchasing decisions for Tebo Riski Danang batik, while the promotional strategy has no significant impact.

The Ecological Architecture Approach Analysis at Kusuma Agrotourism Resort & Convention Hotel

Resort areas are popular recreational destinations for urban populations, but rapid urban development often leads to significant environmental degradation in these areas. This degradation is evidenced by the decline in water, air, and soil quality, the extinction of local flora and fauna, and the destruction of natural ecosystems. As a response, the application of ecological architecture has become essential in designing and constructing city facilities, including resorts, to mitigate environmental damage. This study aims to examine the implementation of various principles of ecological architecture in resort areas and identify sustainable materials suitable for such applications. A qualitative descriptive method is employed, combining literature reviews and field observations to analyze the integration of ecological architectural principles in the resort area. The findings reveal that key principles such as the use of local materials, energy-efficient systems, and environmental management strategies have been applied. However, the study also highlights areas where improvements can be made to better preserve the ecosystem and minimize the negative impacts of architectural design.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Decoding Socio-Economic Demographic Trends: The Power of Spatial Econometrics and Geographic Analysis

Greece is experiencing a steady population decline caused by the declining migratory and natural balance. This research investigates the spatial impact of socio-economic and demographic factors on the natural population balance in Greece for the spatial zoning of municipal administrative units. Using geographically weighted regression (GWR) on data from the 2011 Greek census, the research explores the local impacts of factors like housing repair permits, vacant housing, employment rates, population inflows, distance from regional centers, aging, gender ratios, and education levels. An initial ordinary least squares (OLS) regression was conducted, revealing significant spatial variation and emphasizing the necessity of spatial econometric methods. The GWR model proved to be more effective in accounting for the variance in the data, removing spatial autocorrelation and revealing high local variation. Results show the high negative impact of the aging index in Western Greece and the Ionian islands, the counterintuitive positive effect of the gender ratio in urban areas, and the positive influence of population inflows in high-migration regions like Northern Greece and Crete. The results of this study underline the need to utilize spatial econometric methods for a precise and detailed understanding of demographic trends and provide valuable insights for localized strategies to address demographic challenges.