Full Data Research Articles

Fast Yet Effective Machine Unlearning.

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This article raises the following questions: 1) can we unlearn a single or multiple class(es) of data from an ML model without looking at the full training data even once? and 2) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for a single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multiclass unlearning, does not put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step toward fast and easy implementation of unlearning in deep networks. Source code: https://github.com/vikram2000b/Fast-Machine-Unlearning.

Keyword-Based Remote Data Integrity Auditing Supporting Full Data Dynamics

Keyword-Based Remote Data Integrity Auditing Supporting Full Data Dynamics

Multi-view clustering via double spaces structure learning and adaptive multiple projection regression learning

Multi-view clustering aims to group objects with high similarity into one group according to the heterogeneous features of different views. The graph-based clustering methods have obtained excellent results. However, there remain a few common drawbacks. For example, some methods do not consider graphs' high-order structure information. Thus, fuller data information cannot be obtained. In addition, some methods remove noise, outliers, and redundant information in the graph learning phase, resulting in the loss of graph information. Furthermore, using predefined graphs cannot exploit complementary information between views. A triple strategy-based multi-view clustering method is presented to solve the above issues. First, Laplacian graphs are used for fusion learning, and the underlying first-order and second-order structure information among views are explored simultaneously. Then, a label fusion scheme is designed to eliminate noise, outliers, and redundant information and to mine the intrinsic characteristics of data labels. Besides, the consistent label matrix in adaptive regression learning is used to explore complementary information between views in a mutually guided learning way. Finally, the objective function is solved by using an efficient iterative method. Six types of experiments are conducted on eleven real-world multi-view datasets, and the conclusions that can be drawn are: (1) the proposed algorithm achieves the best results in terms of clustering accuracy on ten datasets with an average accuracy improvement of 5.11% compared to other algorithms. Specifically, the accuracy improved by 9.05% on dataset HW and 10.95% on dataset Reuters compared to the second results; (2) The ablation experiments confirm that the different learning strategies included in the proposed algorithm allow it to achieve better clustering performance.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

A Ranking-Based Cross-Entropy Loss for Early Classification of Time Series.

Early classification tasks aim to classify time series before observing full data. It is critical in time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU). Early diagnosis can provide more opportunities for doctors to rescue lives. However, there are two conflicting goals in the early classification task-accuracy and earliness. Most existing methods try to find a balance between them by weighing one goal against the other. But we argue that a powerful early classifier should always make highly accurate predictions at any moment. The main obstacle is that the key features suitable for classification are not obvious in the early stage, resulting in the excessive overlap of time series distributions in different time stages. The indistinguishable distributions make it difficult for classifiers to recognize. To solve this problem, this article proposes a novel ranking-based cross-entropy () loss to jointly learn the feature of classes and the order of earliness from time series data. In this way, can help classifier to generate probability distributions of time series in different stages with more distinguishable boundary. Thus, the classification accuracy at each time step is finally improved. Besides, for the applicability of the method, we also accelerate the training process by focusing the learning process on high-ranking samples. Experiments on three real-world datasets show that our method can perform classification more accurately than all baselines at all moments.

Bietti’s crystalline dystrophy: genotyping and deep qualitative and quantitative phenotyping in preparation for clinical trials

PurposeTo qualitatively and quantitatively characterise the genotypes and phenotypes of Bietti’s crystalline dystrophy (BCD) in a cohort of patients.DesignCross-sectional and observational study.MethodsClinically confirmed BCD patients were recruited for genotyping and...

Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar.

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included.

Enabling Parity Authenticator-Based Public Auditing With Protection of a Valid User Revocation in Cloud

The significance of the cloud enables the data owners (DOs) to store data remotely in cloud server (CS). The external and internal attacks on the stored data at CS can deliberately remove data. Furthermore, the CS removes the stored data to make empty location for the user's upcoming new data. However, it is a legal expectation of DOs to know whether their data are correctly stored or altered in CS. In this article, we propose a novel privacy-aware and hash-parity-bits-based public auditing (PA-HPPA) framework to secure full data, left half of the data, and the right half of the data, generated by a DO. DO generates two private key pairs with the assistance of a virtual key and a user ID (IP). The virtual key is the sequence number of DO who is registered and provided by the trusted data manager (TDM) while IP is the sequence number of DO working in an organization. Subsequently, DO blinds the categorized data and generates their signatures and hashes. In addition, DO generates the parity bits using xor and assigns to each hard drive (HD) in CS, which assistants to TDM in public auditing. Second, how to identify the error in the stored data and how to securely recover the error/missed data? Extension to the framework, the novel proposed data error identification and secure data recovery produce tags for installed HDs of CS using truth table and recover the altered/missed data via a authenticator, which is produced using xor function. Third, how to protect a valid user from revocation and, in case a user has revoked on merit basis, then how to securely access the stored data of it? This novel work has proposed three conditions to meet the validity of the valid user from revocation and securely generating the public-private key pairs to access the stored data of the revoked user securely from CS. Fourth, there is an efficient novel proposed dynamic operation scheme to insert, update, or delete the stored data at CS without regenerating the signatures, hashes, and tags for the whole stored data in cloud. The security analysis and the performance evaluation of the proposed solutions are provably efficient and secure with reduced communication costs.

Nonspecific Effects of the Bacillus Calmette-Guérin Vaccine in Portuguese Children Under 5 Years of Age: Protocol for a Population-Based Historical Birth Cohort Study.

The Bacillus Calmette-Guérin vaccine (BCG) against tuberculosis (TB) shows beneficial nonspecific effects, which are likely related to innate immune training. Until 2016, a single BCG dose was administered to all newborns in Portugal. In July 2016, a clinical guideline established that only children under 6 years belonging to high-risk groups should receive BCG. This might have prevented nonvaccinated children from developing trained immunological responses as effectively as BCG-vaccinated children. This study aims to investigate if there is variation in TB-related and all-cause mortality, and severe, moderate, or mild morbidity in children under 5 years of age, and whether such variation might be explained by the BCG vaccination policy change in 2016. This population-based historical birth cohort study includes children under 5 years of age born in Portugal between July 1, 2010, and June 30, 2021. Newborns with low birth weight, premature status, or known or suspected HIV infection are excluded. The follow-up period is until the completion of 5 years of age or the end of follow-up (June 30, 2021). The study will use secondary data from the National Health Service user registry, death certificate database, vaccination registry, communicable diseases surveillance system, TB surveillance system, diagnosis-related group information system for hospital admissions and emergency department visits, and primary health care information system. The data will be linked. Primary outcomes include person-time incidence rates of death (all causes and TB), TB diagnosis, and all causes and some specific causes of severe, moderate, or mild morbidity, and the incidence rate ratio of nonvaccinated to BCG-vaccinated children. We will compare the probability of surviving the first and fifth years of life or of not having severe, moderate, or mild morbidity during the follow-up period according to exposure (BCG vaccinated or nonvaccinated, number of doses, and time from birth until the first dose), using the log-rank test for assessing differences in survival rates between exposed and nonexposed children and hazard ratios for quantifying the differences. Moreover, we will perform a proportional hazards regression analysis. Ethics approval has been obtained. In March 2022, database owners were contacted to present the project and discuss the request for data. A unique identifier will be used. In July 2023, a process of redefinition of the variables per database was initiated. Data were received in October and November 2023. In November 2023, further work was conducted. By April 2024, we expect to start analyzing the full data set. The results will contribute to the accumulating body of knowledge and might have relevance to guide global BCG vaccination policy. Data linkage can contribute to a swifter mechanism to use available health data to conduct population-based studies and inform policy decision-making. ClinicalTrials.gov NCT05471167; https://clinicaltrials.gov/study/NCT05471167. DERR1-10.2196/55332.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption () and reduced scattering () coefficients at various depths. To achieve the stratified reconstruction of OPs and the separation between and , using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (, continuous-wave component, , mean time of flight). We compared their performance using a series of simulation and phantom validations. Compared to the LUT approach, utilizing full TR, and datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of and at different depths within 5mm.

Online learning for streaming data classification in nonstationary environments

AbstractIn this article, we implement the classification of nonstationary streaming data. Due to the inability to obtain full data in the context of streaming data, we adopt a strategy based on clustering structure for data classification. Specifically, this strategy involves dynamically maintaining clustering structures to update the model, thereby updating the objective function for classification. Simultaneously, incoming samples are monitored in real‐time to identify the emergence of new classes or the presence of outliers. Moreover, this strategy can also deal with the concept drift problem, where the distribution of data changes with the inflow of data. Regarding the handling of novel instances, we introduce a buffer analysis mechanism to delay their processing, which in turn improves the prediction performance of the model. In the process of model updating, we also introduce a novel renewable strategy for the covariance matrix. Numerical simulations and experiments on datasets show that our method has significant advantages.

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.

Vascular Endothelial Growth Factor Inhibitors and the Risk of Aortic Aneurysm and Aortic Dissection

Vascular endothelial growth factor pathway inhibitors (VPIs) pose a concern for aortic aneurysm (AA) and aortic dissection (AD), signaling potential vascular disease development. To investigate VPI-associated AA and AD. This case-control study with a nested design used full population data from a national claims database in Taiwan between 2011 and 2019. Eligible participants were aged 20 years or older with kidney, hepatic, gastrointestinal, or pancreatic cancer diagnosed between January 1, 2012, and December 31, 2019. The first cancer diagnosis date was defined as the cohort entry date. Cases were patients who received a diagnosis of AA or AD in hospitalizations or emergency visits between the cohort entry date and December 31, 2019. Controls were matched by ratio (up to 1:5) based on age, sex, cancer type, cohort entry date, and the index date (ie, the first AA or AD event date). Data analysis was performed between January 2022 and December 2023. Use of the oral VPIs sorafenib, sunitinib, and pazopanib between cohort entry date and index date. In the primary analysis, AA and AD were evaluated compositely, while in the secondary analyses, they were evaluated separately. Adjusted odds ratios (aORs) were calculated using conditional logistic regression to assess the association with VPI use (sorafenib, sunitinib, and pazopanib) considering various VPI exposure windows and cumulative use. A total of 1461 cases were included (mean [SD] age, 73.0 [12.3] years; 1118 male patients [76.5%]), matched to 7198 controls. AA or AD risk increased with a VPI exposure of 100 days or less before the index date (aOR, 2.10; 95% CI, 1.40-3.15), mainly from VPI-associated AD (aOR, 3.09; 95% CI, 1.77-5.39). Longer VPI duration (68 days or more: aOR, 2.64; 95% CI, 1.66-4.19) and higher cumulative dose (61 or more defined daily doses: aOR, 2.65; 95% CI, 1.66-4.23) increased the risk. The use of the 3 study VPIs (sorafenib, sunitinib, and pazopanib) was associated with an increased risk of AA and AD in patients with cancer, essentially all of the risk from VPI-associated AD. Future studies are needed to determine the risk factors of VPI-associated AA and AD, as well as to establish a class effect.

High‐Temperature Deformation of Enstatite‐Olivine Aggregates

AbstractSynthesized polycrystalline samples composed of enstatite and olivine with different volumetric ratios were deformed in compression under anhydrous conditions in a Paterson gas‐medium apparatus at 1150–1300°C, an oxygen fugacity buffered at Ni/NiO, and confining pressures of 300 or 450 MPa (protoenstatite or orthoenstatite fields). Mechanical data suggest a transition from diffusion to dislocation creep with increasing differential stress for all compositions. Microstructural analyses by optical and scanning electron microscopy reveal well‐mixed aggregates and homogeneous deformation. Crystallographic preferred orientations measured by electron backscatter diffraction are consistent with activation of the slip systems (010)[100] and (010)[001] for olivine and (100)[001] and (010)[001] for enstatite, as expected at these conditions. Nonlinear least‐squares fitting to the full data set from each experiment allowed the determination of dislocation creep flow laws for the different mixtures. The stress exponent is 3.5 for all compositions, and the apparent activation energies increase slightly as a function of enstatite volume fraction. Within the limits of experimental uncertainties, all two‐phase aggregates have strengths that lie between the uniform strain rate (Taylor) and the uniform stress (Sachs) bounds calculated using the dislocation creep flow laws for olivine and enstatite. Calculation of the Taylor and Sachs bounds at strain rate and temperature conditions expected in nature (but not extrapolating in pressure) indicates that using the dislocation creep flow law for monomineralic olivine aggregates provides a good estimate of the viscosity of olivine‐orthopyroxene rocks deforming by dislocation creep in the deeper lithosphere and asthenosphere.

Independence-Encouraging Subsampling for Nonparametric Additive Models

The additive model is a popular nonparametric regression method due to its ability to retain modeling flexibility while avoiding the curse of dimensionality. The backfitting algorithm is an intuitive and widely used numerical approach for fitting additive models. However, its application to large datasets may incur a high computational cost and is thus infeasible in practice. To address this problem, we propose a novel approach called independence-encouraging subsampling (IES) to select a subsample from big data for training additive models. Inspired by the minimax optimality of an orthogonal array (OA) due to its pairwise independent predictors and uniform coverage for the range of each predictor, the IES approach selects a subsample that approximates an OA to achieve the minimax optimality. Our asymptotic analyses demonstrate that an IES subsample converges to an OA and that the backfitting algorithm over the subsample converges to a unique solution even if the predictors are highly dependent in the full data. The proposed IES method is shown to be numerically appealing via simulations and a real data application. Theoretical proofs, R codes, and supplementary numerical results are accessible online as supplementary materials.

Inequalities in healthcare use during the COVID-19 pandemic

The COVID-19 pandemic led to reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally. This study investigates whether reductions in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich full population registry data, we predict expected healthcare use based on pre-pandemic trends (2017 – Feb 2020) and compare these expectations with observed healthcare use in 2020 and 2021. Our findings reveal a 10% decline in the number of weekly treated patients in 2020 and a 3% decline in 2021 relative to prior years. These declines are unequally distributed and are more pronounced for individuals below the poverty line, females, older people, and individuals with a migrant background, particularly during the initial wave of COVID-19 hospitalisations and for middle and low urgency procedures. While reductions in non-COVID related healthcare decreased following the initial shock of the pandemic, inequalities persist throughout 2020 and 2021. Our results demonstrate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

Risk factors for short-term all-cause mortality in patients with end stage renal disease: a scoping review

ObjectivesThere is a lack of prognostic information to guide the prediction of short-term all-cause mortality in patients with end-stage renal disease (ESRD). The aim was to review the risk factors that influenced the risk of short-term all-cause mortality in patients with ESRD.MethodsMEDLINE, Embase, PubMed, CINAHL, the Cochrane Library and Web of Science databases were searched for articles published between 2000 and 2020. Articles describing risk factors predicting short-term mortality (≤ 3 years) in patients with ESRD were included. Four reviewers independently performed title, abstract, full text screening and data extraction. Assessment of risk of bias was assessed using the Quality In Prognosis Studies (QUIPS) tool checklist.Results20,840 articles were identified and 113 papers were included for this review. Of the 113 papers, 6.2% included only peritoneal dialysis (PD) patients, 67.3% included only hemodialysis (HD) patients, 20.4% included both PD and HD patients, with the remaining papers featuring patients on conservative management or awaiting renal transplant. Risk factors were categorised into 13 domains: 1)demographics/ lifestyle, 2) comorbidities 3)intradialytic blood pressure, 4)biomarkers, 5)cardiovascular measurements, 6)frailty status, 7)medications, 8)treatment related indicators, 9)renal related parameters, 10)health status, 11)cause of ESRD, 12)access to healthcare care/ information and, 13)proxy measures for poor health. C-reactive protein(CRP), age, and functional status were observed to have higher percentage of instances of being significantly associated with all-cause mortality.ConclusionCommonly examined risk factors observed from this review may be used to build a general prognostic model for patients with ESRD, with specific treatment related risk factors added on to enhance the accuracy of the models.

Supply Chain Visibility and Performance of Food and Beverage Manufacturing Firms in Kenya

Purpose: The purpose of the study was to evaluate the effect of supply chain visibility on the performance of food and beverage manufacturing firms in Kenya and to find out the moderating effect of supply chain technology on the performance of food and beverage manufacturing firms in Kenya.
 Methodology: The exploratory research design was used in the study which utilized both qualitative and quantitative data. All 270 food and beverage manufacturing firms in Kenya registered by Kenya Association of Manufacturers (2022) were considered using the census approach. Target population for the research was one participant from logistics, supply chain or operations department at each of the registered food and beverage manufacturing firms. Primary and secondary data was used, the primary data was collected using semi structured questionnaire that was administered by the researcher and research assistants. Samples of the questionnaire were pilot tested to test the reliability and validity before full scale data collection. The data was analyzed using the Statistical Package for Social Sciences (SPSS) version 26 software. Quantitative data was analyzed using descriptive statistics while the inferential analysis was further carried out using structural equation modelling, ANOVA and regression coefficients to give effect of the explanatory variable.
 Findings: The study found out that supply chain visibility significantly influenced the performance of food and beverage manufacturing firms in Kenya at both when there was a moderator and without a moderating variable, supply chain technology. In the first model without moderator, it recorded a standardized estimate of 0.538 (p<0.000), indicating that as supply chain visibility increases, performance of food and beverage manufacturing firms also increases. Fit indices on structural equation modelling revealed a marginal fit with a chi-square test of 211.322 with 86 degrees of freedom (P-value =0.0492). The structural path for structural equation modelling from supply chain visibility to supply chain performance remains positive and significant. Standardized estimate of 1.347 and p-value was 0.001<0.05. Which indicates that the variability of supply chain visibility on the performance of food and beverage manufacturing firms could be explained by 53.8% when no moderator is included and increased to 134.7% when ,moderator, supply chain technology is incorporated thereby indicating a stronger relationship. The other fit indices that gave a satisfactory model fit when the moderator supply chain technology was used are RMR=.9196, GFI=.9263, NFI= .9473, RMSEA=.0184 and CFI=.9369 which implies that the model was fit to determine the relationship between supply chain visibility and performance of food and beverage manufacturing firms in Kenya and therein make conclusions and recommendations. ANOVA, regression coefficient and model summary (R2) were also used and indicated significance for there use with all recording p-value of 0.000<0.05.
 Unique Contribution to Theory, Practice and Policy: System theory of management was adopted and validated. By incorporating supply chain enabling technologies in the management and planning of production in a manufacturing firm, it has the potential of revolutionizing the business environment hence increasing flow of raw materials and other items into the company’s production plant, warehouses or retail locations in an effort to create visibility and enhanced performance. The theory can assist Kenya and the manufacturing firms in developing policies and strategies to assist manufacturing sector while creating competitive advantages through supply chain visibility. The study found out that some of the important supply chain mapping strategies to enhance performance of food and beverage manufacturing firms is supply chain visibility and supply chain technology.

Lanthanide Amide Complexes Supported by the Bis‐tris(pyrazolyl)borate Ligand Environment

AbstractSynthesis of primary lanthanide amides in the bis‐hydrotris(1‐pyrazolyl)borate ligand environment has been achieved. Salt metathesis of [Dy(Tp)2(OTf)] 1‐Dy (OTf=CF3SO3) with K(N′′) (N′′=N(SiMe3)2) in toluene yielded the [bis(silyl)]amide [Dy(Tp)2(N′′)] 2‐Dy. Complexes 1‐Ln and 2‐Ln were both used to access primary lanthanide amides, where either metathesis of 1‐Ln with K(NHArCF3) (ArCF3=C6H3(CF3)2‐3,5) or protonolysis of 2‐Ln with H2NArCF3 in toluene yielded [Ln(Tp)2(NHArCF3)] 3‐Ln (Ln=Y, Dy). The synthesis of parent amides was also attempted, but the metathesis of 1‐Y with NaNH2 yielded complicated reaction mixtures, but from which the dimeric parent amide [{Y(Tp)2(μ‐NH2)}2] 4‐Y and an ‘ate’‐salt [{Y(Tp)2(μ2‐OTf)(μ3‐OTf)Na(THF)2}2] 5‐Y were isolated. Full characterisation data are presented for all complexes, including the structure determination.

Supplier Collaboration and Performance of Food and Beverage Manufacturing Firms in Kenya

Purpose: This study sought to evaluate explored the influence of supplier collaboration on the performance of food and beverage manufacturing firms in Kenya and to find out the moderating effect of supply chain technology on the performance of food and beverage manufacturing firms in Kenya.
 Methodology: The study used exploratory research design and utilized both qualitative and quantitative data in carrying out the study. This study adopted a census survey sampling which was conducted on 270 food and beverage manufacturing firms in Kenya registered by Kenya Association of Manufacturers (KAM, 2022). The target population for the research was all 270 respondents each from the food and beverage manufacturing firms. Both primary and secondary data was used, the primary data was collected using semi structured questionnaire that was administered by the researcher and research assistants. Samples of the questionnaire were pilot tested to test the reliability and validity before full scale data collection. The data was analyzed using the Statistical Package for Social Sciences (SPSS) version 26 software. Quantitative data was analyzed using descriptive statistics and presented in tables and figures. The inferential analysis was further carried out using structural equation modelling, ANOVA and regression coefficients. The results were then presented using tables, figures, graphs and charts.
 Findings: Supplier collaboration significantly influenced performance of food and beverage manufacturing firms in Kenya at both without a moderator and also using the moderating variable, supply chain technology. In the first model without moderator, it recorded a standardized estimate of 0.637 (p<0.001), indicating that as supplier collaboration increases performance of food and beverage manufacturing firms also increases. Fit indices on structural equation modelling revealed a marginal fit with a chi-square test of 216.155 with 86 degrees (P-value 0.0561). The structural path for structural equation modelling from supplier collaboration to supply chain performance remains positive and significant standardized estimate of 0.855 and p-value was 0.001<0.05. Which indicates that the variability of supplier collaboration on the performance of food and beverage manufacturing firms could be explained by 63.7% when no moderator is included and increase to 85.5% when supply chain technology is incorporated thereby indicating a stronger relationship. The other fit indices that gave a satisfactory model fit are RMR=.9019, GFI= .9774, NFI= .9164, RMSEA=.0191 and CFI=.9176 this implies that the model was fit to determine the relationship between supplier collaboration and performance of food and beverage manufacturing firms in Kenya and therein make conclusions and recommendations. ANOVA, regression coefficient ant model summary (R2) were also used and indicated significance of there use all recording p-value of 0.000<0.05. 
 Unique Contribution to Theory, Practice and Policy: While transaction cost theory used in this study was validated by offering cost reduction strategies like outsourcing, accurate order forecasting as increasing the organization bottom line. The study recommends that when creating a supplier collaboration portfolio, companies should pool suppliers with the same activities in one pool but to use technology to mop up suppliers with high asset specificity for components delivering competitive advantage. Meanwhile, suppliers with low asset specificity for suppliers with components which result to less competitive advantage needs to be managed as a separate line of engagement.